jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sonal B. Nikam | Sudhir B. Lande | Vinay J. Nagalkar | G.C. Wakchaure | P. Suresh Kumar
Source: Journal of Food Processing and Preservation (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Predictive Classification Model for Quality Grading and Maturity Detection of Dragon Fruit Using Fused Deep CNN Feature and Ensemble Learning Introduction: Dragon fruit quality grading and maturity detection are crucial for ensuring market value and consumer satisfaction. The manual grading and maturity assessment of dragon fruit is a tedious, time‐consuming task that often lacks consistency due to human subjectivity and environmental factors. Existing computer vision–based classification models rely on single deep learning architectures, which may not effectively capture the diverse features required for accurate quality assessment. Moreover, traditional machine learning classifiers struggle to generalize well when dealing with complex agricultural datasets containing variations in fruit texture, size, and lighting conditions. Method: This paper presents a predictive classification model that integrates fused deep convolutional neural network (CNN) features with ensemble learning to achieve high‐accuracy classification. The proposed model extracts deep features from multiple pretrained CNN architectures, including VGG16 and Inception‐ResNet‐v2, and fuses them to enhance feature representation. These fused deep features were then classified using an ensemble of machine learning algorithms, including random forest (RF), gradient boosting (GB), and AdaBoost (AB). Findings: Experimental results on a benchmark dataset of dragon fruit images demonstrated that the proposed hybrid approach outperformed individual deep learning and machine learning models, achieving superior accuracy, precision, and recall in quality and maturity grade classification. RF achieved 99.99% accuracy in quality grading by classifying fresh versus defected fruits, while AB reached 99.69% accuracy in maturity detection, surpassing the performance of both RF and GB. Conclusions: The model effectively distinguished different maturity levels and quality grades, offering a robust and automated solution for dragon fruit quality assessment. Its high accuracy and adaptability support real‐world deployment in smart farming and processing units, including in automated sorting and grading systems. This enables real‐time, consistent, and accurate fruit classification, thereby reducing labor, minimizing human error, and improving supply chain efficiency. Random forest ; Deep learning ; Ensemble learning ; Convolutional neural network ; AdaBoost ; Support vector machine ; Ensemble forecasting ; Grading (engineering) ; Boosting (machine learning) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing and Land Use
Authors: Zhiqiang Zhong | Chun Zhu
Source: JOURNAL OF MEDICINES DEVELOPMENT SCIENCES (article, 2025-05-28)
Field: Chemistry — Spectroscopy

The Critical Role of Impurity Analysis in Quality Control During Drug Testing and Its Optimization Paths Impurity analysis in drug testing plays a crucial role. It can identify and control harmful impurities, ensuring drug purity, stability, and safety while guaranteeing efficacy. Currently, various techniques and methods are employed for impurity analysis, including chromatographic techniques such as ion chromatography, gas chromatography, and high-performance liquid chromatography, as well as mass spectrometry techniques like liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry, and spectrophotometric methods like UV-visible spectrophotometry. However, impurity analysis techniques face challenges, including the need to improve sensitivity and accuracy, overcome difficulties in analyzing complex sample impurities, and address method reproducibility issues. Optimizing impurity analysis involves introducing advanced techniques like ultra-high-performance liquid chromatography and chromatography-mass spectrometry, establishing standardized processes, strengthening personnel training, and utilizing artificial intelligence and automation technology. These efforts enhance drug quality control levels, ensuring safe and effective medications. Chromatography ; Mass spectrometry ; Impurity ; Chemistry ; Gas chromatography ; High-performance liquid chromatography Analytical Chemistry and Chromatography ; Pesticide Residue Analysis and Safety ; Metabolomics and Mass Spectrometry Studies
Authors: Mariantonietta Pizzella | Antonella Accardo | Flavia Anna Mercurio | Teresa Sibillano | Enrico Gallo | Giancarlo Morelli | Giovanni Smaldone | Cinzia Giannini | Marilisa Leone | Nicole Balasco | Carlo Diaferia | Luigi Vitagliano
Source: Nanoscale (article, 2025-01-01)
Field: Materials Science — Biomaterials

Structural and functional characterization of self-assembling fragments identified from the transthyretin amyloid-like structure The structural biology of amyloid-like systems has experienced significant advances due to the impressive technological and methodological advancements of experimental and computational techniques, providing unprecedented atomic-level details into the molecular architecture of these aggregates. Taking advantage of the availability of novel and complex amyloid-like protein structures, we evaluated the possibility of expanding the universe of self-assembling peptides by exploiting these structural data. We utilized transthyretin, a protein whose amyloid-like aggregation has significant pathological consequences but has never been employed to generate peptide-based materials, as a model system to develop a procedure for identifying novel self-assembling peptides. The pipeline we developed is based on the preliminary evaluation of the stability of the fragments through molecular dynamics simulations, experimental verification of the formation of cross-β assemblies in both solution and the solid state, and characterization of the functional properties of the generated biomaterial. In this framework, we demonstrate that selected transthyretin-based peptides have a strong tendency to self-assemble and form soft hydrogels. The characterization of these systems suggests that a mixture of these peptides tends to aggregate by co-assembly, mimicking the interactions that stabilize the amyloid-like structure of the parent protein. Our data emphasize the role that local structures play in the mechanical and optical properties of these assemblies. Transthyretin ; Characterization (materials science) ; Amyloid fibril ; Amyloid (mycology) ; Chemistry ; Materials science ; Biophysics ; Crystallography ; Nanotechnology ; Amyloid β ; Biology ; Medicine ; Pathology Supramolecular Self-Assembly in Materials ; Lipid Membrane Structure and Behavior ; Protein Structure and Dynamics
Authors: Shichen Li | Chenhui Shao
Source: Manufacturing Letters (article, 2025-08-01)
Field: Chemistry — Analytical Chemistry

Multi-modal data fusion for moisture content prediction in apple drying Fruit drying is widely used in food manufacturing to reduce product moisture, ensure product safety, and extend product shelf life. Accurately predicting final moisture content (MC) is critically needed for quality control of drying processes. State-of-the-art methods can build deterministic relationships between process parameters and MC, but cannot adequately account for inherent process variabilities that are ubiquitous in fruit drying. To address this gap, this paper presents a novel multi-modal data fusion framework to effectively fuse two modalities of data: tabular data (process parameters) and high-dimensional image data (images of dried apple slices) to enable accurate MC prediction. The proposed modeling architecture permits flexible adjustment of information portion from tabular and image data modalities. Experimental validation shows that the multi-modal approach improves predictive accuracy substantially compared to state-of-the-art methods. The proposed method reduces root-mean-squared errors by 19.3%, 24.2%, and 15.2% over tabular-only, image-only, and standard tabular-image fusion models, respectively. Furthermore, it is demonstrated that our method is robust in varied tabular-image ratios and capable of effectively capturing inherent small-scale process variabilities. The proposed framework is extensible to a variety of other drying technologies. Water content ; Modal ; Fusion ; Content (measure theory) ; Sensor fusion ; Computer science ; Environmental science ; Process engineering ; Artificial intelligence ; Materials science ; Mathematics ; Engineering ; Composite material ; Geotechnical engineering Spectroscopy and Chemometric Analyses ; Food Supply Chain Traceability ; Wireless Sensor Networks and IoT
Authors: Renne Leini | Jonas N. Kapp | Kari Kopra | Tatu Pantsar
Source: Scientific Reports (article, 2025-07-10)
Field: Materials Science — Materials Chemistry

Binding modes of the KRAS(G12C) inhibitors GDC-6036 and LY3537982 revealed by all atom molecular dynamics simulations Over 100 co-crystal structures of KRAS switch-II pocket (SII-P) targeting inhibitors are currently available in the RCSB Protein Data Bank. These publicly available structures are invaluable tools that have led to a more in-depth understanding of KRAS SII-P pocket, which is crucial for targeted drug design efforts. The binding modes (co-crystal structures) of two KRAS(G12C) inhibitors that have advanced to the clinical trials, GDC-6036 (divarasib) and LY3537982 (olomorasib), are currently unavailable. Here, we reveal the putative binding modes of these two G12C inhibitors by utilizing the available structural data of analogous compounds and molecular dynamics (MD) simulations (total simulation time of 200 micros). Our biochemical assays confirm high affinity of both inhibitors for KRAS(G12C), with differential susceptibility to typical resistance associated co-mutations. A co-mutation at Tyr96 reduces the affinity of both inhibitors, whereas a mutation at His95 negatively impacts only GDC-6036. While both inhibitors display low activity towards KRAS(WT), LY3537982 maintains high activity against other RAS isoforms with G12C mutation. As the simulation predictions align with the experimental results, our findings indicate that microsecond timescale simulations can be a valuable tool for predicting binding modes. Molecular dynamics ; KRAS ; Dynamics (music) ; Atom (system on chip) ; Computer science ; Chemistry ; Bioinformatics ; Computational biology ; Biology ; Physics ; Mutation ; Computational chemistry ; Biochemistry ; Parallel computing ; Gene Enzyme Structure and Function ; Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics
Authors: Walid Abdullah
Source: Sustainable Machine Intelligence Journal (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Advanced Fruit Quality Assessment using Deep Learning and Transfer Learning Technique Ensuring the quality of fruits is essential for consumer satisfaction and food industry standards. Accurately identifying and classifying high-quality fruits is essential for maintaining good health and nutrition. The subjective and labor-intensive nature of traditional techniques of evaluating quality highlights the need for automated solutions. This study utilized transfer learning with Four pre-trained convolutional neural network (CNN) models—MobileNetV2, ResNet50, VGG16, and EfficientNetB0 —to detect the quality of five fruits: apple, banana, strawberry, orange, and mango. All models were trained and tested with a public labeled fruit images dataset, and their performance was evaluated in terms of accuracy, precision, recall, and F1-score. Our results demonstrate that ResNet50 consistently achieves the highest accuracy across all fruit types, surpassing MobileNetV2, EfficientNet and VGG16. Additionally, our models' performance is benchmarked against state-of-the-art techniques, underscoring the superior accuracy and reliability of ResNet50 in automated quality control systems within the agricultural and food sectors. Transfer of learning ; Quality (philosophy) ; Computer science ; Artificial intelligence ; Machine learning Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Smart Agriculture and AI
Authors: Yiming Zou | Xuan Liu | Liangwei Du | Jiafan Yang | Zhishuang Liu | Zicheng Yuan | Lina Liu | Bicheng Yang | Zhe Shao | Igor Popov | Bin Hu
Source: Rapid Communications in Mass Spectrometry (article, 2025-05-08)
Field: Chemistry — Spectroscopy

Onsite Analysis of Complex Samples Using Miniature Mass Spectrometry With Direct Sampling and Ionization Overall, our data showed that this direct sampling/ionization method using wooden tip or plant tissue coupled with mini-MS approach is simple, fast, and sensitive method for onsite direct analysis of raw samples in field environments. Chemistry ; DART ion source ; Ionization ; Analyte ; Mass spectrometry ; Sampling (signal processing) ; Analytical Chemistry (journal) ; Chromatography ; Ambient ionization ; Sample preparation ; Sample (material) ; Chemical ionization ; Electron ionization ; Computer science ; Ion Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Pesticide Residue Analysis and Safety
Authors: Akira K. Nakamura | Emily M. Fulk | Christopher W. Johnson | Farren J. Isaacs
Source: ACS Synthetic Biology (article, 2025-03-06)
Field: Materials Science — Biomaterials

Synthetic Genetic Elements Enable Rapid Characterization of Inorganic Carbon Uptake Systems in <i>Cupriavidus necator</i> H16 <i>Cupriavidus necator</i> H16 is a facultative chemolithotroph capable of using CO<sub>2</sub> as a carbon source, making it a promising organism for carbon-negative biomanufacturing of petroleum-based product alternatives. In contrast to model microbes, genetic engineering technologies are limited in <i>C. necator</i>, constraining its utility in basic and applied research. Here, we developed a genome engineering technology to efficiently mobilize, integrate, and express synthetic genetic elements (SGEs) in <i>C. necator</i>. We tested the chromosomal expression of four inducible promoters to optimize an engineered genetic landing pad for tunable gene expression. To demonstrate utility, we employed the SGE system to design, mobilize, and express eight heterologous inorganic carbon uptake pathways in <i>C. necator</i>. We demonstrated all inorganic carbon uptake systems' upregulated intracellular bicarbonate concentrations under heterotrophic conditions. This work establishes the utility of the SGE strategy for expedited integration and tunable expression of heterologous pathways, and enhances intracellular bicarbonate concentrations in <i>C. necator</i>. Cupriavidus necator ; Characterization (materials science) ; Carbon fibers ; Synthetic biology ; Carbon source ; Chemistry ; Biology ; Biotechnology ; Computational biology ; Bacteria ; Polyhydroxyalkanoates ; Biochemistry ; Nanotechnology ; Materials science ; Genetics biodegradable polymer synthesis and properties ; Microbial Fuel Cells and Bioremediation ; Biofuel production and bioconversion
Authors: Mbatha Abednigo Jabu | Alugongo AA | N Z Nkomo
Source: International Journal of Engineering Trends and Technology (review, 2025-01-31)
Field: Materials Science — Polymers and Plastics

A Review of the Potential Applications of Composites from Agricultural Waste The disposal of agricultural waste is a challenge in our environment; it keeps increasing with the increase in population and production. The automotive industry has used agricultural waste, such as macadamia shells, to fabricate automobile door panels. The use of agricultural waste in the automotive industry lessens pollution and promotes sustainability in the environment. This paper reviews how effectively agricultural waste composites can be used in the automotive industry, eliminating waste in the landfill. Agricultural waste composites have a lot of potential in the automotive industry since they are recyclable and biodegradable, which makes disposing of them extremely convenient and eco-friendly. Furthermore, using agricultural waste bio-composites lowers the cost of production of automotive parts. There is still a need for further research into various agricultural waste, which has not been explored in the automotive industry and other applications. Agriculture ; Agricultural waste ; Composite material ; Waste management ; Materials science ; Engineering ; Geography ; Archaeology Natural Fiber Reinforced Composites
Authors: Sridhar Raju | Hanieh Kargarzadeh | Andrzej Gałęski
Source: Nanomaterials (article, 2025-04-27)
Field: Materials Science — Polymers and Plastics

Preparation of Polylactide/Halloysite-Nanoclay/Polytetrafluoro-Ethylene Composite Foam and Study of Properties and Morphology Halloysite nanoclay (HNC) and as-polymerized polytetrafluoroethylene powder (PTFE) were introduced into biodegradable polylactic acid (PLA) via a melt mixing technique to enhance its mechanical, rheological properties and foaming ability. The synergetic effects of these fillers on the morphological, mechanical, thermal, and foaming properties of PLA were investigated. Results indicated that the tensile properties were improved in comparison to neat PLA. Differential Scanning Calorimetry (DSC) revealed a decrease in PLA crystallization time with increasing filler concentration, indicating a strong nucleating effect on PLA crystallization. Extensional flow tests showed that strain hardening in PLA composites is influenced by fillers, with PTFE particularly exhibiting a more pronounced effect, attributed to nanofibrillation and entanglement during melt processing. The addition of a dual-filler system improved the melt strength and viscosity of PLA, resulting in foams with decreased cell size and increased cell density. Materials science ; Polylactic acid ; Composite material ; Differential scanning calorimetry ; Ultimate tensile strength ; Crystallization ; Halloysite ; Composite number ; Rheology ; Chemical engineering ; Polymer Polymer Foaming and Composites ; biodegradable polymer synthesis and properties ; Polymer Nanocomposites and Properties
Authors: Filipp Nikitin | Ian Dunn | David Ryan Koes | Olexandr Isayev
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Biomaterials

GEOM-drugs revisited: toward more chemically accurate benchmarks for 3D molecule generation Deep generative models have shown significant promise in generating valid 3D molecular structures, with the GEOM-drugs dataset serving as a key benchmark. However, current evaluation protocols suffer from critical flaws, including incorrect valency definitions, bugs in bond order calculations, and reliance on force fields inconsistent with the reference data. In this work, we revisit GEOM-drugs and propose a corrected evaluation framework: we identify and fix issues in data preprocessing, construct chemically accurate valency tables, and introduce a GFN2-xTB-based geometry and energy benchmark. We retrain and re-evaluate several leading models under this framework, providing updated performance metrics and practical recommendations for future benchmarking. Our results underscore the need for chemically rigorous evaluation practices in 3D molecular generation. Our recommended evaluation methods and GEOM-drugs processing scripts are available at https://github.com/isayevlab/geom-drugs-3dgen-evaluation. Valency ; Construct (python library) ; Key (lock) ; Scripting language ; Energy (signal processing) ; Generative grammar Nanoparticle-Based Drug Delivery ; Molecular Junctions and Nanostructures ; Cancer therapeutics and mechanisms
Authors: Alexandra Jinga | Mărioara Abrudeanu | Vasile Radu | Alexandru Ionut Nitu | Livia Stoica | Denisa Toma | Mircea Ionuţ Petrescu
Source: Applied Sciences (article, 2025-01-15)
Field: Materials Science — Materials Chemistry

Evaluation of the Zirconium Hydride Morphology at the Flaws in the CANDU Pressure Tube Using a Novel Metric The Zr-2.5%Nb alloy used to manufacture the pressure tubes for the CANDU 600 power plant experiences continual corrosion when the reactor’s fuel channels are functioning normally. Analysis of the hydrogen accumulation circumstances, the creation of zirconium hydride platelets, evaluation of the platelets’ reorientation in the thermo–mechanical field at volumetric flaws, and determination of the hydrogen morphology are therefore significant goals of the worldwide research being conducted in this field. In this paper, pressure tube alloy samples with artificial V flaws were hydrided by the technique of electrolytic deposition of a hydrogen film layer, followed by a specific thermal treatment. Micrographs of the Zr-2.5%Nb alloy samples in the radial cross-section were taken to examine the hydride reorientation phenomenon that occurred when the samples were heated under mechanical stress. The complex stress field around a tip flaw promoted the formation of some circles from hydride filaments. To characterize the zirconium hydride morphology in the reorientation zone at the volumetric flaw tip, image analysis and processing techniques were applied. To better characterize the reorientation zone from the point of view of embrittlement and mechanical behavior, this paper proposes a novel metric for the morphology of hydrides located in the reorientation zone. This work’s findings are beneficial for the investigation of CANDU fuel channels’ structural integrity. Zirconium ; Materials science ; Hydride ; Tube (container) ; Nuclear engineering ; Metallurgy ; Engineering ; Composite material Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Fusion materials and technologies
Authors: J. Subramanian | RUTAMBHARA PADHY | Arun Joshi | Vivek Reddy Murthannagari | G N K Ganesh
Source: International Journal of Applied Pharmaceutics (article, 2025-09-07)
Field: Materials Science — Biomaterials

STIMULI-RESPONSIVE DRUG DELIVERY SYSTEMS: EXTENSIVE OVERVIEW Drug delivery systems based on stimuli-responsive materials offer a modern scientific solution to physicians who need drug release triggered by biological signals or outside influences. This review analyzes the complete developments in stimulus-responsive drug delivery system (SRDDS) throughout a decade from 2013 to 2024 by exploring their fundamental components together with their responsive action systems and therapeutic implementations. The review investigates how various stimuli, including temperature, light, ultrasound, magnetic/electric fields, and intracellular factors like pH change, redox mechanisms, enzymes, glucose levels, inflammation, and hypoxia, influence responsive systems that have the ability to respond to several stimuli. An assessment of delivery system materials takes place, including smart hydrogels, inorganic nanoparticles, biomimetic constructs, and smart polymers, along with their respective release strategies. Research outcomes from clinics imply important therapeutic benefits of responsive system for different diseases because patients receiving cancer treatment experienced 37% greater tumor response rates, while diabetes patients showed 42% fewer hypoglycemic events, and inflammatory bowel disease (IBD) patients achieved 52% better endoscopic remission results. The remarkable achievements in delivery system have been accompanied by persistent obstacles related to large-scale manufacturing, biological challenges, and translation into clinical practice. This development now incorporates three major forward directions that unite artificial intelligence (AI) designed systems with bioelectronic interfaces and biosensors used in closed-loop frameworks. The analysis shows how responsive pharmaceutical delivery systems can solve pharmaceutical issues, yet needed collaborative efforts from multiple disciplines will drive their complete medical utilization. Drug delivery ; Delivery system ; Site of action ; Drug ; Disease ; Targeted drug delivery ; Diabetes treatment ; Pharmaceutical sciences Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Advanced Drug Delivery Systems