JBCS

Bacillus subtilis TIM27, isolated from mangrove sediments, produced the BiosT27 biosurfactant and the ProT27 subtilisin, exhibiting protease and esterase activities. When tested together as detergent additives, they demonstrated excellent stain removal performance.

A highly efficient hypocrystalline V-P-C catalyst with a porous honeycomb structure is utilized for the continuous production of acrylic acid through glycerol dehydration.

ZnO and Ni-Al-Oxide associated with Ag prismatic nanoplatelets as efficient materials for the photocatalytic degradation of Remazol Black 5 and Methylene Blue.

A review of the functionalities of natural antioxidants with sustainable food packaging.

Representative process of atmospheric degradation study in a semiarid climate of pure poly (butylene adipate-co-terephthalate) films and those with orange oil additives using conventional and machine learning methods.

Phenol degradation was studied by advanced oxidative processes using different approaches and the maxima efficiency obtained was 99% after 120 min.

From chemical compounds to predicting the antitrypanosomal activity of new candidates against Trypanosoma cruzi trypomastigotes.

Machine learning models were used to predict the biological activity of natural products against Schistosoma mansoni. Virtual screening identified 14 promising compounds, which were further analyzed for absorption, distribution, metabolism, excretion and toxicity (ADMET) properties.

This paper advances sustainability by introducing an eco-friendly solvent for perovskite solar cell fabrication, reducing its environmental impact.

Mesoporous carbon-based materials from biomass can be produced through various synthetic methods and have diverse environmental applications.

This work paper the applications of Natural Language Processing (NLP) and Large Language Models (LLMs) in chemistry and materials science, highlighting their role in chemical entity recognition, reaction prediction, materials discovery, and literature analysis.

Development of a photodynamic inactivation-enhanced air filtration system using nonwoven polypropylene coated with electrospun nanofibers.

The evolution of environmental analysis using stir bar sorptive extraction (SBSE) highlights its growing applications in detecting environmental contaminants. Recent advancements emphasize its use in analytical chemistry, particularly with gas chromatography, for enhanced sensitivity and precision. Current research reviews the latest applications and discusses present capabilities while forecasting future trends, such as the development of advanced sorptive materials and integration with innovative analytical platforms, paving the way for more sustainable and efficient environmental monitoring solutions.

Mixed oxide synthesis from the recycling of notebook batteries and abrasive sludge for application in photocatalysis and pseudocapacitor.

The figure illustrates the core components of quantitative structure-activity relationship (QSAR)-Lit, a platform designed to streamline the QSAR modeling process. It encompasses data curation, descriptor calculation, machine learning, and virtual screening, enabling seamless and efficient analysis for drug discovery applications.

Synthesis, characterization, and evaluation of the immunomodulatory and anti-inflammatory activity of RhCl₃, Rh^III/beta cyclodextrin (βCD) and Rh^III/hydroxypropyl-beta-cyclodextrin (HPβCD).

The present work describes the use of chemotaxonomy to identify chemical patterns of diterpenes along four Euphorbiaceae genera and it may guide future research in systematics, phytochemistry, and pharmacology fields.

This study investigates the effects of pH, ammonium sulfate supplementation, and refrigeration on higher alcohols production in sugarcane molasses at a microdistillery scale, highlighting their impact on isoamyl alcohol production (A), isobutanol production (B), and the A/B ratio.

We report the synthesis of 5-arylidene barbituric acids under optimized conditions by condensation reaction between aromatic aldehydes and barbituric acid using ionic liquid catalyst. The arylidene barbiturates were tested for antimicrobial activity using two bacteria. Quantum chemical calculations were performed using the DFT/M062X/6-311++G(d,p) method to optimize the structure and principal component analysis indicated bioactivity.

The synthesis of regenerated cellulose sponge with silver nanoparticles (RCS@AgNPs) biocomposites via a hydrothermal method demonstrated cytocompatibility with fibroblasts, absence of mutagenicity, and antimicrobial activity against both Gram-positive and Gram-negative bacteria.

Sources of toxic elements in particulate matter (PM₁₀) and associated risks in educational centers.

A quantum active learning method (QAL) for automatic structural determination of doped materials has been developed and implemented in the QMLMaterial software. QAL uses quantum circuits for data encoding to create quantum machine learning models on-the-fly.

Design and synthesis of novel ergosterol peroxide derivatives as apoptotic inducers through mitochondria-related pathways.

Electrospun polyvinyl alcohol (PVA) nanofibrous mats with poly(amidoamine) (PAMAM) dendrimers and ibuprofen were developed and characterized. The nanofibrous mats demonstrated controlled drug release, which indicates their potential for advanced drug delivery applications.

Benzimidazole derivatives were identified as potential cruzain (Cz) inhibitors through molecular modelling.

This study employs supervised learning methods, including Partial Least Squares Discriminant Analysis (PLS-DA) with bootstrap resampling and Support Vector Machine (SVM) ensemble, to analyze biogenic volatile organic compounds (BVOCs) emissions in the Atlantic Forest, achieving high classification accuracy.

A model molecule tetradecanoic (myristic) acid was pyrolyzed under inert atmosphere in the presence of fresh and equilibrium Fluidized Catalytic Cracking (FCC) catalysts, containing two different levels of rare earth. Deoxygenated molecules were produced, potential precursors for green gasoline, kerosene and diesel production.

Silver-dichloroquinoline complexes have been prepared and their spectroscopic, structural and mycobactericidal properties against Mycobacterium tuberculosis H37Rv (ATCC 27294) have been determined.

A chemical feature-based pharmacophore model for cyclooxygenase-1 (COX-1) was developed to virtually screen nine commercial databases, yielding 807 potential hits. Following molecular docking, compounds with binding affinities between ‒11.27 and
‒10.47 kcal mol^-1 were identified, and four were selected for further analysis through molecular dynamics (MD) simulations, indicating their potential as lead compounds to modulate COX-1 activity in neuroinflammation.

Exploring the native fruits of Brazil, cagaita and mamacadela, to uncover their potential for diverse applications.

Novel functionalized microspheres with trimercaptotriazine (TMT) groups based on poly(styrene-co-divinylbenzene) (PS-DVB), with and without polystyrene brushes, were synthesized, characterized, and compared for TMT group content. The surface-initiated atom transfer radical polymerization (SI-ATRP) efficiency was compared under different surface initiations.

An analysis of the relationship between fatty acids, chlorophyll, and RGB (red, blue, green) channels to identify the adulteration of extra virgin olive oil with soybean oil.

Full voltammogram analysis through machine learning for enhanced detection in electrochemical immunosensors.

Molecules from the QM-symex database are converted to SMILES (simplified molecular input line entry system) and stored in a new QM-symex-modif dataset with their target properties. The data is processed and used in machine learning models to develop predictive models for photophysical properties.

The blood composition imbalance following coronavirus disease (COVID-19) causes a systematic change in impedance, which can be modelled by multivariate analysis.

Application of machine learning and computational tools to predict antileishmanial activity, emphasizing the synergy of computational and experimental methods in developing novel therapeutic agents.

This study utilizes Quantitative Structure-Activity Relationship (QSAR)-based machine learning models, validated with bioactivity data median inhibitory concentration (IC₅₀) of compounds against Trypanosoma brucei and Trypanosoma cruzi, for screening Food and Drug Administration (FDA)-approved compounds as candidates for repurposing in the treatment of both trypanosomiases.

De novo design of a new lead compound with potential inhibitory effect on monkeypox virus F13 protein (VP37) by deep reinforcement learning and structure-based drug design.

Machine learning combined with virtual screening has enabled the discovery of a significant variety of molecules exhibiting high affinity with shikimate kinase. Subsequent evaluation through molecular dynamics and free energy calculations has facilitated the identification of potential inhibitor candidates.

Machine learning and multivariate statistical analyses identified molecular features correlated with biological activity of phenylpropanoid against Trypanosoma cruzi.