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Abstract TiO2‐based composite photocatalysts are currently being explored to address concerns intrinsic to TiO2, specifically high charge carrier recombination and UV light activation. Among various materials utilized for composite formation, carbon nanomaterials stand out due to their high electron conductivity, charge storage, photosensitization, and surface properties. However, high carbon content in composites has been shown to reduce performance with potential toxicity concerns. To harness the diverse properties of carbon nanomaterials in a single composite while optimizing the carbon content below 1% by weight, multi‐walled carbon nanotubes (MWCNT)/TiO2 sensitized by carbon nanodots (CND) are synthesized. The heterojunction formed between MWCNTs and TiO2 in the binary composite reduced the charge carrier recombination rate compared to TiO2. The addition of CNDs to MWCNT/TiO2 induced visible light absorbance of the resulting ternary composite, due to the forbidden electron transitions undergone in CND aggregates. CND/MWCNT/TiO2 exhibited a fivefold and 1.6‐fold increase in photocatalytic degradation of acid orange 7 and tetracycline under visible light compared to TiO2. This enhancement is attributed to the photosensitizing property of CNDs working in synergy with the charge storage ability of MWCNTs. A plausible charge transfer pathway for the activity of CND/MWCNT/TiO2 is proposed.

Abstract Addressing climate change challenges by reducing greenhouse gas levels requires innovative adsorbent materials for clean energy applications. Recent progress in machine learning has stimulated technological breakthroughs in the discovery, design, and deployment of materials with potential for high‐performance and low‐cost clean energy applications. This review summarizes basic machine learning methods—data collection, featurization, model generation, and model evaluation—and reviews their use in the development of robust adsorbent materials. Key case studies are provided where these methods are used to accelerate adsorbent materials design and discovery, optimize synthesis conditions, and understand complex feature–property relationships. The review provides a concise resource for researchers wishing to use machine learning methods to rapidly develop effective adsorbent materials with a positive impact on the environment.

Summary: New photocatalysts are traditionally identified through trial-and-error methods. Machine learning has shown considerable promise for improving the efficiency of photocatalyst discovery from a large potential pool. Here, we describe a multi-step, target-driven consensus method using a stacking meta-learning algorithm that robustly predicts bandgaps and H2 evolution activities of photocatalysts. Trained on small datasets, these models can rapidly screen a large space (>10 million materials) to identify promising, non-toxic compounds as candidate water splitting photocatalysts. Two effective compounds and two controls possessing optimal bandgap values (∼2 eV) but not photoactivity as predicted by the models were synthesized. Their experimentally measured bandgaps and H2 evolution activities were consistent with the predictions. Conspicuously, the two compounds with strong photoactivities under UV and visible light are promising visible-light-driven water splitting photocatalysts. This study demonstrates the power of machine learning and the potential of big data to accelerate discovery of next-generation photocatalysts.

The Chemical Communications journal, published by The Royal Society of Chemistry, features a variety of research articles on topics such as perovskite-based photovoltaics, biomimetic light-harvesting antennas, and non-radioactive methods for screening radiolytic stability. The journal also covers research on reactive oxygen species quantitation, organic photovoltaics, and molecular glue-based Lin28 degraders. Additionally, articles on photocatalysis, DNA modification switching, and cancer chemodynamic therapy are included in this issue. [Extracted from the article]

TiO2‐based composite photocatalysts are currently being explored to address concerns intrinsic to TiO2, specifically high charge carrier recombination and UV light activation. Among various materials utilized for composite formation, carbon nanomaterials stand out due to their high electron conductivity, charge storage, photosensitization, and surface properties. However, high carbon content in composites has been shown to reduce performance with potential toxicity concerns. To harness the diverse properties of carbon nanomaterials in a single composite while optimizing the carbon content below 1% by weight, multi‐walled carbon nanotubes (MWCNT)/TiO2 sensitized by carbon nanodots (CND) are synthesized. The heterojunction formed between MWCNTs and TiO2 in the binary composite reduced the charge carrier recombination rate compared to TiO2. The addition of CNDs to MWCNT/TiO2 induced visible light absorbance of the resulting ternary composite, due to the forbidden electron transitions undergone in CND aggregates. CND/MWCNT/TiO2 exhibited a fivefold and 1.6‐fold increase in photocatalytic degradation of acid orange 7 and tetracycline under visible light compared to TiO2. This enhancement is attributed to the photosensitizing property of CNDs working in synergy with the charge storage ability of MWCNTs. A plausible charge transfer pathway for the activity of CND/MWCNT/TiO2 is proposed. [ABSTRACT FROM AUTHOR]

Sensitively detecting hazardous and suspected bioaerosols is crucial for safeguarding public health. The potential impact of pollen on identifying bacterial species through fluorescence spectra should not be overlooked. Before the analysis, the spectrum underwent preprocessing steps, including normalization, multivariate scattering correction, and Savitzky–Golay smoothing. Additionally, the spectrum was transformed using difference, standard normal variable, and fast Fourier transform techniques. A random forest algorithm was employed for the classification and identification of 31 different types of samples. The fast Fourier transform improved the classification accuracy of the sample excitation–emission matrix fluorescence spectrum data by 9.2%, resulting in an accuracy of 89.24%. The harmful substances, including Staphylococcus aureus, ricin, beta-bungarotoxin, and Staphylococcal enterotoxin B, were clearly distinguished. The spectral data transformation and classification algorithm effectively eliminated the interference of pollen on other components. Furthermore, a classification and recognition model based on spectral feature transformation was established, demonstrating excellent application potential in detecting hazardous substances and protecting public health. This study provided a solid foundation for the application of rapid detection methods for harmful bioaerosols. [ABSTRACT FROM AUTHOR]

The article in Chemical Communications discusses the advancements in perovskite-based photovoltaics and photocatalysis, emphasizing the importance of sustainable energy solutions in light of increasing energy demands and environmental concerns. Perovskite materials have shown promise in solar cells and photocatalysis due to their optoelectronic properties and efficient carrier dynamics. The article highlights significant breakthroughs in the development of perovskite solar cells and photocatalysts, showcasing innovative methodologies and materials that have led to improved efficiencies and expanded applications. ChemComm serves as a valuable resource for cutting-edge research in perovskite materials, offering insights into the future potential of perovskite-based technologies in sustainable energy production and environmental remediation. [Extracted from the article]

The document is a contents list for the Journal of Materials Chemistry A, which features a variety of articles and reviews on topics related to materials chemistry. The journal aims to promote the chemical sciences and reinvest its profits into the chemistry community. The document provides a comprehensive overview of recent research in the field, covering topics such as polymer coatings for batteries, electrolyte flooding in gas diffusion electrodes, infrared spectroscopy for understanding Nafion, and advances in conductive metal-organic frameworks. Each paper focuses on a specific research study and offers detailed findings and analysis. [Extracted from the article]

Conventional Magnéli phase titanium suboxide syntheses often involve highly flammable H2 gas, extensive precursor pretreatment and yield unwanted TiC or TiOxCy byproducts. To overcome these limitations, this work introduces a low-cost, safe and scalable carbothermal reduction synthesis method that eliminates the need for H2 or pretreatment and does not produce carbon-based titanium byproducts. Magnéli phases were generated without physical contact between the bulk organic that acted as the reductant and TiO2. Activated charcoal served as the reductant, reducing TiO2 under an Ar flow, thus limiting O2. The phase transition from anatase TiO2 to Magnéli phases was studied as a function of reduction time. Critical assessment of surface and bulk defects in the series of Magnéli titanium suboxides synthesised revealed a possible redistribution of defects within the lattice with time while maintaining a constant total defect content. Optical property analysis indicated that increasing oxygen deficiency led to increased inter-bandgap absorbance and prolonged lifetime of the photogenerated charge carriers. Oxygen deficiencies exhibited a direct correlation with the water evaporation rate when these Magnéli phase titanium suboxides were applied in solar steam generation. This was attributed to reduced thermal conductivities with increasing oxygen vacancies due to increased phonon scattering by planar defects. The defect-rich Magnéli suboxide sample consisting mainly of Ti6O11 with a remarkably low thermal conductivity of <0.032 W m−1 K−1 at 25 °C showed a solar energy conversion efficiency of 56% when applied in an aerogel for solar steam generation. This method provides flexibility for fabricating materials tailored for diverse applications requiring specific defect concentrations or thermoelectric performance. [ABSTRACT FROM AUTHOR]

The document is a contents list for the Journal of Materials Chemistry A, published by The Royal Society of Chemistry. It provides a comprehensive overview of the research and advancements in the field of materials chemistry, with a focus on energy and sustainability. The list includes reviews and papers on topics such as metal-organic frameworks, self-healing materials, electrochemical energy storage, and photocatalytic water splitting. Each paper is authored by different researchers and explores different aspects of materials science. The document also includes two corrections to previously published papers. [Extracted from the article]

Continued industrial growth along with population growth is putting pressure on clean water supplies. Photocatalysis is an eco-friendly and cost-effective technology that can treat polluted water. As a visible-light-responsive photocatalyst, polymeric carbon nitride (CN) has shown significant potential for photocatalytic pollutant degradation. However, the fast charge recombination, slow charge transport and relatively large band gap restrict its photocatalytic performance. To overcome these challenges and develop a highly efficient photocatalyst, CN with robust visible-light photoactivity was solvothermally synthesised through defect engineering by carbon doping and surface vacancy modification. The additional carbon induces charge delocalization for enhancing charge tranfer and broadens the wavelength range of absorbance. The introduction of surface cyano functionality (–C≡N) not only enhances charge separation but also serves as electron-rich sites, thereby facilitating the generation of superoxide radicals. The controlled introduction of the C dopant and surface cyano group by simply introducing citric acid and varying the LiOH volume during synthesis demonstrated their synergy in optimising the electronic structure and the charge transport. As a consequence, there was a substantial generation of superoxide radicals when exposed to visible light (wavelengths > 420 nm), facilitating the efficient photodegradation of pollutants, such as tetracycline and Rhodamine B. Furthermore, the defect-engineered CN exhibited exceptional performance in the degradation of real-world wastewater. These findings underscore the promising potential of engineered CN for the advancement of visible-light-driven water purification technologies. [ABSTRACT FROM AUTHOR]

Dehydrogenation is considered as one of the most important industrial applications for renewable energy. Cubic ceria-based catalysts are known to display promising dehydrogenation performances in this area. Large particle size (>20 nm) and less surface defects, however, hinder further application of ceria materials. Herein, an alternative strategy involving lactic acid (LA) assisted hydrothermal method was developed to synthesize active, selective and durable cubic ceria of <6 nm for dehydrogenation reactions. Detailed studies of growth mechanism revealed that, the carboxyl and hydroxyl groups in LA molecule synergistically manipulate the morphological evolution of ceria precursors. Carboxyl groups determine the cubic shape and particle size, while hydroxyl groups promote compositional transformation of ceria precursors into CeO2 phases. Moreover, enhanced oxygen vacancies (V€o) on the surface of CeO2 were obtained owing to continuous removal of O species under reductive atmosphere. Cubic CeO2 catalysts synthesized by the LA-assisted method, immobilized with bimetallic PtCo clusters, exhibit a record high activity (TOF: 29,241 h-¹) and V€o-dependent synergism for dehydrogenation of bio-derived polyols at 200 °C. We also found that quenching V€o defects at air atmosphere causes activity loss of PtCo/CeO2 catalysts. To regenerate V€o defects, a simple strategy was developed by irradiating deactivated catalysts using hernia lamp. The outcome of this work will provide new insights into manufacturing durable catalyst materials for aqueous phase dehydrogenation applications. [ABSTRACT FROM AUTHOR]

Inorganic lead‐free halide perovskites, devoid of toxic or rare elements, have garnered considerable attention as photocatalysts for pollution control, CO2 reduction and hydrogen production. In the extensive perovskite design space, factors like substitution or doping level profoundly impact their performance. To address this complexity, a synergistic combination of machine learning models and theoretical calculations were used to efficiently screen substitution elements that enhanced the photoactivity of substituted Cs2AgBiBr6 perovskites. Machine learning models determined the importance of d10 orbitals, highlighting how substituent electron configuration affects electronic structure of Cs2AgBiBr6. Conspicuously, d10‐configured Zn2+ boosted the photoactivity of Cs2AgBiBr6. Experimental verification validated these model results, revealing a 13‐fold increase in photocatalytic toluene conversion compared to the unsubstituted counterpart. This enhancement resulted from the small charge carrier effective mass, as well as the creation of shallow trap states, shifting the conduction band minimum, introducing electron‐deficient Br, and altering the distance between the B‐site cations d band centre and the halide anions p band centre, a parameter tuneable through d10 configuration substituents. This study exemplifies the application of computational modelling in photocatalyst design and elucidating structure–property relationships. It underscores the potential of synergistic integration of calculations, modelling, and experimental analysis across various applications. [ABSTRACT FROM AUTHOR]

Addressing climate change challenges by reducing greenhouse gas levels requires innovative adsorbent materials for clean energy applications. Recent progress in machine learning has stimulated technological breakthroughs in the discovery, design, and deployment of materials with potential for high‐performance and low‐cost clean energy applications. This review summarizes basic machine learning methods—data collection, featurization, model generation, and model evaluation—and reviews their use in the development of robust adsorbent materials. Key case studies are provided where these methods are used to accelerate adsorbent materials design and discovery, optimize synthesis conditions, and understand complex feature–property relationships. The review provides a concise resource for researchers wishing to use machine learning methods to rapidly develop effective adsorbent materials with a positive impact on the environment. [ABSTRACT FROM AUTHOR]

The bulk photovoltaic (BPV) effect of conventional ferroelectric (FE) materials has sparked a great deal of interest due to anomalous above-bandgap photovoltage. However, large bandgaps and weak photocurrents remain longstanding challenges for FE PV materials in practical applications. To address these issues, we propose a new defect-engineering strategy and demonstrate it on a narrow bandgap centrosymmetric material, BiNbO4 (BNO): the BPV effect is introduced into BNO by tuning the defect amounts, then a defect-modified homojunction structure is constructed to enhance the BPV effect. This defect engineering strategy enables synergetic effects, e.g., enhanced light absorption, FE-like depolarization field and interfacial polarization. This homojunction structure results in two-fold promotion of photovoltage and ten-fold promotion of photocurrent, compared to the defect-modified BNO sample. We believe this new strategy will break through limitations in traditional material design and pave a novel route to future multifunctional materials, especially high performance BPV materials. [ABSTRACT FROM AUTHOR]