jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Akash Phillip | Tushar Chauhan | Santosh Kumar | Bagepalli Srinivas Ashok Kumar
Source: Journal of Emerging Science and Engineering (article, 2026-01-12)
Field: Materials Science — Biomaterials

From pond to polymer: A concise review on algae-derived plastics Algae-derived plastics signify a revolutionary advancement in the pursuit of sustainable materials, providing a renewable and environmentally benign substitute for conventional petroleum-based polymers. This review explores the most recent advancements in algae cultivation, polymerization methodologies, and synthetic biology that have elevated algae-based bioplastics to a position of prominence. It emphasizes the distinctive characteristics of algal biopolymers, their capacity to sequester carbon, and their various applications, encompassing packaging and 3D printing. Nonetheless, the pathway from aquatic environments to polymeric substances is laden with obstacles, including elevated production costs, challenges related to scalability, and regulatory impediments. Through the analysis of case studies, market dynamics, and burgeoning research, this paper highlights the pivotal importance of algae-derived plastics in fulfilling circular economy objectives and mitigating plastic pollution. The review culminates with an appeal for international collaboration, policy advocacy, and sustained investment in algae bioplastics to realize their complete potential as a fundamental element of sustainable development. Bioplastic ; Appeal ; Production (economics) ; Investment (military) ; Position (finance) ; Sustainable development ; Sustainable production biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Seaweed-derived Bioactive Compounds
Authors: Amey Kulkarni | Sejal Pathrabe | Hans Gupta | Gajanan K. Birajdar | Sangita Chaudhari
Source: International Journal of Image Graphics and Signal Processing (article, 2026-04-03)
Field: Chemistry — Analytical Chemistry

Classification and Shelf Life Prediction of Bananas Using Thermal Imaging with Vision Transformer and Random Forest Classifying and predicting banana shelf life is vital for optimizing storage and distribution in agriculture. Traditional methods, relying on subjective visual inspection, are inconsistent and time-intensive. This study presents a new, non-destructive approach combining thermal imaging, and machine learning to classify naturally ripened and artificially ripened bananas and forecast their shelf life. Preprocessed thermal images are flattened, segmented into fixed-size patches, and then linearly projected into feature tokens. Position embeddings are incorporated to retain spatial information, and the sequence is processed by a Vision Transformer (ViT) encoder, which leverages self-attention mechanisms to model relationships between patches. The [CLS] token output is subsequently processed through fully connected layers for final classification, achieving 97.59% accuracy. Validation using t-SNE visualization demonstrated clear class separability, and receiver operating characteristic (ROC) curves confirmed robust performance. With an MSE of 0.10, MAE of 0.18, and R2 score of 0.85, the random forest algorithm performed exceptionally well at predicting the shelf life of artificially ripened bananas. This approach offers significant advantages, including improved accuracy, reduced subjectivity, and efficiency in data processing. By integrating thermal imaging with advanced models, the proposed method enhances agricultural supply chain management and promotes precision in ripening classification and shelf life prediction. Random forest ; Off the shelf ; Transformer ; Thermal ; Visualization ; Feature (linguistics) Spectroscopy and Chemometric Analyses ; Food Drying and Modeling ; Smart Agriculture and AI
Authors: Maoyuan Yin | Daixin Liu | Hongyan Yang | Xiaowei Shi | Guan Xiong | Min Zhang | Tianyu Zhu | Lingling Chen | Ruihua Zhang | Xinqing Xiao
Source: Agriculture (article, 2026-03-12)
Field: Chemistry — Analytical Chemistry

Flexible Vis/NIR Wireless Sensing and Estimation with DeepEnsemble Learning for Pork The rapid chilling and aging stages following pork slaughter represent a critical window for determining final physicochemical quality and flavor development. To address the destructive nature of conventional meat quality assessment methods and the limitations of rigid spectral probes when applied to irregular biological surfaces, this study developed and validated a wireless monitoring system integrating a flexible visible/near-infrared (VIS/NIR) sensing array with ensemble learning algorithms. The proposed system enables non-destructive, continuous monitoring of pork quality during cold-chain storage. A DeepEnsemble regression model based on a stacking framework was constructed by integrating Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) to predict pH, moisture content, and total amino acid concentration. During a 26 h dynamic aging experiment, the proposed model achieved coefficients of determination (R2) of 0.9019, 0.9687, and 0.9600 for pH, moisture content, and total amino acids, respectively, with prediction performance exceeding that of individual regression models. The wireless transmission module maintained stable data communication under low-temperature and high-humidity conditions (−20 °C and 0–4 °C), with packet loss rates below 0.1%. These results indicate that the proposed system can effectively capture the dynamic evolution of pork quality during aging and provides a practical non-destructive approach for intelligent pork quality evaluation, cold-chain monitoring, and digital management of meat supply chains. Partial least squares regression ; Wireless ; Regression ; Boosting (machine learning) ; Stacking ; Regression analysis ; Support vector machine ; Linear regression Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Food Supply Chain Traceability
Authors: Sakshi K. Kamble | Anagha G. Harshe | Soniya C. Dhupdale | Shubham U. Dharwat | Soham P. Kapileshwar
Source: IJARCCE (article, 2026-03-29)
Field: Materials Science — Materials Chemistry

aLLoyM: “Phase Diagram Prediction System” Phase diagrams are essential thermodynamic tools that describe equilibrium phase stability as functions of temperature and composition in alloy systems.They guide alloy design, heat-treatment optimization, and microstructural control in critical industries.However, experimental determination of phase diagrams is costly and time-intensive.Although computational approaches such as CALPHAD (Calculation of Phase Diagrams) provide reliable thermodynamic modelling through Gibbs free energy minimization, they depend on curated parameter databases and expert assessment, limiting rapid exploration of new material systems.In this work, we introduce aLLoyM, a domain-adapted Large Language Model (LLM) developed for structured alloy phase diagram prediction.Thermodynamic data generated from CALPHAD assessments in the Computational Phase Diagram Database (CPDDB), covering 389 binary and 38 ternary systems, were systematically sampled to produce over 800,000 equilibrium data points.These data were transformed into multi-task Question-Answer (Q&A) pairs and used to finetune the Mistral-Nemo-Instruct model via Low-Rank Adaptation (LoRA), enabling efficient domain specialization.The framework supports three thermodynamic reasoning tasks: full phase information prediction, phase name inference, and inverse experimental condition prediction.Performance was evaluated under both interpolation and extrapolation settings to assess generalization.Results show substantial improvement over baseline LLM performance and demonstrate the model's ability to infer plausible phase behaviour for previously unseen systems.These findings highlight the potential of integrating Large Language Models with computational thermodynamics to develop scalable AI-assisted tools for accelerating alloy design and materials discovery. CALPHAD ; Extrapolation ; Phase diagram ; Phase (matter) ; Gibbs free energy ; Interpolation (computer graphics) ; Ternary plot Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Material Selection and Properties
Authors: Yinan Zeng | Jianhua Zhang
Source: Materials (article, 2026-03-17)
Field: Materials Science — Polymers and Plastics

Multifunctional Polymer Nanocomposites Polymer nanocomposite materials are a type of composite material that physically or covalently incorporates nanosized particles or nanostructures into a matrix of cross-linked polymer networks [...]. Nanocomposite ; Polymer ; Polymer nanocomposite ; Composite number ; Nanoparticle ; Nanostructure Polymer Nanocomposites and Properties ; Conducting polymers and applications ; Polymer crystallization and properties
Authors: Sharan Tej Reddy | Murali Mohan Ramakrishnappa | Vijay Kumar S | Vijee Kumar
Source: Advances in Science and Technology – Research Journal (article, 2026-02-10)
Field: Materials Science — Ceramics and Composites

Experimental investigations on analysis of variance analysis of hardness and thermal conductivity of AL7475 reinforced silicon nitride and graphite This study investigates the mechanical and tribological behavior of Al7475-based metal matrix composites (MMCs) reinforced with varying proportions of silicon nitride (SiN) and graphite (Gr), developed through stir casting.The present study highlights the beneficial impact of graphite and Si 3 N 4 reinforcement on the Al7475 wear performance to determine the hardness and thermal conductivity of the composites.This study examines Al7475based MMCs reinforced with 3-12% SiN and graphite via stir casting.The optimal 5% SiN-5% Gr hybrid composite achieved superior Hardness (74.1) and the Thermal conductivity for 5% SiN-5% Gr is 242.7 W/ (mC).Microstructural analysis revealed that the inclusion of SiN improved grain refinement and hardness, while graphite contributed to enhanced lubrication and wear resistance.SEM/EDS confirmed uniform dispersion, while fractography revealed a ductile-to-brittle transition with higher reinforcement.ANOVA (R > 89% for Thermal conductivity, > 98%) validated reinforcement significance, highlighting that optimal ratios enhance performance, whereas excess causes agglomeration and reduced ductility.The findings confirm that an optimal hybrid reinforcement ratio effectively enhances mechanical strength and thermal conductivity, while excessive reinforcement leads to agglomeration, reduced ductility, and inferior performance. Thermal conductivity ; Graphite ; Silicon nitride ; Thermal conductivity measurement ; Indentation hardness ; Thermal ; Silicon Advanced ceramic materials synthesis ; Aluminum Alloys Composites Properties ; Boron and Carbon Nanomaterials Research
Authors: Tae Gwan Park | Chun-Chieh Chang | Antoinette J. Taylor | Abul K. Azad | Hou-Tong Chen
Source: arXiv (Cornell University) (article, 2026-04-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Octave-Spanning Terahertz Quarter-Wave Plates Based on Over-Coupled Fabry-Pérot Resonances in Reflective Metal-Dielectric-Metal Metasurfaces Compact devices for broadband polarization control in the terahertz (THz) regime are challenging due to the intrinsic phase dispersion of birefringent materials and resonant structures. Here, we demonstrate high-performance broadband THz quarter-wave plates based on over-coupled metal-dielectric-metal reflective metasurfaces. The devices operate as single-port anisotropic Fabry-Pérot cavities in which the phase dispersion of over-coupled resonances is engineered to produce an approximately constant relative phase delay between orthogonal field components. By tailoring the metasurface geometry, efficient linear-to-circular polarization conversion is achieved while maintaining high reflectance. Four complementary metasurface designs, operated at an incidence angle of $45^\circ$, collectively cover the 0.25--3 THz frequency range accessible to a typical THz time-domain spectroscopy system. Each device exhibits an approximately octave-wide bandwidth with an axial ratio below 3\,dB and polarization conversion efficiencies exceeding 80\% across most of the operating band. Systematic optimization suppresses coupling to higher-order diffraction and surface wave modes, further extending the usable bandwidth while preserving the required phase relationship. The metasurfaces are compatible with wafer-scale fabrication, and experimental results show excellent agreement with simulations. These findings establish over-coupled reflective metasurfaces as a robust and versatile platform for broadband THz polarization control. Terahertz radiation ; Broadband ; Birefringence ; Polarization (electrochemistry) ; Metamaterial ; Bandwidth (computing) ; Orthogonal polarization spectral imaging ; Anisotropy ; Phase response ; Group delay and phase delay Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Terahertz technology and applications
Authors: Oleg Kornev | Aleksandr Shuvalov | Anna V. Kornilova | Valentin Alexeyevich Ermakov
Source: Structural Mechanics of Engineering Constructions and Buildings (article, 2026-04-03)
Field: Materials Science — General Materials Science

Integral Criterion for the Selection of Aluminum Alloy for the Construction of Reservoirs in the Arctic The purpose of the study is to develop an integral criterion for choosing an aluminum alloy for tank construction at low temperatures. In the course of the work, experimental studies of four aluminum alloys 1915T, 6082-T6, AD35T1, 1565ch were carried out in accordance with Russian standards. Standard mathematical statistics algorithms were used to process the test results: calculating sample characteristics, checking samples for the normality of the distribution, and eliminating gross measurement errors. Groups of characteristics that affect the effectiveness of the alloy are identified: standard mechanical properties, impact strength, crack resistance characteristics, fatigue characteristics, corrosion resistance, cost and weight characteristics. The expert surveys conducted during the study allowed to determine the weighting coefficients both within the groups and when forming the integral criterion. It is shown that fatigue characteristics and crack resistance characteristics have the greatest weight, which indicates the need to include the calculation of fatigue and crack resistance parameters in regulatory documents for the design of aluminum alloy tanks. The importance of increasing the fatigue characteristics and crack resistance of aluminum alloys for use in Arctic conditions should be taken into account when designing new alloys and thermal treatments of existing ones. Of the alloys considered in the study, alloy 1915T has the best integral index. This alloy has significantly higher fatigue characteristics and impact strength compared to the other alloys studied. The AD35T1 alloy has the worst integral index, which indirectly confirms the advantages of natural aging of aluminum alloys. Alloy 1565ch has the best cost and weight characteristics. Further research suggests expanding the indicators included in the proposed integral criterion by introducing weldability indicators. Alloy ; Aluminium ; Selection (genetic algorithm) ; Corrosion ; Material selection ; Weighting ; Fracture mechanics ; Paris' law Material Properties and Applications ; Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability
Authors: Mevlüt Akçura | İrem Poyraz
Source: Food Analytical Methods (article, 2026-03-18)
Field: Chemistry — Analytical Chemistry

Classification of Proso Millet (Panicum miliaceum L.) Seeds Based on Physical and Nutritional Approaches Through Machine Learning and Data Mining Algorithms Abstract The experimental population of this work was concerned with the classification of seeds of five proso millet ( Panicum miliaceum L.) genotypes (KY1, KY2, KY3, KY4, DS20), which were produced by single-plant selection using Turkish landraces. It was classified based on the morphometric and color features, which were obtained by using a scanner-based image-processing system, and the nutritional parameters obtained by near-infrared (NIR) spectroscopy, namely their suitability to human consumption. An equal image dataset was assembled, which had 500 seeds in each genotype. This was achieved by scanning the seeds at a resolution of 600 dpi of five technical replicates where each replicate had 100 seeds. To perform the nutritional analysis, 100 spectral measurements of an approximate amount of 300 g of ground sample of each genotype were measured using a calibration model specifically based on cereals, and this was repeated five times. In the classification task, there were 12 different machine learning algorithms (CART, CHAID, J48, kNN, LMT, MLP, NB, REPTree, RF, RT, SL, SMO) carefully tested with tenfold stratified cross-validation. The findings clearly showed that the random forest (RF) algorithm showed the best and the most stable performances in all metrics true positive rate [TPR] = 0.950, F -measure = 0.950, Matthews correlation coefficient [MCC] = 0.938, receiver operating characteristic area under curve [ROC AUC] = 0.996, and precision-recall area under curve [PRC AUC] = 0.985. RF model was next followed by the CART and REPTree algorithms, whereas the kNN algorithm had relatively lower classification success. All these results support the notion that morphometric signatures and decision based on NIR-based compositional data have some synergy in proso millet that can be utilized to offer an applicable, repeatable, and cost-effective decision framework to address critical processes including the control of seed purity, quick verification of variety, and quality classification. Replicate ; Random forest ; Population ; Support vector machine ; Receiver operating characteristic ; Statistical classification ; Sample (material) ; Correlation coefficient Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Nico Domschke | Chen Wang | Richard Golnik | Aleš Charvát | Anatolii Spesyvyi | Thomas Gatter | Bernd Abel | Peter F. Stadler
Source: Research Square (preprint, 2026-02-13)
Field: Chemistry — Spectroscopy

Fragment Tree Reconciliation (FTR) for Space Applications Fragmentation (computing) ; Data structure ; Fragment (logic) ; Graph ; Tree (set theory) ; Mass spectrometry Mass Spectrometry Techniques and Applications ; Atmospheric chemistry and aerosols ; Light effects on plants
Authors: J. McGee | S. V. Dordevic
Source: ArXiv.org (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

Sparse Statistical Modeling in Condensed Matter Physics In this work we explore the possibility of using sparse statistical modeling in condensed matter physics. The procedure is employed to two well known problems: elemental superconductors and heavy fermions, and was shown that in most cases performs better than other AI methods, such as machine or deep learning. More importantly, sparse modeling has two major advantages over other methods: the ability to deal with small data sets and in particular its interpretabilty. Namely, sparse modeling can provide insight into the calculation process and allow the users to give physical interpretation of their results. We argue that many other problems in condensed matter physics would benefit from these properties of sparse statistical modeling. Process (computing) ; Interpretation (philosophy) ; Statistical model ; Work (physics) ; Sparse matrix ; Statistical analysis ; Statistical mechanics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Atara R Israel | Zachary Cohen | Nazifa Rahman | Anastasiia Vasylaki | Amelia K. Ryan | Syeda Rahman | Pratyusha Ghosh | Ryan M. Williams
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

High-throughput SWCNT NIR-II screening enables cell and <i>in vivo</i> applications Diagnostic sensor development and clinical translation lag, in part, due to a lack of rapid, high-throughput screening methodology. Optimization of high-throughput sensor development and deployment will likely help in expediting tools toward the clinic. To address this issue, we optimized high-throughput screening parameters using a near-infrared (NIR-II) plate reader attached to an external probe for <i>in vivo</i> testing. We assessed spectroscopy parameters to improve speed and precision in screening a single-walled carbon nanotube (SWCNT)-based optical sensor. To do so, we assessed the appropriate well plate specifications, including laser power, excitation wavelength, exposure time, and focal height parameters for SWCNT-based optical sensor development. We also used the plate reader to screen fluorescent SWCNT which were endocytosed by a macrophage cell line. We then performed NIR probe spectroscopy to assess SWCNT embedded within a methylcellulose hydrogel. Finally, we used the NIR probe to measure SWCNT center wavelength and intensity from live immunocompetent mice. We anticipate that this framework may be broadly applicable to the development of near infrared nanosensors with the potential for more rapid clinical diagnostic translation. Nanosensor ; Carbon nanotube ; Near-infrared spectroscopy ; Excitation wavelength ; Laser ; Spectroscopy ; Expediting ; Fluorescence Carbon Nanotubes in Composites ; Neuroscience and Neural Engineering ; Biosensors and Analytical Detection
Authors: Z M Li | Lidi Liu | Peng Zhang | Zhongjie Wen | Yu Jia | Xiaoyang Liao
Source: International Journal of Nanomedicine (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Cardiotoxicity Challenges in Nanomaterial: Risk Assessment, Mechanisms, and Mitigation Strategies The growing use of nanomaterials (NMs) in consumer, medical and industrial products raises significant concerns about human exposure and the risk of cardiovascular toxicity. This narrative review synthesizes three critical and interconnected aspects of nanomaterial-induced cardiotoxicity-risk assessment models, mechanisms, and mitigation strategies-with the overarching goal of advancing fundamental knowledge and supporting the development of safer NMs. A variety of assessment models are explored, ranging from traditional in vitro and in vivo systems to emerging organ-on-a-chip platforms. A tiered, decision-driven strategy for model selection is based on risk-stage, objective-orientation, evidence complementarity, and ethical optimization, with emphasis on the critical need to assess toxicity under pathological conditions. Key mechanisms include oxidative stress, mitochondrial dysfunction, inflammatory responses, disruption of ion homeostasis, and induction of cell death. The specific pathway is often dictated by the physicochemical properties of nanoparticle. Potential mitigation strategies include surface engineering, elemental substitution/doping, morphological design, the use of chelating agents/antioxidants, and adopting Safe-by-Design principles. Interdisciplinary collaboration is crucial during the developmental phase to balance the immense application potential of NMs with the imperative to address their associated toxicity challenges. Cardiotoxicity ; Risk assessment ; Narrative review ; Variety (cybernetics) ; Human health ; Risk management Nanoparticles: synthesis and applications ; Advanced Nanomaterials in Catalysis ; Graphene and Nanomaterials Applications