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This paper presents a comprehensive approach for designing polymer acceptors for organic photovoltaic applications through the generation of an extensive database and the application of machine learning (ML) techniques. Over 40 ML models are trained for the prediction of power conversion efficiency (PCE). Histgradient boosting regressor has appeared as best model. Almost 10 k polymers are generated and their PCE values are predicted. The chemical space of polymers has been visualized and analyzed. Cluster analysis revealed significant differences among the selected polymers. Additionally, an assessment of synthetic accessibility for these polymers indicated that the majority can be synthesized with relative ease. [ABSTRACT FROM AUTHOR]

The potential use of organic electronic materials in various optoelectronic devices has drawn considerable attention in recent years. For organic electronic devices to operate more effectively, the exciton binding energy must be reduced. Using high-throughput screening methods and machine learning analyses, a thorough framework for creating organic electronic materials with lower exciton binding energies has been described in the current research. Our approach combines computer simulations, databases of materials, and data-driven algorithms to find viable compounds for materials with low exciton binding energies. Python software has been used to analyze various ML models. Breaking retrosynthetically interesting chemical substructures methodology has been used to form new compounds. The characteristics of compounds has been represented through molecular descriptors. Random forest regressor model, gradient boosting regressor model, K Neighbors regressor model and extra tree regressor model have been used to analyze the performance parameters. Twenty organic semi-conductors have been selected with low Eb values. The synthetic accessibility score indicated easy synthesis of selected semi-conductors. Similarity analysis has indicated structural similarity between selected semi-conductors. Machine learning is helping the potential use of organic electronic materials in various optoelectronic devices, such as organic photovoltaics, organic light-emitting diodes, and organic field-effect transistors. [ABSTRACT FROM AUTHOR]

The shift towards sustainable energy systems has led to an exploration of hydrogen (H 2) as a sustainable and eco-friendly fuel substitute. This article explores significant progress in carbon-based catalysts (CBCs) for H 2 -rich syngas production from wastes via pyro-steam gasification, as well as the application of carbon-based adsorbents (CBAs) for carbon dioxide (CO 2) adsorption to enhance H 2 ratio in syngas. It also examines the efficacy of several catalysts supported on carbon materials (char and activated carbon). These catalysts include transition metals, noble metals, and alkali metals. Furthermore, the influence of operational parameters such as temperature, space velocity, steam-to-carbon ratio, feedstock composition, and catalyst particle size on H 2 production and CBC performance is examined. It also evaluates CBC production techniques, including precipitation, impregnation, physical mixing, and adsorption, emphasizing the advantages and disadvantages of these procedures in order to develop effective catalysts. Furthermore, the review examines CBAs, particularly char and activated carbon, for their CO 2 adsorption capacity and mechanisms, which include physisorption and chemisorption. A comparative analysis of activation methods for producing CBAs, such as physical and chemical activation, and their respective CO 2 adsorption efficiencies are also presented. The article concludes with valuable recommendations and insights on optimizing gasification and catalyst design for H 2 -rich syngas production and improving CO 2 adsorption to promote integrated solutions for producing H 2. [Display omitted] • Carbon-based catalysts (CBCs) for H 2 -rich syngas production are reviewed. • Char from the same pyrolysis process makes CBCs highly economical. • Char-supported multi-metals boost CBC's activity and reduce coke formation. • Carbon-based adsorbents (CBAs) lead to the economic production of H 2. [ABSTRACT FROM AUTHOR]

Alginate extracted from the marine brown algae is a massively utilized biopolymer in multiple fields such as microreactors for the fabrication of metal nanoparticles along with other polymeric and nonpolymeric materials to enhance their mechanical strength. These sodium alginate (Na-Alg)-based fabricated nanocomposites find applications in the field of catalysis and biological treatment as antibacterial/antifungal agent due to the synergistic properties of Na-Alg and fabricated metal nanoparticles (NPs). Na-Alg offers mechanical strength and nanoparticles provide high reactivity due to their small size. Sodium alginate exhibits hydroxyl and carboxylate functional groups that can easily interact with the metal nanoparticles to form composite particles. The research on the preparation of Na-Alg–based nanoparticles and nanoaggregates have been started recently but developed quickly due to their extensive applications in different fields. This review article encircles different methods of preparation of sodium alginate–based metal nanocomposites; analytical techniques reported to monitor the formation of these nanocomposites and used to characterize these nanocomposites as well as applications of these nanocomposites as catalyst, antibacterial, and antifungal agent. [ABSTRACT FROM AUTHOR]

Designing the materials with desirable properties is very difficult task. Experimental approaches are expensive and time consuming. Machine learning (ML) guided screening is better option. In present study, different machine learning models are tried for the prediction of crystallization tendency of polymers. Hist gradient booting model is the best one. A large database of polymers is mined and easily synthesizable polymers are selected. Their crystallization tendency is predicted using fast ML model. A selected portion is analyzed using t-distributed stochastic neighbor embedding (t-SNE) method. Change in crystallization tendency on structural change is studied using structure Activity Landscape Index (SALI) analysis. On the selected polymers, clustering analysis is also performed to explore the structurally closely associated polymers with higher crystallization tendency. The synthetic accessibility assessment is also done for selected polymers. Our proposed approach is very useful for virtual screening of efficient materials. [ABSTRACT FROM AUTHOR]

The dyes with light absorption ability in extended spectrum have huge potential in various photovoltaics applications. Absorption maxima (Lambda max) is predicted using machine learning (ML). Multiple machine learning models are used. Random Forest is best model among the tried ML models. A library of new dyes is created using python-based tool. Absorption maxima of newly generated dyes is predicted using best ML model (Random Forest). The generated library of dyes is visualized using Uniform Manifold Approximation and Projection (UMAP) plot. 30 dyes with absorption in extended spectrum are selected. Synthetic accessibility assessment is done to check ease of synthesis of selected dyes and to further narrow down the number of potential candidates. Structural behavior of selected dyes is studied using hierarchical cluster analysis. This study offers a theoretical framework for developing potential dyes that could exhibit light absorption in near-IR region of solar spectrum. [Display omitted] • Machine learning is used to predict absorption maxima of dyes for photovoltaics applications. • Random Forest model is best model for predicting absorption maxima. • Python-based tool is used to generate a library of near-IR absorbing dyes. • 30 dyes with extended spectrum absorption are selected. • Synthetic accessibility and structural analysis are also done. [ABSTRACT FROM AUTHOR]

Summary: This study sheds light upon how the decomposition of cabbage waste (CW) is brought to make it capable of providing bioenergy along with bio‐chemicals. The CW is evaporated through three steps, and at the same time, non‐condensable products (NH3, CO2, CH4, CO, SO2 and NO) and condensable products like (H2O, CH3CH2OH, CH3COOH, CC, C6H5OH and HCOOH) are yielded. The products that have gone through the pyrolysis process comprise 45% condensable products. Thermogravimetry‐Fourier transform infrared (TG‐FTIR) analysis shows that lower temperature can be more effective for condensable products formation from CW as compared to higher temperature. Pyrolysis gas chromatography (Py/GC‐MS) confirms presence of the high energy and value‐added chemical compounds such as toluene, benzene and phenols among pyrolytic products. According to initial reports, liquid pyrolytic products produce more than 70% energy. All these results demonstrate that CW could be promising source of bioenergy and valuable bio‐chemicals production via pyrolysis. [ABSTRACT FROM AUTHOR]

[Display omitted] • Machine learning is used to predict the power conversion efficiency (PCE). • Gradient boosting regressor outperforms other models in PCE prediction. • 30 polymer donors have identified and evaluated for synthesis. • Selected donors have exhibited significant structural similarity. The design of novel polymer donors for organic solar cells has been a major research focus for decades, but discovering unique materials remains challenging due to the high cost of experimentation. In this study, machine learning models are employed to predict power conversion efficiency (PCE), Mordred descriptors are used for model training. Among the four machine learning models evaluated, the gradient boosting regressor emerged as the best-performing model. Additionally, a chemical library of polymer donors was generated and analyzed using various measures. 30 donors with highest PCE are selected and their synthetic accessibility is evaluated. Similarity analysis has indicated much resemblance in selected polymer donors. [ABSTRACT FROM AUTHOR]

[Display omitted] The communalization of all-polymer solar cells depends on the cost of active layer materials. We have introduced a framework to find the easily synthesizable polymers. Breaking Retrosynthetically Interesting Chemical Substructures (BRICS) method is used to generate a large database of polymers and synthetic accessibility of generated polymers is predicted. The generated database is visualized using various methods. Energy levels of polymers are also predicted using pretrained machine learning models. Polymers are screened on the basis of predicted properties. Library of polymers are displayed using the T -distributed Stochastic Neighbor Embedding (t -SNE) visualization. Structure Activity Landscape Index (SALI) visualization is also used. A significant change is observed in synthetic accessibility score on structural changes. The histagradient boosting regressor is used to predict the energy levels of polymers that energy levels play significant role in the selection of materials for organic photovoltaic cells. Synthetic accessibility of polymers is analyzed and a significant number of polymers are easy to synthesize. Thirty polymers are selected through screening process that are potential candidates for organic photovoltaic cells. [ABSTRACT FROM AUTHOR]

• Unique core/shell ternary photocatalyst based on noble-metal-free CoSe was synthesized successfully. • Facile aqueous solution method was used. • Synergistic effect of ZnSe and CoSe enhanced the H 2 evolution rate to 785 µmol h−1 under optimal conditions. • Apparent quantum yield was boosted to 55.3% at monochromatic 420 nm light. • Based on spectroscopic and electrochemical results a proposed mechanism has also been discussed. The development of inexpensive cocatalysts based on earth-abundant-elements, is highly demanding strategy to produce large-scale photocatalytic H 2 for real-life applications. Herein, we report the utilization of CoSe as a highly efficient cocatalyst to significantly improve the photocatalytic H 2 production on ZnSe/CdS under visible irradiation. The maximum H 2 production rate of 785 μmol h−1 was attained under optimized conditions. Furthermore, the current system showed robust stability up to 30 h of irradiation and more than 23 mmol of H 2 was produced. The highest apparent quantum yield of the system was 55.3% at monochromatic 420 nm light. Photocurrent responses, UV–vis diffuse reflectance absorption spectroscopy, electrochemical impedance spectroscopy and photoluminescence (PL) spectroscopy were used to explain the mechanistic roles of CoSe and ZnSe in current photocatalytic system. This work showcased the successful usage of CoSe as cocatalyst and ZnSe as visible light active semiconductor for inexpensive conversion of solar energy to H 2. [ABSTRACT FROM AUTHOR]

The paper introduces a new class of convexity named strongly modified (p, h)-convex functions and establishes various properties of these functions, providing a comprehensive understanding of their behavior and characteristics. Additionally, the paper investigates Schur inequality and Hermite-Hadamard (H-H) inequalities for this new class of convexity. Also, H-H inequalities are proved within context of Riemann-Liouville integrals and Caputo Fractional derivatives. The efficiency and feasibility of Schur inequality and H-H inequalities are supported by incorporating multiple illustrations, that demonstrate the applicability of strongly modified (p, h)-convex functions. The results contribute to the field of mathematical analysis and provide valuable insights into the properties and applications of strongly modified (p, h)-convex functions. [ABSTRACT FROM AUTHOR]

Pyrolysis, a promising method for extracting energy and value-added chemicals from municipal solid waste (MSW), encounters challenges associated with generation of inferior quality oil and low-quality char byproducts. In this context, this study proposed catalytic reforming of pyrolysis volatiles derived from MSW, using MSW char (MSWC) and modified MSWC-based catalysts to produce high-quality oil containing monocyclic aromatic hydrocarbons (MAH), alkanes and alkenes, and H 2 -rich gas products. Three groups of modified MSWC-based catalysts are prepared through the activation of MSWC with Na 2 CO 3 , Zn(NO 3) 2 ⋅6 H 2 O and ZnCl 2 for the reforming process. Experimental findings reveal that impregnating MSW with ZnCl 2 , specifically the ZnCl 2 /C-1:1 char from one-step pyrolysis, exhibits the most favorable catalytic performance, with a 47% selectivity of MAH in oil product, yielding > 25 wt% oil at 450 °C and a calorific value of 26.24 MJ/kg. Simultaneously, 44 wt% of MSW is converted into gas products with a 49 v/v% H 2 concentration for ZnCl 2 /C-1:2 catalyst. The Na 2 CO 3 -activated MSWC catalyst corresponds to the highest oil yields and achieves an alkane selectivity of 45.95 area% in the oil product, while Zn(NO 3) 2 -activated MSWC catalyst corresponds to the highest oxygen compounds in the oil product, thus with the lowest calorific value of 19.4 MJ/kg. These results highlight that varying catalyst modifications to MSW char can effectively improve the production of distinct oil compounds, with Zn[OH]+ species and higher Lewis-acid content in the catalyst exerting a positive influence on aromatic hydrocarbon production; and Na 2 CO 3 facilitating alkane production. This study thus provides valuable insights for upgrading oil product quality and increasing H 2 concentration in gas products, particularly at lower pyrolysis and reforming temperatures. • The effective catalysts for producing hydrocarbon oil contain Lewis-acid. • The ZnCl 2 /C-1:1 catalyst obtained the highest monocyclic aromatic hydrocarbons selectivity of 47%. • Three groups of char-based catalysts all improved H 2 concentration in the gas. • The ZnCl2/C-1:2 catalyst corresponds to H 2 content of 49% in gas product. [ABSTRACT FROM AUTHOR]

Cocatalysts play a critical role in the activity and stability of photocatalytic systems. Currently, efficient cocatalysts mainly comprise of expensive noble metals. Herein we report a composite photocatalyst consisting of CdS nanorods (NRs) and noble-metal-free cocatalyst NiSe, which efficiently enhances the hydrogen production activity of CdS NRs under visible light. NiSe was synthesized through a facile aqueous solution method and CdS/NiSe NRs composites were prepared by in situ deposition of NiSe on CdS NRs. This provides increased contact between cocatalyst and photosensitizer leading to enhanced electron transfer at the interface of NiSe and CdS. The current photocatalytic system gave the highest hydrogen evolution rate of 340 µmol h−1 under optimal conditions. The enhanced stability of the system was observed for 30 h of irradiation resulting in 14 mmol of hydrogen evolution. The highest AQY of 12% was observed using the 420 nm monochromatic light. In addition, CdS/NiSe NRs showed significant higher H 2 evolution rate than that of 1.0 wt% loaded CdS/Pt NRs proving NiSe as highly efficient cocatalyst. Photoluminescence spectra and the photocurrent response were used to confirm the efficient charge transfer at the interface of NiSe and CdS nanorods. The work presented here demonstrates the successful use of an inexpensive, non-noble-metal cocatalyst for enhanced photocatalytic hydrogen production. [ABSTRACT FROM AUTHOR]

• Relationship between un-saturation (C C), hybridization and HHV was investigated. • Relationship of oxygen bearing functional groups and HHV was examined. • Relationship of reactants thermally stability and HHV was studied. • The proposed statements were validated by FTIR and HHV comparison. The aims of this study is to investigate the effects of functional groups like C C and C OH on variation of higher heating values (HHV) of organic compounds. HHV of fuel hydrocarbons, gaseous and liquids including single bonded and multiple bonded carbons and green tea polyphenols (GTP) were determined by using Bomb Calorimeter. It was observed that, multiple bonded carbon and oxygen bonded carbon i.e. C C and C O result in less carbon reduced state while, also increase endothermicity of reactants by changing hybridization state with more s-character and hence, contribute to lower level of HHV. Besides, hydrogen bonding was also considered as the major cause of the difference in HHV of fuel hydrocarbons having the same molecular formula but different oxygen-bearing functional groups due to structure stabilization. These statements were further supported by the combination of Fourier transform infra-red spectra (FTIR) and HHV calculation of raw GTP (set as a representative of biomass) and its solid products obtained at 250 °C and 350 °C by thermal treatment done by using high temperature tube furnace. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Canola oil factory residue was investigated as potential pyrolysis feedstock. • Residue has higher volatile matter, lower moisture content than other biomasses. • Distributed activation energy model applied to canola residue pyrolysis. • Evolved gas analysis showed CO 2 emission increased with temperature. • Thermodynamic and kinetics analysis suggested pyrolysis optimized ∼450 °C. Abstract The identification of biomasses for pyrolytic conversion to biofuels depends on many factors, including: moisture content, elemental and volatile matter composition, thermo-kinetic parameters, and evolved gases. The present work illustrates how canola residue may be a suitable biofuel feedstock for low-temperature (<450 °C) slow pyrolysis with energetically favorable conversions of up to 70 wt% of volatile matter. Beyond this point, thermo-kinetic parameters and activation energies, which increase from 154.3 to 400 kJ/mol from 65 to 80% conversion, suggest that the energy required to initiate conversion is thermodynamically unfavorable. This is likely due to its higher elemental carbon content than similar residues, leading to enhanced carbonization rather than devolatilization at higher temperatures. Evolved gas analysis supports limiting pyrolysis temperature; ethanol and methane conversions are maximized below 500 °C with ∼6% water content. Carbon dioxide is the dominant evolved gas beyond this temperature. [ABSTRACT FROM AUTHOR]

Abstract This study illustrated the pyrolysis of banana peel (BP) as a potential waste management solution. Samples were characterized through Fourier transform infrared spectrometry (FTIR), elemental analysis, and high heating value (HHV) calculation. After pyrolysis experiments were performed at different heating rates (10, 20, 30, and 40 °C min−1) by using a thermogravimetric analyzer coupled with FTIR (TG-FTIR), the apparent activation energies were computed with Friedman, Kissinger–Akahira–Sunose (KAS), and Flynn–Wall–Ozawa (FWO) methods, and the evolved gaseous products were analyzed simultaneously. During pyrolysis, BP underwent three devolatilization steps accompanied by the evolution of some major gaseous products, including CO 2 , CH 4 , H 2 O, CH 3 COOH, C C, C 6 H 5 OH, HCOOH, and CH 3 CH 2 OH. Among them, C C, CH 3 COOH, and CO 2 accounted for approximately 71.56% of the total gaseous products. Gas evolution was more significantly influenced by the pyrolysis temperature than by the heating rate. Pyrolysis–gas chromatography/mass spectrometry (Py-GC/MS) analysis confirmed the presence of some high-energy compounds and valuable chemicals containing aromatic, aldehyde, ketone, and other functional groups. In terms of preliminary energy balance, more than 70% of the total energy output was attributed to the liquid pyrolytic products followed by the solid and gaseous products. The energy recovery ratio of BP pyrolysis was superior to that of other fuel feedstocks. This work provided insights into resolving environmental problems associated with BP management by pyrolyzing BP as a potential source of renewable bioenergy. Highlights • Pyrolytic kinetic and gaseous products of banana peel were investigated. • Banana peel showed bioenergy potential in terms of E a , HHV, and Py-GC/MS analysis. • Relative content of gas product was more affected by temperature than heating rate. • Its energy input and output was comparable with other established feedstocks. [ABSTRACT FROM AUTHOR]

Tedious polymeric modifications, laborious syntheses, low water solubility, high cytotoxicity and low quantum yields are still the major concerns related to fluorescent materials used for biosensing applications. Here we report one-pot facile synthesis of polyethylene glycol (PEG)-modified cystamine-based multifunctional fluorescent dyes named as CPEG-168, CPEG-1000 and CPEG-2000, which can be used for biosensing and detection of both cations and anions in pure aqueous medium. Rapid response toward thiols could be achieved due to the presence of disulfide functionality and reversible redox property between thiol and disulfide. A cyclic structure is formed by (NH-H) intramolecular hydrogen bonding to produce electron dense region for strong fluorescence emission. The presence of -NH group made them sensitive toward Cu2+ ions, while excellent sensitivity toward pH and F− ions was provided by intramolecular hydrogen bonding between -NH groups. The increase in chain length of PEGs enhanced the fluorescence emission intensities due to the formation of more rigid structure, and hence, the desired quantum yields can be tuned by changing the chain length of PEG. The aforementioned properties along with high brightness in aqueous solution, at biological pH and temperature range, make these fluorescent dyes potential candidates for biosensing applications and detection of cations and anions. [ABSTRACT FROM AUTHOR]

Abstract Development of novel environment friendly self-crosslinker, which enables polymers crosslinking without thermal treatment, is highly desirable for several industrial scale applications such as paints and dyes. Herein, we reported a one-pot facile synthesis of hyperbranched polyvinylbenzylchloride (HBPVBC) followed by its quaterinization with pyridine. The resulting product (QHBPVBC) was then used as a crosslinker. Detailed characterization of the HBPVBC and QHBPVBC were conducted by nuclear magnetic resonance (1H NMR), Fourier transform infrared spectroscopy (FTIR) and thermo-gravimetric analysis (TGA). Four different quaterinized crosslinked membranes (QHPs) were prepared by changing the ratio of pyridine to HBPVBC, denoted as QHP-0.25, QHP-0.50, QHP-0.75 and QHP-1.0. Sol-Gel analysis and elemental analysis were used to evaluate the degree of crosslinking and quaterinization of all QHPs. Furthermore, crosslinking effectiveness of QHBPVBC was also demonstrated by introducing QHP-0.50 in sulfonated polyphenyloxide polymer (H+sPPO) structure and observing the mechanical and thermal properties. Initiator-free preparation of QHBPVBC and the self-crosslinking ability in pure aqueous medium at ambient temperature exhibited high effectiveness and environmental-friendly nature. All these outstanding properties facilitated the enhancement of thermal and mechanical properties of linear polymers, making QHBPVBC a potential crosslinker candidate for various industrial applications. Highlights • Initiator-free quaterinized hyperbranched polyvinylbenzylchloride (QHBPVBC) was synthesized successfully. • Structure confirmation was done by 1H-NMR and FTIR analysis. • Effect of Quaterinization on degradation temperature and thermal stability was studied. • Effect of solvent and temperature was studied to see its chemical stability and durability at higher temperature. • Crosslinking effectiveness of QHBPVBC was examined by crosslinking linear structure of H+sPPO. [ABSTRACT FROM AUTHOR]

[Display omitted] • Furniture waste pyrolysis was studied using TG-FTIR at 10, 20, and 40 °C/min. • Average E a of 173.34 (±1.11) kJ/mol was needed to make the activated complex. • ANN model predicted pyrolysis with an MSE of 0.3694 and an adjusted R2 of 0.9686. • Pyrolysis products contained –OH and –COOH groups and CH 4 , CO 2 , and H 2 O. The thermal conversion of waste furniture boards (WFB) was performed in a coupled TG-FTIR under an inert atmosphere at different heating rates (10, 20, and 40 °C/min). Kinetic analysis was performed on the obtained TG data using model-free methods. The average activation energy (E a) calculated from three different model-free methods viz Friedman, KAS, and OFW was 175.91 (±3.50), 171.41 (±5.01), and 172.70 (±5.06) kJ/mol, respectively. Distribution of activation energy (DAEM) was performed assuming multiple area normalized Gaussian distributions of pseudo-components. Thermodynamic parameters indicate that the conversion of WFB under pyrolytic conditions is endothermic, but favorable. Artificial neural network model was used to predict thermal degradation. Mean square error (MSE) of 0.3694 and adjusted R 2 value of 0.9686 indicate the agreement of experimental and model prediction. WFB pyrolysis produced molecules with –OH and –COOH functional groups, and small gas molecules such as CH 4 , CO 2 , and H 2 O. [ABSTRACT FROM AUTHOR]

In the present study, an innovative method of fermentation osmotic activation was employed to produce activated carbon derived from rice husk. The fermentation procedure dismantles the robust lignin present on the rice husk surface, thereby liberating amorphous SiO 2 , enhances osmotic activation, and promotes pore development. Following the high-temperature osmosis treatment utilizing a KOH solution, a significant portion of the amorphous SiO 2 on the husk's surface is successfully dissolved and removed, leading to the formation of numerous carbon skeletons. This procedure culminates in the production of activated carbon characterized by a high specific surface area and substantial pore volume, attributed to the role of K ions during carbonization. The sample DFRO1-AC exhibited the most effective hydrogen gravimetric storage capacity of 1.21 wt% at room temperature (25 °C) and 80 bar pressure, featuring a BET specific surface area of 2270 m2/g. Compared to other activated carbons used for hydrogen storage, our materials demonstrate superior hydrogen storage capacity and employ renewable agricultural waste, indicating high feasibility and broad application prospects. • An innovative method of fermentation osmotic activation. • The fermentation can promote pore development. • Osmotic activation eliminates SiO 2 and etches the carbon skeleton. • Excellent hydrogen storage capacity at room temperature. [ABSTRACT FROM AUTHOR]

This study proposed upgradation of products by co-pyrolyzing a hydrogen-deficient waste, such as cabbage waste, with a hydrogen-rich waste, such as tire trash, demonstrating full resource utilization in an economical way. The effect of tire waste (TW) on the pyrolysis of cabbage waste (CW) was investigated to determine the coupling synergistic interaction. Four distinct samples, CW, 25 T%W, 50%TW, and 75%TW, were prepared by changing the mass ratio of TW to CW. The activation energy was calculated by Ozawa-Flynn-Wall (OFW) and the Kissinger-Akahira-Sunose (KAS) methods and found lowest for 50%TW as 108.39 kJ/mol (OFW) and 116.63 kJ/mol (KAS). The relative concentration of pyrolytic products including CO 2 , CH 4 , NH 3 , NO, CO, C C, C 6 H 5 OH and SO 2 was determined by TG-FTIR analysis, and it was found that the concentrations changed significantly with temperature. Py/GC-MS results showed that as the mass ratio of TW increased, the yield of olefins and MAHs increased as well, reaching maximum levels of 29.76% and 26.78% for 75%TW, respectively. In contrast, the yield of OCs and PAHs declined, reaching minimum levels of 7.83% and 5.01% for 75%TW, respectively. Increased production of olefins and MAHs, particularly D -limonene, toluene, and benzene, demonstrates the significance of co-pyrolyzing CW and TW to meet energy needs for the chemical sector. While reduced PAH production has environmental benefits since it reduces the risk of cancer and cardiovascular diseases. Thus, in order to upgrade pyrolytic products, TW and CW may have a significant coupling synergistic interaction. • Hydrogen-deficiency of a waste is fulfilled by a hydrogen-rich waste reflecting economical approach. • Full resource utilization of hydrogen-deficient cabbage waste is proposed by co-pyrolyzing with tire waste. • Yield of D -limonene, toluene and benzene increased reflecting energy and chemical process importance. • As a result of co-pyrolysis, decreased yield of PAHs demonstrates environmental advantageous. • 50%TW was found to have most effective synergic effect for catalytic activity and products upgradation. [ABSTRACT FROM AUTHOR]

Organic molecules have been extensively utilized in various applications including materials science, chemical, and biomedical fields. Traditionally, the design of organic molecules is achieved through experimental approaches, guided by conceptual insights, intuition, and experience. Although these experimental approaches have been successfully utilized to unveil various high‐performance materials, these methods show serious limitations due to vast design space and the ever‐increasing demand for organic molecules (new materials). Artificial intelligence with computer science is used by modern researchers to design materials with better performance and for predicting the properties of new materials. Herein, benzofuran‐based building blocks are used as a standard molecule to search for new building blocks. Similarity analysis is performed to screen/search potential candidates for photodetectors based on the chemical structural similarity. Extended‐connectivity fingerprints (ECFPs) are used for the similarity analysis. The virtual libraries of unique monomers are enumerated. The breaking retro‐synthetically interesting chemical substructures (BRICS) method is also used to design building blocks by automatically decomposing and combining monomers in enumerated libraries. Moreover, this work offers a potential way to identify new monomers for photodetectors cost‐effectively and rapidly. [ABSTRACT FROM AUTHOR]

Organic solar cells (OSCs) are gaining fame for their cost-effective solution processing. Machine learning is increasingly popular for material design in OSCs. In this study, molecular fingerprints are used to train over 40 machine learning models. The random forest regressor emerges as the most predictive one. 10k new dyes are generated. A pre-trained ML model is used to predict their reorganization energy values. Dyes are selected on the basis of reorganization energy, dyes with lower reorganization energy are retrained. The synthetic accessibility of chosen dyes is then analyzed. Chemical similarity analysis has indicated reasonable resemble among selected dyes. [Display omitted] • Machine learning optimized dye design for lower hole reorganization energy. • Trained over 40 models, Random Forest is identified as top predictor. • Generated 10k new dyes for comprehensive analysis. • Dyes with lower reorganization energy are prioritized and retrained. • Analysis confirms accessibility of chosen dyes for practical application. [ABSTRACT FROM AUTHOR]

The generalization of strongly convex and strongly m-convex functions is presented in this paper. We began by proving the properties of a strongly modified (h , m) -convex function. The Schur inequality and the Hermite–Hadamard (H-H) inequalities are proved for the proposed class. Moreover, H-H inequalities are also proved in the context of Riemann–Liouville (R-L) integrals. Some examples and graphs are also presented in order to show the existence of this newly defined class. [ABSTRACT FROM AUTHOR]

• Fast pyrolysis of tea waste was perfumed for phenolic-rich bio-oil production. • TG-FTIR analysis was performed to optimize pyrolysis temperature. • GC–MS analysis was performed to see authenticity of TG-FTIR analysis. • Activated carbon + KOH investigated as efficient catalyst to enhance phenols yield. • Switchable hydrophilic solvent (SHS) was used to isolate phenols. Fast pyrolysis has appeared as a promising technology for bio-fuels and bio-based chemicals production. This paper reports tea waste (TW) as a promising source of phenolic-rich bio-oil production via catalytic fast pyrolysis in fluidized bed reactor. TG-FTIR analysis is applied to optimize pyrolysis temperature for phenolic-rich bio-oil production and maximum phenols yield (17.3 %) was obtained at 500 °C. Meanwhile, bio-oils are produced at each corresponding temperatures of TG-FTIR analysis and GC–MS analysis is performed to examine consistency and authenticity of TG-FTIR results. Moreover, catalytic effect of activated carbon + KOH (AC-K) with different biomass-to-catalyst ratio was examined to optimize most efficient ratio at 500 °C and maximum phenol contents (39.72 %) were obtained at 1:6 ratio of biomass-to-catalyst as compared to non-catalytic pyrolysis (23.85 %). Moreover, effect of temperature on product distribution was examined and maximum liquid yield (40.7 %) was obtained at 500 °C. Furthermore, switchable hydrophilic solvents (SHS) were used to isolate phenols from bio-oil with 95.02 % yield calculated by GC–MS analysis. The elemental analysis and high heating value (HHV) of the crude and residue bio-oil were determined. The carbon contents and HHV of residue bio-oil were increased to 67.3 % and 13.7 MJ/Kg from 46.2 % and 10.38 MJ/Kg respectively while; oxygen contents were decreased to 19.7 from 45.3 % demonstrating its potential for fuel application. [ABSTRACT FROM AUTHOR]

[Display omitted] • Mango Peel as a source of bioenergy, bio-based chemicals productions was investigated. • The apparent activation energy was found in range of 88 kJ mol−1 to 304 kJ mol-1. • Evolved gas analysis was performed to examine chemical compounds. • Py/GC-MS was performed to confirm theme of research manuscript. Waste biomass is one of the promising feedstocks for bioenergy generation in present era. The aim of present study is to demonstrate Mango peel as a source of bioenergy, bio-based chemical and phenols productions via pyrolysis, thermodynamics and evolved gas analyses. Therefore, samples were characterized through thermogravimetric analyser, Fourier transform infrared spectrometry (FTIR) and pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). Pyrolysis experiments were carried out at different heating rates i.e. 10, 20 and 30 °Cmin−1 and thermodynamic parameters were calculated using different kinetic models. The apparent activation energy was found in range of 88 kJ mol−1 to 304 kJ mol−1 with all kinetic models. Evolved gaseous analysis was performed though out pyrolysis and between three temperature intervals for qualitative and quantitative analysis of pyrolytic compounds. Similarly, effect of temperature and heating rate on pyrolytic compounds concentrations were also carried out. Release of several chemicals, aromatics compounds and high phenols concentration i.e. 21.01 % via Py/GC-MS confirms its potential for bioenergy, bio-based chemicals productions and anti-oxidant characteristics associated with phenolic compounds. [ABSTRACT FROM AUTHOR]

[Display omitted] • Machine learning is used to predict power conversion efficiency of organic solar cells. • Gradient boosting regression has identified as best ML model. • Designed and evaluated 10,000 small molecule donors. • Chemical similarity analysis has revealed reasonable structural resemblance. • Synthetic accessibility has indicated easy synthesis for majority of selected donors. Designing of materials for organic solar cells is a tedious process. In present study, machine learning (ML) is used to predict the power conversion efficiency (PCE). Over 40 ML models are tried. Gradient boosting regression is appeared as best model. 10k small molecule donors are designed. Their PCE values are predicted using best model. The library of generated donors is visualized using various tools. Chemical similarity analysis is done to study structural behavior of selected donors. Reasonable resemblance is found. Synthetic accessibility assessment has indicated easy synthesis for majority of selected small molecule donors. The introduced framework has ability to find the efficient materials in short time. [ABSTRACT FROM AUTHOR]

• Pyrolysis feedstock potential of Safflower residues were investigated. • Main decomposition stage was well defined with first-order reaction model. • CO 2 , C 6 H 5 OH, and C C were determined as the main pyrolysis gas products. • p y-GC/MS results confirmed the presence of high energy-containing compounds. • Low-temperature could be more suitable for bioenergy and phenols production. In this study, pyrolysis kinetics and thermodynamic parameters of Safflower residues (SR) obtained from oil extraction were investigated by using TG/DSC-FTIR and py- GC/MS. Thermal analysis was performed from ambient temperature to 750 °C under a nitrogen atmosphere. The first-order reaction kinetics model was applied to thermal analysis data to determine apparent kinetic parameters. Activation energy and pre-exponential factor were calculated as 76.60 kJ.mol−1 and 1.89x106 min−1, respectively. The thermodynamic parameters such as the change in Gibb's free energy, the difference in enthalpy and the entropy change were calculated to be 201.36 kJ mol−1, 71.79 kJ mol−1, and −0.196 kJ mol−1, respectively. TG/FTIR analysis revealed that CO 2 , C 6 H 5 OH, and C C functional group as the main pyrolysis gas products. According to Py- GC/MS results of SR, the presence of high energy-containing compounds among the pyrolysis products was proved. All these results show that SR is suitable for pyrolysis to produce biofuel and/or chemicals. [ABSTRACT FROM AUTHOR]

• TG-FTIR technique for high quality bio oil production was proposed. • Bio oil was produced at different temperature to support TG-FTIR analysis. • Chemical composition of bio oil was confirmed using GC–MS analysis. • Complex method was applied for phenols extraction. The present study proposed successfully TG-FTIR as a novel technique for high quality bio oil production having desirable concentration of targeted products especially phenols and acids. TG-FTIR analysis of pine waste was performed to calculate yield of desirable chemical compounds at different temperature ranged 350−600 °C. While, bio oil was produced at each corresponded temperatures and GC–MS analysis was carried out to support TG-FTIR analysis which showed high consistency. Additionally, efficient extraction of phenolic compounds was performed via complex method using Ca2+ as a complex agent under alkaline conditions which showed that 95 % phenols were extracted successfully. Meanwhile, production of calcium acetate in residue bio oil turns this study highly valuable for future scientific research. [ABSTRACT FROM AUTHOR]

• Fe 3 C/CdS NRs composite photocatalysts were designed using a facile method. • The composite photocatalyst showed ∼15 times higher photocatalytic activity than pure CdS NRs. • Role of Fe 3 C as a cocatalyst was thoroughly studied using spectroscopic and electrochemical techniques. Development of photocatalytic systems for scalable and inexpensive solar hydrogen (H 2) requires non-noble-metal cocatalysts. Integration of Fe 3 C with 1D metal sulfide photo-absorber highly improved the activity and durability of photocatalytic H 2 evolution. Consequently, the enhanced hydrogen evolution rate of 195 μmol h−1 was achieved which is 15 times higher than that of pure CdS NRs. The apparent quantum yield was approached to 11.5 % under monochromatic 420 nm light. On the other hand, photocorrosion of the photosensitizer was hampered and the system demonstrated robust stability of 22 h under visible light. Moreover, Fe 3 C showcased high current density with low overpotential for electrocatalytic HER, proving it feasible for H 2 evolution. The role of Fe 3 C was comprehensively studied using electrochemical studies, BET surface area, PL and TRPL spectroscopy. This work demonstrated the exceptional potential of Fe 3 C/CdS NRs as a promising inexpensive photocatalyst with high activity and stability for H 2 production under visible light. [ABSTRACT FROM AUTHOR]

• Three hyperbranched poly(amido amine)s (PAMAM) have been synthesized. • These PAMAM were characterized by HNMR, CNMR and GPC. • Detailed optical properties of these PAMAM were investigated in pure aqueous medium. • MBAP was found to be insensitive to thiols due to absence of disulfide group but sensitive to pH and could be a potential candidate for bioimaging. • CBAB has low quantum yield in water but could be used for different functionalization due to presence of vinyl terminal group. • HPAP was found to be highly water soluble and sensitive to DDT. Therefore, it used for detection of thiols. Hyperbranched Poly(amido amine)s (PAMAM) especially those containing disulfide groups have been highly investigated for DNA condensation, drugs and genes delivery. However, the detailed optical properties of PAMAM, intrinsic fluorescence characteristics and their applications for detection of thiols in pure aqueous medium have not been explored yet. Here we report for the first time the detailed investigation of optical properties of PAMAM and their biosensing application in pure aqueous medium. Three PAMAM named as MBAP (without disulfide group), CBAP and HPAP (with disulfide) groups were chosen, synthesized and characterized. Quantum yields of these PAMAM at different pH value were calculated in pure aqueous medium and all the PAMAM were found to be pH sensitive due to presence of tertiary amine and have shown increase in quantum yields in acidic medium. MBAP was not found to be redox sensitive due to the absence of disulfide group while CBAP has shown low redox sensitivity in aqueous medium because of low water solubility. HPAP has shown high redox sensitivity and high quantum yields hence applied for detection of thiols with the limit of detection of 5.29 mM in pure aqueous medium under physiological conditions. These investigations have not only shown the detailed optical properties of extremely important PAMAM but also broadens their application for bioimaging and biosensing. [ABSTRACT FROM AUTHOR]

For the selection of efficient materials capable of optical applicability, the property of refractive index (RI) of the material is considered extremely significant. Acquisition of RI values via empirical framework is difficult and prolonged task. Consequently, data-driven method provides a rapid alternative for estimating RI values. This study presents a fast framework that is based on machine learning (ML) approach to design novel polymers capable of performing in optical applications. The framework involves training of ML models to predict the RI values of polymers. Subsequently, 10,000 new polymers were generated utilizing the Breaking Retrosynthetically Interesting Chemical Substructures (BRICS) methodology and fast ML model is used to predict the RI values of newly generated polymers. Polymers exhibiting higher RI values were retained. The synthetic accessibility of these selected polymers was also assessed in order to facilitate the future empirical measurements. Furthermore, chemical similarity among the chosen polymers was also investigated and structural diversity was revealed among the selected polymers. This study is introducing fast and easy framework for the designing and screening of efficient materials. This framework can be modified to study other materials and properties. [Display omitted] • Machine learning models are trained to predict the refractive index. • Gradient boosting regressor is best model. • Database of new polymers is generated. • Generated database of polymers is visualized and analyzed. [ABSTRACT FROM AUTHOR]

The comprehensive first-principles analysis of Ba4Sb4Se11 and [Ba4Sb4Se11]:Sr3+ Selenoantimonate Using DFT demonstrates its semiconductor nature, anisotropic ductile properties, and prospective optoelectronic applications, particularly in solar cells and LED technologies, supported by comprehensive structural, electronic, optical, and mechanical studies. All the relevant parameters were determined in this investigation using the framework of DFT by modified Becke Johnson approximations. These parameters include the extinction coefficient, absorption coefficient, energy loss function, reflectivity, refractive index, optical conductivity, and birefringes. The elastic parameters have been calculated based on anisotropic sound velocities and mechanical stability. These parameters include bulk modulus, shear modulus, Young's modulus, and Poisson's ratio. Based on an analysis of the energy band dispersions, it can be concluded that the examined compounds possess semiconductor properties. The data on elastic parameters suggest that the material exhibits anisotropic and ductile characteristics, which could have potential applications in optoelectronics. The Ba4Sb4Se11 (2.2 eV) and [Ba4Sb4Se11]: Sr3+ (1.68 eV) have a direct band gap, which falls within the visible spectrum showing semiconducting nature. The analysis of the thermoelectric properties of investigated compounds has been conducted using the Boltztrap code, marking a significant in scientific research. The study revealed that these compounds have the potential to be utilized in highly challenging transport conditions. Additional investigations and cooperation are essential for understanding the fundamental processes and enhancing the material for effective utilization in various technological applications for solar cells and LED in the energy and optoelectronic industry. [ABSTRACT FROM AUTHOR]

Quantum mechanism calculations were performed to study the relationship between strength of electron‐donating, electron‐withdrawing groups and electronic, photochemical, charge transport properties. Electron‐donating groups blueshifted the ultraviolet (UV)/visible spectra, while electron‐withdrawing groups redshifted the UV/visible spectra. Inverse relationship observes between Hammett parameter and reorganization energy. Small molecules acceptors with electron‐withdrawing substituents showed higher electron mobility. This study can pave way for experimental chemists to synthesize efficient small molecule acceptors. Effect of different electron‐releasing and electron‐withdrawing groups on pi‐spacer of SM acceptor was studied. Strong electron‐releasing and electron‐withdrawing groups significantly reduced the band gap. Electron‐withdrawing groups red‐shifted the absorption spectra. A reverse relationship was observe between reorganization energy and Hammett parameters. SM acceptor with electron‐withdrawing groups showed higher charge mobility. [ABSTRACT FROM AUTHOR]

This study investigates the selective production of aromatics through the co-pyrolysis of cabbage waste and plastics followed by catalytic reforming with Ni-modified ZSM-5. Co-pyrolysis significantly improves the inflection point (a parameter for determining thermal stability) to 251.3 °C from 232.7 °C. This process also enhances the relative yield of olefins and phenols, which are precursors for aromatic production to 25.46 and 17.23 respectively, up from 13.87 to 12.05. Furthermore, catalytic pyrolysis elevates the relative yield of aromatics to 53.4, as confirmed by Thermogravimetric Analysis (TGA) and Thermogravimetric-Fourier Transform Infrared Spectroscopy (TG-FTIR) analyses. GC-MS confirms the enhanced formation of valuable chemical precursors via co-pyrolysis such as phenols and olefines to 18.67 % and 22.13 % leading to a 63.81 % selective yield of aromatics in the final oil with 10Ni/ZSM-5. Ni modification of ZSM-5 is influential in decreasing PAH formation (7.26 % from 20.37 %), indicating that this two-step process is an effective method for transforming waste into high-quality bio-oil with enriched aromatic content. • This study introduces a two-step catalytic co-pyrolysis for converting waste into high-aromatic bio-oil. • Catalytic co-pyrolysis of cabbage waste with plastic using Ni-modified ZSM-5 to produce aromatic-rich oil. • Co-pyrolysis significantly enhances the yields of aromatic precursors such as olefins and phenols. • Incorporating a Ni-modified ZSM-5 catalyst into pyrolysis boost aromatic production. • GC-MS analysis confirms selective yield of aromatics (63.81 %) in the final bio-oil. [ABSTRACT FROM AUTHOR]

Polymer microgels are smart materials used to fabricate and encapsulate different enzymes. Encapsulation of enzymes in the polymer gel particles prolong their life span, enhance and tune their activity in biomedical field to prevent cell damage and make possible tunable drug delivery. Enzymes are natural catalysts and have prodigious ability to make the reaction kinetically feasible. Enzymes encapsulated polymer microgels gained much attention due to their synergistic properties. Here, different methods adopted for the synthesis of enzyme encapsulated polymer microgels, their properties, and classification based on responsive behavior have been described in detail. Applications of these enzyme encapsulated polymer microgels in sensing, catalysis, environmental remediation, useful product formation, and biomedical field to prevent disabilities have also been elaborated with future directions. [ABSTRACT FROM AUTHOR]

Inexpensive cocatalysts are promising materials to improve the performance of photocatalytic systems. However, a cocatalyst can be anchored onto a photosensitizer in certain amount and further loading can hinder the visible light by blocking the active sites of the photosensitizer. Herein, we introduce the utilization of a cobalt based homogenous cocatalyst with composite photocatalyst CdS/Ni3C. The homogenous cocatalyst did not occupy the physical space on the photosensitizer and the performance of CdS/Ni3C was significantly enhanced from 263 μmol h−1 to 750 μmol h−1. The present study showed that homogenous cocatalysts paved a way to further capture the electrons from photoexcited composite photocatalysts and have great potential to enhance the photocatalytic performance for low cost H2 evolution under visible light. [ABSTRACT FROM AUTHOR]

Composite metal oxide catalyst Cu 2 M 9 CeOx (M = Fe, Co, Ni) was prepared by citric acid complexation method for NOx reduction. Prepared catalysts activities were tested in a fixed bed reactor for NOx reduction by CO and H 2. The sequence of activity of the three catalysts for NOx reduction by CO is: Cu 2 Co 9 CeOx > Cu 2 Fe 9 CeOx > Cu 2 Ni 9 CeOx. The catalytic efficiency of Cu 2 Co 9 CeOx increased to 100% at 250 °C. For NO reduction by H 2 , the sequence of activity follows: Cu 2 Fe 9 CeOx > Cu 2 Co 9 CeOx > Cu 2 Ni 9 CeOx. The catalytic activity of Cu 2 Fe 9 CeOx was ranged from 60% to 100% during 250–325 °C. Additionally, characterization was conducted to investigate the physical and chemical properties of Cu 2 M 9 CeOx catalysts. In-situ Fourier transform-infrared spectra was also used to study the mechanism of the reaction. The results indicated that Cu-□-Co synergistic oxygen vacancies in Cu 2 Co 9 CeOx and unstable CuFe 2 O 4 (spinel) in Cu 2 Fe 9 CeOx promote NO reduction. The reasons of activities over different Cu 2 M 9 CeOx catalysts were then carefully explored. • Cu 2 Fe 9 CeOx perform well in NO reduction by CO and H 2. • Cu 2 Co 9 CeOx performs excellent in NO reduction by CO. • Cu-□-Co synergistic oxygen vacancies in Cu 2 Co 9 CeOx promote catalytic activity. • Unstable CuFe 2 O 4 (spinel) is important for the activity of Cu 2 Fe 9 CeOx. [ABSTRACT FROM AUTHOR]

This study examined the potential consequences of doping on Sr2YGaX2O7(X = Fe, Rh) photovoltaic properties. Density functional theory (DFT) was used to calculate pyrochlore oxides' energy band structure and optical characteristics using the full potential linearized augmented plane wave (FP-LAPW) method. The generalized gradient approximation (GGA + U) was utilized to treat the exchange and correlation potential. We studied the metallic atoms Fe, Rh, Y, and Ga orbital electronic states. The utilization of the complex dielectric function facilitated the computation of various optical properties such as the energy band dispersion statistics, absorption coefficient, reflectivity, energy loss function, refractive index, extinction coefficient, and real optical conductivity parameters. We employed Boltzmann transport theory to delve deeper into the electrical transport characteristics (specifically thermoelectric properties) of Fe and Rh-doped pyrochlore oxides in the temperature range of 0–800 K. It is observed that the Sr2YGaRh2O7 compound indicated higher values of ZT 0.9, 1.25 for 50 K and 800 K, respectively. Further, both the compounds exhibit p-type nature as their seebeck coefficient shows a positive region between 50 and 800 K. The materials with strong thermoelectric properties are assumed in high reflectivity zone and potentially effective in solar heating. [ABSTRACT FROM AUTHOR]

Machine learning (ML) analysis has gained huge importance among researchers for predicting multiple parameters and designing efficient donor and acceptor materials without experimentation. Data are collected from literature and subsequently used for predicting impactful properties of organic solar cells such as power conversion efficiency (PCE) and energy levels (HOMO/LUMO). Importantly, out of various tested models, hist gradient boosting (HGB) and the light gradient boosting (LGBM) regression models revealed better predictive capabilities. To achieve the prediction effectively, the selected (best) ML regression models are further tuned. For the prediction of PCE (test set), the LGBM shows the coefficient of determination (R2) value of 0.787, which is higher than HGB (R2 = 0.680). For the prediction of HOMO (test set), the LGBM shows R2 value of 0.566, which is higher than HGB (R2 = 0.563). However, for the prediction of LUMO (test set), the LGBM shows R2 value of 0.605, which is lower than HGB (R2 = 0.606). Among the three predicted properties, prediction ability is higher for PCE. These models help to predict the efficient acceptors in a short time and less computational cost. [ABSTRACT FROM AUTHOR]

This research employs a conventional pyrolysis mechanism model to determine the characteristics and kinetics of waste cabbage leave pyrolysis. Further analysis is conducted using the Multi-path Product Generation model to support the findings. The samples were pyrolyzed from ambient temperature to 1073.15 K at heating rates of 20, 30, 40, and 50 K/min. Average activation energies obtained for waste cabbage leave through the Kissinger-Akahira-Sunose, Flynn-Wall-Ozawa, and Starink methods ranged from 102.44 to 319.71 kJ/mol, 105.43–296.83 kJ/mol, and 115.36–366.19 kJ/mol, respectively. The average values across these methods were 183.97, 181.08, and 207.42 kJ/mol, displaying noticeable variation in activation energies depending on the method employed. Additionally, the model demonstrated: (i) pyrolysis attributes in multi-path reactions, (ii) product-generating regime development, and (iii) valuable insights of pyrolysis product yield and composition. The model comprises three primary devolatilization reactions (iChar, iGas, iTar), two tar cracking reactions (tar to char, tar to gas), and one secondary char reaction. The model parameters accurately align with the thermogravimetric curves at varying heating rates, which demonstrate the progression of multiple product pathways. Thus, this model can be effectively utilized for the simulation of multi-path pyrolysis processes. [Display omitted] • The Multi-path Product Generation model demonstrates multi-path pyrolysis processes. • •The pyrolysis details and characterization of waste cabbage leave were revealed. • The MPG effectively predicts the products yeild of waste cabbage leave. • The model could develop industrial applications for producing high-value chemicals. [ABSTRACT FROM AUTHOR]

This study investigated the pyrolysis of hemp residue using kinetic analysis. The thermal degradation of hemp residue occurred in three temperature ranges, and the activation energy (E a) varied between 147.5 kJ/mol and 299.2 kJ/mol depending on the model-free method used. Combined kinetics, statistical approach, was used to calculate the kinetic triplets including the reaction mechanism and pre-exponential factor besides E a. Boosted regression trees (BRT), machine learning approach, was used to predict the E a during the course of conversion at different heating rates. Bio-oil extracted from hemp residues at different temperatures was examined. The oil contained valuable compounds, including phenols, esters, ethers, aromatics, hydrocarbons, organic acids, furans, alkanes, and ketones. The production of hydrocarbons was found to increase at elevated temperatures, indicating their bioenergy potential. However, acids and esters formation decreased at higher temperatures, while aldehydes were produced by the breakdown of a sugar ring. The study provides insights into the potential of hemp residue to be used in the production of value-added products. • Hemp residue was subjected to slow pyrolysis to analyze its thermal degradation. • Degradation occurred in three temperature ranges, leading to a complex reaction mechanism. • BRT model accurately predicted activation energy at different heating rates. • Bio-oil contains valuable compounds like phenols, hydrocarbons, and D -limonene [ABSTRACT FROM AUTHOR]

Context: Selecting high performance polymer materials for organic solar cells (OSCs) remains a compelling goal to improve device morphology, stability, and efficiency. To achieve these goals, machine learning has been reported as a powerful set of algorithms/techniques to solve complex problems and help/guide exploratory researchers to screen, map, and develop high performance materials. In present work, we have applied machine learning tools to screen data from reported studies and designed new polymer acceptor materials, respectively. Quantitative structure-activity relationship (QSAR) models were generated using machine learning-assisted simulation techniques. For this purpose, 3000 molecular descriptors are generated. Consequently, molecular descriptors having key effect on power conversion efficiency (PCE) were identified. Moreover, numerous regression models (e.g., random forest and bagging regressor models) were developed to predict the PCE. In particular, new materials were designed based on the similarity analysis. The GDB17 chemical database consisting of 166 million organic molecules in an ordered form is used for performing similarity analysis. A similarity behavior between GDB17 materials and the materials reported in literature is studied using RDKit (a cheminformatics software). Noteworthily, 100 monomers proved to be unique and effective, and PCEs of these monomers are predicted. Among these monomers, four monomers exhibited PCE higher than 14%, which is better than various reported studies. Our methodology provides a unique, time- and cost-efficient approach to screening and designing new polymers for OSCs using similarity analysis without revisiting the reported studies. Methods: To perform machine learning analysis, data from reported studies and online databases was collected. Different molecular descriptors were generated for polymer materials utilizing Dragon software. 3D structures of studied molecules were applied as input (SDF; structure data file format). Importantly, about 3000 molecular descriptors were generated. Comma-separated value (.csv) file format was used to export these molecular descriptors. To shortlist best descriptors, univariate regression analysis was performed. These descriptors were further utilized for training machine learning models. Moreover, necessary packages of Python for data analysis and visualization were imported such as Matplotlib, Numpy, Pandas, Scikit-learn, Seaborn, and Scipy. Random forest and bagging regressor models were applied for performing machine learning analysis. A cheminformatics software, RDKit, was applied for similarity analysis. [ABSTRACT FROM AUTHOR]

[Display omitted] • The key parameters of HOMO, LUMO, and λ max values associated with the organic molecules was utilized to perform machine learning (ML) analysis. • The dataset of each parameter was utilized to train the model and the validity of the models was tested against the test dataset. • The Hist gradient boosting (HGB) model exhibited best results for the prediction of the understudy parameters. • The fabrication of high-performance organic solar cells (OSCs) devices was also suggested by using the ML models. Typical research design associated with organic solar cells (OSCs) is conventionally considered time-consuming and laborious because the selection of the materials as the core, pi-acceptor, and terminal groups required for the engineering of these devices is done via hit and trial methodology. The advanced data-driven approaches, particularly machine learning (ML), have materialized as the robust technique for identifying the organic materials for the fabrication of the OSCs devices. The key parameters of highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO), and maximum absorption wavelength (λ max) were selected for developing the ML models. The molecular descriptor associated with each parameter was investigated and the relative contribution of the understudy descriptors in the training of the ML model was studied by using the relative importance test. The Hist gradient boosting (HGB) model exhibited the best results for performing the predictive analysis of all three parameters. Moreover, the chemical database was constructed based on the academic literature to develop the high-performance OSCs devices, and the trained HGB model was applied to predict the HOMO, LUMO, and λ max values for these newly designed OSCs devices. Synthetic accessibility of designed molecules is also predicted which revealed that the suggested new organic molecules can be easily commercialized via experimentation. Highly encouraging results in terms of the understudy key parameters were acquired by this ML approach indicating that the data-driven approaches hold extreme potential for engineering high-performance OSCs devices. [ABSTRACT FROM AUTHOR]

• Machine learning assisted designing of hole-transporting materials for high performance perovskite solar cells. • About 04 machine learning regresssor models are applied for predicting reorganization energy (Rh). • Chemical similarity analysis is used for screening potential candidates for perovskite solar cells. • 30 potential compounds are identified that could be synthesized with ease. In recent years, the advancement of perovskite solar cells has accelerated, leading to continuous performance improvements. Over the past few years, machine learning (ML) has gained popularity among scientists researching perovskite solar cells. In this study, ML is used to screen hole-transporting materials for perovskite solar cells. To construct machine-learning (ML) models, data from prior investigations are collected. Out of four machine learning algorithms trained for predicting reorganization energy (Rh), the gradient boosting regression model stood out as the most effective, attaining an R2 value of 0.89. Data visualization analysis is then utilized to scrutinize the patterns within the dataset. 10,000 new compounds are generated. Chemical space of generated compounds is visualized using various measures. Minor structural modifications resulted in only a slight alteration in reorganization energy (Rh). The newly introduced multidimensional framework has the potential to efficiently screen materials in a short amount of time. [ABSTRACT FROM AUTHOR]

In recent years, data driven methods particularly machine learning have brought a significant revolution in materials discovery specially designing new organic compounds for the batteries. This work presents data-driven designing of organic electrode materials for batteries. In order to train machine learning models, data is collected from previous studies. Importantly, four trained machine learning models are used to predict the redox potential values of the electrode materials. Among others, random forest and bagging regression emerged as the best trained models with an impressive R2 value of 0.72. Using Dragon Software, 4000 descriptors are calculated. Breaking Retrosynthetically Interesting Chemical Substructures (BRICS) tool of RDkit is used to design 20,000 new organic compounds. 50 compounds are selected on the basis of their redox potential. Similarity analysis is applied by using cluster plot and heatmap. Interestingly, the synthetic accessibility score of the newly designed compounds is below 4, which indicates that they can be easily synthesized. This work paves the way for accelerated screening and rational designing of high-performance electrode material for batteries. [Display omitted] • Data-driven designing of organic electrode materials for batteries. • Machine learning models are applied to predict the redox potential of organic electrode materials. • BRICS technique is employed for designing 20,000 compounds. • Chemical similarity analysis is performed for searching potential electrode materials for batteries. • 50 potential compounds are identified that could be synthesized with ease. [ABSTRACT FROM AUTHOR]

[Display omitted] • Machine learning assisted designing of conjugated organic chromophores is introduced. • Light absorption and emission behaviors are predicted. • Structural diversity is unveiled in selected conjugated organic chromophores. Organic materials have several important characteristics that make them suitable for use in optoelectronics and optical signal processing applications. For absorption and emission maxima, the stabilities and photoactivities of conjugated organic chromophores can be tailored by selecting a suitable parent structure and incorporating substituents that predictably change the optical characteristics. However, a high-throughput design of efficient conjugated organic chromophores without using trial-and-error experimental approaches is required. In this study, machine learning (ML) is used to design and test the conjugated organic chromophores and predict light absorption and emission behavior. Many machine learning models are tried to select the best models for the prediction of absorption and emission maxima. Extreme gradient boosting regressor has appeared as the best model for the prediction of absorption maxima. Random forest regressor stands out as the best model for the prediction of emission maxima. Breaking Retrosynthetically Interesting Chemical Substructures (BRICS) is used to generate 10,000 organic chromophores. Chemical similarity analysis is performed to obtain a deeper understanding of the characteristics and actions of compounds. Furthermore, clustering and heatmap approaches are utilized. [ABSTRACT FROM AUTHOR]

• Preparation of silver nanoparticles by single step chemical method using sodium dodecyl sulfate (SDS) as stabilizing agent • Reduction of toxic dye, methyl orange (MO) in presence of Sd@Ag-NPs catalyst using sodium borohydride as reducing agent • Simultaneous reduction of MO and Rhodamine b (Rh-B) dyes in presence of Sd@Ag-NPs catalyst • Catalytic reduction of MO followed first order kinetic • Value of apparent rate constant was increased with increase of catalyst dose in reaction mixture Sodium dodecyl sulfate (SDS) was employed as a stabilizing agent for the preparation of Ag-NPs while using AgNO 3 salt as a source of silver ions and sodium borohydride as a reducing agent in an aqueous medium. Synthesized sodium dodecyl sulfate stabilized silver nanoparticles (Sd@Ag-NPs) were extensively characterized by FTIR, UV–Vis spectroscopy, SEM, XRD and DLS analysis. After that, Sd@Ag-NPs particles were employed as an active catalyst for the reduction of methyl orange (MO) and Rhodamine B (Rh-B) dyes. At first, reduction of MO was carried out in the presence of a catalyst and an excessive amount of NaBH 4 in water medium. Reduction of MO was completed in 21 min while the value of apparent rate constant (k app) was found as 0.385 min −1. Reduction of Rh-B was completed in 15 min with the value of k app found as 0.131 min−1. Controlled reactions were also performed for the reduction of MO in the absence of catalyst and in the absence of NaBH 4 to check the kinetic feasibility of the reaction as well as to check either the decrease in absorbance value was due to the adsorptive action of Sd@Ag-NPs or its working as a catalyst. It was concluded that MO reduction was completed in feasible time in the presence of catalyst. The activity of 9 months old catalyst was slightly decreased as compared to the freshly prepared catalyst for the reduction of MO under same reaction conditions. It was also concluded that Sd@Ag-NPs catalyst successfully degraded the MO dye in the presence of another toxic dye Rh-B in a feasible time. It indicates that our reported Sd@Ag-NPs catalyst can simultaneously reduce the mixture of dyes. [ABSTRACT FROM AUTHOR]

[Display omitted] • Gradient boosting model is best model for the prediction of UV/visible absorption maxima of organic compounds. • Python tools are used to generate and visualize 5,000 new organic compounds. • Promising compounds with red-shifted absorption are identified. • Selected compounds have shown lower synthetic accessibility score. In this study, UV/visible absorption maxima of organic compounds are predicted with the help of machine learning (ML). Four ML models are evaluated, the gradient boosting model has performed best. We also analyzed feature importance. Using Python-based tools, we generated and visualized a new set of 5,000 organic compounds. These compounds were screened based on their predicted UV/visible absorption maxima, selecting those with red-shifted absorption. The assessment of synthetic accessibility indicated that most of the chosen compounds are relatively easy to synthesize. [ABSTRACT FROM AUTHOR]

Bandgap is a key parameter for selecting suitable materials for a broad range of applications. Organic solar cells (OSCs) are emerging as powerful devices due to their low-cost solution processing. Developing OSCs necessitates producing effective materials in a computationally cost-effective and rapid manner. Machine learning has become popular and well-recognized among researchers to screen and design high performance materials for OSCs. Machine learning models require data from the literature (reported studies or databases) to effectively predict targeted properties. To unveil the hidden dataset patterns, a thorough data visualization analysis is conducted. Importantly, multiple database mining is performed for designing low band gap organic semiconductors. Molecular descriptors are utilized to train machine learning models. Importantly, about 22 different machine learning models are tested. Among all models, extra trees regressor shows higher predictive capability. Residuals, learning curve and validation curve are also drawn for extra trees regressor. Feature importance analysis determines the significance of the features. Moreover, library enumeration and similarity analysis further facilitate designing of high-performance semiconductor materials. This work may help in screening and designing efficient semiconductors having low band gap for increasing the efficiency of OSCs. [Display omitted] • Machine learning is applied to design new low band gap organic semi-conductors for OSCs. • Data mining and property prediction strategies are applied to screen potential candidates for OSCs. • The chemical similarity analysis and library enumeration techniques are performed. • More than >20 different regression models are developed and used for better prediction. [ABSTRACT FROM AUTHOR]