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To evaluate and compare antimicrobial efficacy of Chlorhexidine and Chlorine dioxide mouthwashes on S.mutansbiofilm created on metal and ceramic self-ligating brackets.

Heterocycle derived moieties, namely, N-(4-methoxyphenyl)-1-(1H-pyrrol-2-yl)methanimine (MPM), 1-(furan-2-yl)-N-(4-methoxyphenyl)methanimine (FMM), and N-(4-methoxyphenyl)-1-(thiophen-2-yl)methanimine (MTM), were synthesized followed by analysis of their structural aspects using FTIR and 1H NMR spectroscopic techniques. The corrosion retarding abilities of the same were distinguished by gravimetric and certain electrochemical measures for mild steel in 0.5 M H2SO4, and MTM was obtained with maximum inhibition efficiency of 97.93% at 250 mg L–1concentration; the thermodynamic and activation parameters were recorded in this regard. The results were further seen to be supported by various surface studies: SEM-EDS, XPS, AFM, contact angle, and UV–visible spectroscopy. Potentiodynamic polarization studies unveiled the mixed nature of heterocyclic inhibitors with overriding anodic effect. Furthermore, the adsorption of inhibitors over mild steel coupons demarcates the prevalence of physical and chemical interactions in the environment. In addition, the computational studies, global and local reactivity, molecular dynamics, and density functional theory, were employed and the experimental results obtained were found in correlation with the theoretical results.

Mutations in BRCA1and BRCA2significantly elevate the risk of developing breast and ovarian cancer. Limited data exists regarding the prevalence of BRCAmutations, and optimal, cost-effective testing strategies in developing countries like India. This study aimed to evaluate the utility of a Next Generation Sequencing (NGS) panel for BRCA1/2mutation testing among women diagnosed with, or at risk of developing hereditary breast and ovarian cancers. We also aimed to identify population specific BRCA1/2mutation hotspots, to enable the development of more affordable testing strategies. We identified 921 patients with breast and ovarian cancer who underwent mutation testing. The target enrichment was followed by targeted NGS in 772 patients and an allele-specific PCR (ASPCR) based genotyping for BRCA1:c.68_69delAG (or 185delAG), was carried out in 149 patients. We identified 188 (20.4%) patients with BRCA1/2variants: 118 (62.8%) with pathogenic/likely pathogenic and 70 (37.2%) with VUS. The 185delAG was identified as a recurrent mutation in the Southern Indian population, accounting for 24.6% of the pathogenic variants. In addition, a family history of breast, ovary, pancreas, or prostate (BOPP) cancer was found to be associated with an increased risk of identifying a deleterious BRCA1/2variant [OR = 2.11 (95% CI 1.45–3.07) p≤ 0.001]. These results suggest that Targeted NGS is a sensitive and specific strategy for BRCAtesting. For Southern Indian patients, a two-tiered approach can be considered: Initial screening with ASPCR for BRCA1185delAG followed by NGS for those testing negative. Expanding the gene panel and identifying other population-specific mutation hot spots is a promising area with potential for improvements in testing and treatment strategies.

The one-phase brushless DC motor (BLDC) has become indispensable in-home appliances due to its high-power density, flexible control, and straightforward driving circuit, outperforming induction and universal motors. Additionally, it ensures higher efficiency across a wide range of speed-torque loads. This paper introduces a pioneering real-time control algorithm based on machine learning to enhance the BLDC motor’s overall performance compared to the traditional fuzzy-PID controller. A dynamic model of the BLDC motor is utilized to determine the EMF (electromotive force) and torque properties through finite element simulations conducted in the ANSYS/Maxwell environment. The targeted BLDC motor is driven by a space vector modulation inverter powered by a DC voltage source. The proposed machine learning-based control algorithm demonstrates superior performance over traditional methods under various load disturbances and reference speed variations, with overshoot/undershoot and settling time improvements of at least 60% and 46%, respectively. The enhanced performance was validated using a comprehensive dynamic model developed in the MATLAB environment and confirmed through an experimental setup.

The development of highly active, durable, and low-cost metal-free catalysts for the photocatalytic CO2reduction reaction (CO2RR) is an efficient and environmentally friendly solution to address significant problems like global warming and high energy demand. In the present study, we have demonstrated the design and synthesis of a donor–acceptor based conjugated microporous polymer (CMP), TPA-GFP, by integrating an electron donor, tris(4-ethynylphenyl)amine (TPA), with a green fluorescent protein chromophore analogue (Z)-4-(2-hydroxy-3,5-diiodobenzylidene)-1-(4-iodophenyl)-2-methyl-1H-imidazol-5(4H)-one (o-HBDI-I3) (GFP). In comparison to nondonor 1,3,5-triethynylbenzene (TEB) based TEB-GFP CMP, photocatalytic CO2reduction using donor–acceptor based TPA-GFP CMP displays a 3-fold increment of CO production yield with a maximum CO yield of 1666 μmol g–1at 12 h. Further, the CO selectivity increases significantly from a mere 54% in TEB-GFP to an impressive 95% in TPA-GFP. The impressive CO2reduction efficiency and selectivity for TPA-GFP can be attributed to the efficient light-harvesting capability and facile charge separation and migration through donor–acceptor building units of the CMP. The mechanistic aspect of the photocatalytic CO2reduction process is explored using in situDRIFTS and DFT calculation, and a plausible photocatalytic mechanism is proposed.

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In this paper, the effect of heat treatment temperature on the resultant composition of composite Al-Si coatings on ZK60A alloy investigated. The coatings were developed using magnetron sputtering deposition. These coatings can serve as an effective anti-corrosion barrier for Mg alloys in aqueous mediums. The magnetron target composition was 80% Al and 20% Si, while in the deposited coating it was 78% Al and 18% Si, with the rest being atoms from the substrate. The as-deposited coatings had pores and the Al-Si composite was deposited in the form of globules. The sputtered coatings were subjected to heat treatment for 1.5 hours at 420 °C (HT1) and 350 °C (HT2). The ratio of elements in the heat-treated coatings changed significantly due to diffusion between substrate-coating interface and migration through the pores that exist in the coating. It was found that Mg migrates into the coating and Si into the substrate. Al/Mg ratio reduced by 81.2 and 60.6 %, respectively, in the coating after the HT1 and HT2.

[Display omitted] Contemporary research is more outlined towards synthesis and utilization of inhibitors to encounter corrosion. Corrosion being a global challenge contributes a significant share to economic and heritage losses every year and corrosion inhibitors owing to their synthesis and ease of applicability have gained prime importance in this regard. Industrial processes, pickling, descaling, etc., make extensive usage of acid solutions (H 2 SO 4 , H 3 PO 4 , HCl) and incorporate inhibitors depicting salient features like possessing lone pairs and heteroatoms (N,P,O,S), etc. for their corrosion averting ability. This present review gives an account of various heterocyclic corrosion inhibitors being utilized for ferrous alloys during series of industrial applications; acid pickling, acid cleaning, descaling, oil–gas industry, and boiler tubes. A diversified literature study unveiled various corrosion monitoring techniques employed along with factors affecting corrosion. [ABSTRACT FROM AUTHOR]

As cryptocurrencies become more popular as investment vehicles, bitcoin draws interest from businesses, consumers, and computer scientists all across the world. Bitcoin is a computer file stored in digital wallet applications where each transaction is secured using strong cryptographic algorithms. It was challenging to forecast the future price of bitcoin due to its nonlinearity and extreme volatility. Several recent classic parametric models have been found with limited accuracy. To address the limitations and fill the existing research gaps, there is a need for a good prediction model which will provide the desired accuracy in the case of uncertainty and dynamism. This research suggested a deep learning-based framework for predicting and forecasting Bitcoin price. The research will be helpful for worldwide consumers and industries to take their decision on whether to invest or not. The research utilizes Yahoo! finance dataset for the period of 01-03-2016 to 26-02-2021 having 1828 samples. The experimental outcomes of the proposed Long Short-Term Memory (LSTM) model outperformed similar deep learning models by securing minimum loss and confirming that it can be used for future price prediction of the cryptocurrencies, which is helpful for the buyer to take their decision.

The demand for medical sensors in the Smart Healthcare System (SHS) creates an intelligent Internet of Medical Things (IoMT) system. This system plays an important role in detecting the vital parameters of the human body. However, security and privacy issues in terms of network vulnerability have arisen due to the transmission of data and lack of control over the data. The Intrusion Detection System (IDS) is one of the security solutions to identify various threats and vulnerabilities in the consumable edge-centric IoMT industry. Several IDS techniques have been developed in previous years. However, a real-time and highly accurate attack detection system in the edge-centric IoMT industry is needed. This paper proposes a blended deep learning framework that leverages the strengths and capabilities of different deep learning architectures. The proposed model combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to recognize the latest intruders accurately and defend the healthcare data. The major outcome of the proposed framework is to detect different attacks during data transmission at the edge of the network with high accuracy and efficiency. The proposed model was analyzed on the CSE-CIC-IDS 2018 systematic dataset containing two distinct classes of profiles. The experimental results demonstrate that the proposed framework’s accuracy is higher than the existing approach.

With the increasing growth of the Electronic healthcare system (EHS), the security of the EHS is an essential requirement because different types of users (patient, doctor, nurse, etc.) are accessing these systems for various purposes like treatment, research, drug analysis, etc. In the EHS, two major security challenges arise. First one is the selection of an access control mechanism without any prior information about the healthcare users. The second one is how much amount of data will be shared by the healthcare services and practitioner. Hence, a suitable access control technique is essential which not only provides the static access but also dynamically control the views of the requested data, so that the information will be shared in a controlled manner. In the healthcare system, trust can be viewed as an important judgment parameter for controlling the access of different stakeholders as it is an open system with different types of users. The main aim of the work is to control the access view so that only authorized user can access the information in a controlled manner. It also improves adaptivity of the access control model by integration of dynamic trust degree of communicating parties. To fulfill the above-discussed security requirements, in this paper, we have proposed an access control model, which is based on the trust degree of the healthcare user and service, named as mutual trust. The assessment of user and service trust degree is based on the beta distribution technique. A rule set has been developed based on this mutual trust degree to control the data access view, which is dynamically changed with the communicating parties trust level. The detail implementation of the proposed model shows that the accuracy and efficiency of the model are better as compared to other models.

The potential of Indian agriculture in atmospheric CO2capture and its role in enabling farmers to monetize C credits is critical to climate change mitigation strategies. This potential is especially significant given the vast and diverse agricultural landscapes across India, which inherently possess the capacity for considerable C sequestration. The realization of this potential hinges on the establishment of a comprehensive policy framework. Essential components of this framework include methodologies for accurate measurement, reporting, and verification (MRV) of C sequestration. To fully harness the potential of Indian agriculture in atmospheric CO2capture and C credit monetization, future endeavors should focus on developing integrated technological solutions, and collaborative efforts are the cornerstone for accurate C measurement and verification, alongside formulating inclusive policies that specifically support small-scale and marginalized farmers in transitioning to carbon-efficient practices for sustainable agriculture. Overall, it will help to implement the aim of monetizing C credits by the Ministry of Power (catenate notice: CG-DL-E-30062023-246859 Dated June 28, 2023) and the Ministry of Environment, Forests, and Climate Change of Developing (catenate notice: CG-DL-E-27062023-246825 Dated June 27, 2023) and adopted the green credit program. The Indian government has demonstrated its will to combat climate change by starting a policy discussion on carbon. This important conversation aims to establish solutions for sustainable development focusing on environmental stewardship and carbon reduction across several sectors. Ultimately, this would enhance the country's defenses against climate change and encourage net zero emission through C-negative agriculture. It will also promote the “Sustainable Development Goals,” which aim to improve the country and the globe.

Introduction: Breast cancer is most common cancer among women and hereditary mutations account for 5–10%. HBOC syndrome is most common hereditary mutation affecting breast & ovary. The prevalence of STIC lesions in these women ranges from 2 to 12% and that in general population is unknown. Role of risk reduction salpingo-oophorectomy (RRSO) is well established among these women. Surgical ovarian ablation also plays role in hormone positive breast cancers in prolonging their survival by permanent hormone suppression. The role of oophorectomy has different impacts in breast cancer. Methodology: The study aimed to determine the different indications for oophorectomy and their histologies among premenopausal breast cancer women. This was a prospective cross sectional study of breast cancer women diagnosed ≤ 50 years of age. All these women underwent genetic counselling, gynaecology examination, transvaginal scan &CA 125 testing. Surgery was offered to women with BRCA mutations (RRSO) and also for hormone receptor positive women (surgical ovarian ablation). Histopathologies of these women were analysed using SEE-FIM protocol. Results: A total of 98 breast cancer women were identified. The median age was 41.5 years (SD 5.7) & median CA 125 was 9.3 (IQR 6–20.5). The genetic acceptance rate was 18%. Four patients were germline BRCA 1/2 positive, 1 VUS, 13 negative and 80 didn’t opt for test. Ultrasound imaging showed cystic ovaries among 12 patients. Most of them had benign features. Twenty two patients underwent surgery (bilateral salpingo-oophorectomy)—3 BRCA positive and rest for hormone positive breast cancer. Histopathologies were normal except for Krukenberg’s mets in one hormone positive breast cancer patient. Conclusion: The incidence of germline mutations in our study was 5%. STIC lesions were not detected in our study, attributing to low acceptance of genetic testing. The value of screening for ovarian cancer was limited. Incorporation of genetics into routine gynaecology oncology clinic needs further addressal. Our study opened the doors for the genetic awareness among these young women.

Scientists aim to improve crop response under stress conditions and gain better yields in continuously changing environmental conditions. They rely on plant phenotyping to quantify crop response under adverse conditions to achieve this goal and select the most tolerant genotypes. Recent advances in phenotyping platforms allow dissecting of complex traits such as abiotic stress. For example, the phenotyping platform is integrated with artificial intelligence (AI) and remote sensing tools to provide more robust, high throughput data collections in real-time changing environments. This review will give a deep understanding of the requirement of phenomics in crop improvement under stress conditions. We have discussed different phenotyping platforms, suitable traits for phenotyping, and machine learning and AI integration with the high throughput phenotypic platform for collecting a large data set of crops under stress conditions. Overall our review will dissect the phenomics aspects of complex traits, such as biotic and abiotic stress-related traits requiring sensor advancement, high-quality imagery combined with machine learning methods, and efforts in transdisciplinary science to foster integration across disciplines and better our understanding of plant stress biology.

Nowadays, trust management plays a significant role in different applications like commercial applications, Internet of Things (IoT) based applications, social networking applications, cloud computing-based applications, sensor networks, online services, and many more. Thus, it is an important research area for industry and engineering research communities. The concept of trust is mainly derived from long-term sociological and psychological human behaviour. It is used for decision-making purposes in different contexts and perspectives. Many growing research studies address the development of trust computation models using different approaches in different areas, such as cloud computing, P2P networks, online social network, etc. Still, most of the prior work does not provide a comprehensive systematic review of different trust management systems nor provide sufficient details about the computation process. In this paper, we have discussed a systematic review of the existing trust management systems by indicating the limitation of previous works. A systematic review started with research question identification, followed by searching and filtration of relevant works, and ended with a comparison and final report. A detailed comparative study of trust computational models will help understand the security and privacy aspects of trust. This survey provides a detailed study of each trust taxonomy and computation model in different online computing environments. The survey result finds several trust computation techniques. The results also say that trust in the online computing environment provides greater security and protection against several threats and attacks. Despite the potential benefits of trust, the results also included challenges and open research issues that are still opportunities for new researchers.

In this study, soy protein isolate and modified starch were evaluated as coat material for developing spray-dried flaxseed oil microcapsules. The emulsions were prepared by varying total solids (TS) and oil load (OL) using high shear mixer and then atomized in spray dryer for encapsulation of flaxseed oil. Emulsion with 30% TS and 30% OL had maximum zeta (ζ) potential value of − 36.83 mV, and emulsion having 25% OL and 30% TS had lowest mean droplet diameter of 701.80 nm. Emulsions exhibited shear thinning behaviour. Microencapsulation efficiency (ME), bulk density, tapped density and flowing properties of microcapsules were studied. ME of microcapsules varied from 63.79 to 95.84%. The microcapsule prepared with 30% OL and 30% TS owing to more oil load and zeta potential was selected for evaluation of certain characteristics. Microcapsules were spherical in shape with average particle size of 37.917 µm. Alpha-linolenic acid (18:3) content was 61.67% in the oil extracted from microcapsule. Fourier transform infrared spectra also confirmed the encapsulation of flaxseed oil by selected coat materials. 38.24% oil was released in simulated gastrointestinal conditions from the flaxseed oil microcapsules, and it increased to 60.86% when accompanied with heating prior to digestion.

Xylooligosaccharides (XOS) hold industrial importance owing to their prebiotic, antioxidant, antibiotic, and anti-inflammatory potential. The fungal enzyme xylanase (Talaromyces thermophilusF1208) is known for its capacity to produce XOS by hydrolytic degradation. This study aims to achieve the maximum xylan yield through experiments by optimizing corn cob pyrolysate’s parameters. The xylan in the pyrolysis solution was converted into XOS using the ultrasonic-coupled enzymolysis method and optimizing its parameters. Optimizing pyrolysis conditions employing maleic acid concentration, pyrolysis temperature, and holding time resulted in a maximum xylan yield (20.04%) at 0.2%, 180 °C, and 30 min of the three parameters, respectively. The multivariate response surface methodology (RSM) for pyrolysis statistically validated the results and suggested pyrolysis temperature and holding time to be the most crucial parameters influencing the yield. Similarly, the highest yield of XOS (21.75%) was obtained at 7.5 pH, 4-h reaction time, and 6-U/mL enzyme dosage for the enzyme hydrolysis alone by selecting these three as optimization parameters. In the case of enzyme hydrolysis coupled to ultrasonication, the maximum yield of 27% XOS was achieved at 190 °C pretreatment temperature, 30-min ultrasonication time, 55 °C ultrasonication temperature, and 0.31-W/cm2ultrasonication frequency, which was ~25% higher than that obtained using enzymatic hydrolysis alone. Moreover, ultrasonic intervention shortened the reaction time to 12.5% of the original time. The difference in enzymatic hydrolysis of xylan components (H1–H5) after alcohol precipitation was also investigated. It was found that the H2and H3substrates were slow reaction xylans, which were not easy to be enzymatically hydrolyzed.

Objectives. Gemstone polishers suffer from musculoskeletal problems due to constrained working postures, substantially influenced by the poor design of conventional gemstone polishing workstations. The present study investigated the effects of three workstation adjustment parameters (illumination at workstation, polishing height, tool post position) on postural angle, muscle activity and perceived postural discomfort in order to develop recommendations for new gemstone polishing workstations. Methods.Twelve professional gemstone polishers performed the polishing task on a prototype test-rig in 27 different test conditions. Taguchi’s L27orthogonal array was employed to estimate the optimum setting for gemstone polishing workstation design. Results. Study variables had a significant influence on postural angles. Polishing height substantially influenced muscle activity and perceived discomfort in the shoulder and lower back region. Tool post position was found to be the determinant factor for forearm muscle activity and perceived discomfort in the wrist/forearm region. Based on the findings, illumination of 1500 lux, polishing at 15 cm above the elbow height and a 20° tool position was recommended. Conclusions.Our findings present an alternative approach to using Taguchi’s design of experiment for workstation improvement, which has received very little attention in ergonomics studies. A confirmation test was conducted to validate the study recommendations.

ABSTRACTPrebiotics as a part of dietary nutrition can play a crucial role in structuring the composition and metabolic function of intestinal microbiota and can thus help in managing a clinical scenario by preventing diseases and/or improving health. Among the different prebiotics, non-digestible carbohydrates are molecules that selectively enrich a typical class of bacteria with probiotic potential. This review summarizes the current knowledge about the different aspects of prebiotics, such as its production, characterization and purification by various techniques, and its link to novel product development at an industrial scale for wide-scale use in diverse range of health management applications. Furthermore, the path to effective valorization of agricultural residues in prebiotic production has been elucidated. This review also discusses the recent developments in application of genomic tools in the area of prebiotics for providing new insights into the taxonomic characterization of gut microorganisms, and exploring their functional metabolic pathways for enzyme synthesis. However, the information regarding the cumulative effect of prebiotics with beneficial bacteria, their colonization and its direct influence through altered metabolic profile is still getting established. The future of this area lies in the designing of clinical condition specific functional foods taking into consideration the host genotypes, thus facilitating the creation of balanced and required metabolome and enabling to maintain the healthy status of the host.

AbstractCross-reactive and broadly neutralizing antibodies against surface proteins of diverse strains of rapidly evolving viral pathogens like SARS-CoV-2 can prevent infection and therefore are crucial for the development of effective universal vaccines. While antibodies typically incorporate mutations in their complementarity determining regions during affinity maturation, mutations in the framework regions have been reported as players in determining properties of broadly neutralizing antibodies against HIV and the Influenza virus. We propose an increase in the cross-reactive potential of CR3022 against the emerging SARS- CoV-2 variants of concern through enhanced conformational flexibility. In this study, we use molecular dynamics simulations, in silicomutagenesis, structural modeling, and docking to explore the role of light chain FWR mutations in CR3022, a SARS-CoV anti-spike (S)-protein antibody cross-reactive to the S-protein receptor binding domain of SARS-CoV-2. Our study shows that single substitutions in the light chain framework region of CR3022 with conserved epitopes across SARS-CoV strains allow targeting of diverse antibody epitope footprints that align with the epitopes of recently-categorized neutralizing antibody classes while enabling binding to more than one strain of SARS-CoV-2. Our study has implications for rapid and evolution-based engineering of broadly neutralizing antibodies and reaffirms the role of framework mutations in effective change of antibody orientation and conformation via improved flexibility.Communicated by Ramaswamy H. Sarma

The images generated by smart cameras and sensors in an Industrial Internet of Things (IIoT) ecosystem are at significant risk when transmitted over a public network due to the dynamicity and open nature of the IIoT environment. Encryption is a viable method for safeguarding IIoT digital images. This article addresses an image security framework using chaotic maps and DNA cryptography. The proposed algorithm uses a tent, circle, Chebyshev, and 3-D logistic map in a multilevel fashion to generate three keys. These keys are used for row-column rotation of subblocks, determine the rule to perform DNA encoding–decoding on the subblocks, and generate a key image on which DNA XOR operation is performed to get the encrypted image. The proposed scheme’s result analysis illustrates that the average NPCR (99.6566%), UACI (33.4588%), Entropy (7.9971), and larger key-space of $ 10^{195}$ are better than the existing schemes and are resistant to different attacks.

This paper presents the novel smart hybrid EfficientNet-deep autoencoder (EF-DA) Deep Neural Network model to classify brain images. This is the succession of modified EfficientNetB0 with a deep autoencoder to detect tumours. Initially, the feature extraction is done by modified EfficientNet, and then classification is done by the proposed smart deep autoencoder. The images are filtered, cropped by morphological operations, and augmented to train a deep hybrid EF-DA model in the first stage. In the second stage, a modified deep autoencoder is used for classification. The statistical result analysis of the hybrid model is assessed using seven types of degree metrics like F-score, precision, recall, specificity, Kappa score, accuracy, and area under the ROC curve (AUC) score. It is compared with three types of pre-trained models like MobileNet, MobileNetV2, and ResNet50 for analysis. The EF-DA model has achieved an overall accuracy of 99.34% and an AUC score of 99.95%.

Silver nanoparticles (AgNPs) synthesized through the water extract of Trachyspermum ammi, Piper nigrumand Syzygium aromaticumhad showed surface plasmon resonance in the range of 380 nm to 450 nm in UV–Vis spectra. Average size of these AgNPs was between 10 and 40 nm as observed in transmission electron microscopy image (TEM). High concentration of silver nitrate as a precursor showed an increase in the particle size, whereas the use of aqueous extract of Syzygium aromaticumexhibited high yield of AgNPs. Syzygium aromaticumsynthesized AgNPs showed relatively increase in the oxidation peak current. The anticancer effect of these nanoparticles was investigated against HepG2 cells, and lowest IC50value was observed in Syzygium aromaticumsynthesized AgNPs as compared to the other two spices. Further, double staining by Hoechst 33,342/ PI dyes and DCFH-DA assay data unveiled that these nanoparticles induce apoptosis due to the generation of intracellular reactive oxygen species (ROS). The enhanced anticancer activities of Syzygium aromaticumsynthesized AgNPs and corresponding increase in ROS. This may revealed that AgNPs with high redox current show the high anticancer properties.

The periodic colored polymer bands have been investigated in frontal polymerization (FP) reaction containing methacrylic acid (MAA), hydroquinone, Benzoyl peroxide (BP), and N, N Dimethyl aniline (DMA) system. The MAA acts as a monomer, which was diluted adequately with methanol (MeOH), ethanol (EtOH), and butanol (BuOH) separately to observe the patterning characteristics in different reaction schemes. The structural modulations that include the periodicity of colored polymer bands, morphological changes, and spacings between two adjacent layers were studied. The colored variations of polymer bands and their dependency on the concentration of BP, hydroquinone and alcoholic compositions have been studied. The highly consistent pink-white colored polymer bands, loosely-packed band structures, and perfectly ordered band structures resulted in MeOH, EtOH, and BuOH systems, respectively, as discussed. The evolution of tiny bubbles and convection-type instability has been observed in different conditions of the FP reactions that significantly affect the planar movement of the polymer fronts. The hot spots, which usually represent the high-temperature regions of a typical frontal surface, have also been demonstrated in all reacting systems, which may cause spin mode propagation of the fronts and change the surface geometry, as described. The materials characterization was carried out using UV–visible spectrophotometer, NMR, FTIR, and FESEM techniques, providing information about the polymer phases involved in band structures, composition, and surface properties. The analytical data and results obtained during the study further emphasized that two different colored polymers, namely 1, 4-dihydroxy anthraquinone methacrylic acid and poly-methacrylic acid dihydroxy, anthraquinone produced simultaneously and crystallized periodically, results in the development of a colored polymer band structures. The possible reactions and associated chemical mechanisms concerning the observations, the nature of the monomer, and solvent characteristics have been proposed, which show a close agreement with the analytical data and results obtained during the study.