jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shoaib Younas | Farhan Ali | Ukasha Arqam | Xin Wang | Fatima Tariq | Asif Wali
Source: Food Analytical Methods (article, 2025-11-19)
Field: Chemistry — Analytical Chemistry

Comparison of FNN with Advanced Algorithms in Non-destructive Estimation of Water State of Mushroom During Thermal Processing Artificial neural network ; Mean squared error ; Chemometrics ; Feedforward neural network ; Correlation coefficient ; Stability (learning theory) ; Coefficient of determination ; Mushroom ; Backpropagation Spectroscopy and Chemometric Analyses ; Food Drying and Modeling ; Fungal Biology and Applications
Authors: Yifei Gao | Wenhao Zhao | Xuedong Wang | Anfu Yang | Yan Zhuo | Yajing Qu | Meiying Wang | Xiaoyu Wang | Jin Ma
Source: The Science of The Total Environment (article, 2025-10-25)
Field: Chemistry — Analytical Chemistry

A novel method for predicting bioconcentration factor in rice based on the quantitative ion character-activity relationship (QICAR) coupled with explainable machine learning Bioconcentration ; Pollutant ; Random forest ; Soil water ; Heavy metals Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Heavy Metals in Plants
Authors: Yukti Chandervanshi | Prabhat Kumar Mandal | Sakshi Tewari
Source: Plant Nano Biology (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

Next generation nanobioformulation: A fascinating field for smart and sustainable agriculture Agricultural sustainability is increasingly threatened by human activities, highlighting the urgent need for eco-friendly alternatives to current practices. While nanotechnology has introduced innovative solutions, the widespread use of chemically synthesized nanoparticles (NPs) raises long-term environmental concerns. The review highlights the coupling of biologically synthesized NPs with biological moieties, such as plant growth-promoting microorganisms (PGPMs), and embellishing them with carriers for designing next-generation nanobioformulations (NBFs). To the best of our knowledge, no inclusive review has compiled insights on the technological advancements for plant stimulation, stress mitigation, and pathogen suppression with respect to NBFs. This article fills these gaps and offer a novel and comprehensive insights into the subject of next-generation NBFs by combining the advantages of nanotechnology + bioformulation research. Additionally, integrating Artificial Intelligence (AI) for predictive analysis and real-time monitoring in NBFs research can significantly improve agronomic precision, leading to intelligent and eco-conscious farming approaches. Information generated from AI-powered sensors, drones, big data analytics, and machine learning (ML) algorithms can help researchers working in the area of NBFs to customize the products according to the requirements and mitigate existing constraints in agricultural systems. The advance solutions provided by commercially available nanobioproducts can enhance efficiency, ensure environmental safety, and enable targeted agricultural applications. • Emerging concept of nanobioformulation in agriculture. • Amalgamating the merits of bioformulations and nanoformulations in order to develop novel nanobioformulations. • Methodology of designing nanobioformulations is clearly explained. • Integrating artificial intelligence with nanobioformulation. Sustainability ; Field (mathematics) ; Agriculture ; Sustainable agriculture ; Precision agriculture ; Work (physics) Nanoparticles: synthesis and applications
Authors: Jia‐Wei Tang | Yong-Xuan Hong | Jie Chen | Yun-Yun Xie | Zhang-Wen Ma | Qinghua Liu | Liang Wang
Source: ACS Measurement Science Au (article, 2025-11-01)
Field: Chemistry — Analytical Chemistry

Ratio Quantification of Geranium and Rose Essential Oil Mixtures via Deep Learning Analysis of Complex Raman Spectra Due to their high economic value, essential oils (EOs) are increasingly subject to adulteration, posing significant challenges for quality assessment and control. Owing to its excellent molecular specificity, Raman spectroscopy has been widely employed for the analysis and evaluation of EO products. In this study, we collected a total of 2700 Raman spectra comprising rose essential oil (REO, <i>n</i> = 900), geranium essential oil (GEO, <i>n</i> = 900), and their mixtures (<i>n</i> = 900). We first constructed a conventional convolutional neural network to distinguish spectra corresponding to varying EO mixing ratios. Interpretability analysis revealed that spectral peaks at 800, 1000, 1002, 1028, 1200, 1378, and 1668 cm<sup>-1</sup>, which show notable intensity variations in the averaged spectra of REO and GEO, also played a critical role in model decision-making, indicating that these spectral features can serve as discriminative markers for different EO ratios. Subsequently, we developed a channel attention residual feature extraction network (CARFENet), which employs spectral capturing and spectral separation modules to deconstruct mixed EO spectra into their constituent pure components. CARFENet demonstrated robust performance on the validation set and yielded predicted spectra that closely resembled the true spectra in an external test set, with similarity indices exceeding 0.99. These findings indicate that CARFENet enables the effective quantitative analysis of pure EO components within mixed Raman spectra. Raman spectroscopy ; Interpretability ; Spectral line ; Residual ; Discriminative model ; Analytical Chemistry (journal) ; Essential oil ; Principal component analysis ; Convolutional neural network Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Edible Oils Quality and Analysis
Authors: Jittiwat Nithikarnjanatharn | Jetnipat Pimollukanakul | Wannisa Nutkhum | Kanchit Rongcha | Thongchai Arunchai | Panuput Satuwong
Source: Results in Engineering (article, 2025-11-27)
Field: Materials Science — Polymers and Plastics

Predicting the mechanical properties of polypropylene and recycled polypropylene by the application of sigmoid and ReLU functions in neural networks • An ANN model was developed to predict mechanical properties of PP/r-PP composites. • The model uses ReLU and Sigmoid activation functions for performance comparison. • Accurately predicts tensile strength, modulus, and elongation at break. • 2–5–5–5 ANN specified with layer-wise equations and loss function. • Rationale clarified for selecting ReLU and Sigmoid activation functions in this application. This research analyzed artificial neural network (ANN) models for predicting the mechanical properties of polypropylene (PP) composite plastics blended with recycled polypropylene (r-PP), comparing the efficacy of ReLU and Sigmoid activation functions under similar training conditions. The input variables consist of the composite plastic blending ratio and silane. The predicted results included 5 properties: tensile strength (σ T ), Young's modulus (E), elongation at break (e), impact resistance (F), and hardness (H shore D ). A dataset including 30 samples was divided into training, validation, and testing sets in a 70/15/15 ratio, using normalization and early stopping methods. The model's efficacy was determined using MSE, RMSE, and R². The test results indicated that the model using the Sigmoid function achieved better accuracy, with R² values of 0.95, 0.97, and 0.99 for σ T , E, and H shore D , respectively. The MSE was around 0.8, while the RMSE was below 1 unit, with R² values of 0.82 and 0.93 for σ T and E, respectively. In the conclusion, the choice of activation function profoundly influences the ability to predict of the ANN and influences the design of network models for improving the prediction efficiency of PP:r-PP material properties in the industrial field. Sigmoid function ; Ultimate tensile strength ; Polypropylene ; Artificial neural network ; Activation function ; Shore durometer ; Composite number ; Izod impact strength test Polymer Nanocomposites and Properties ; Polymer crystallization and properties ; Fiber-reinforced polymer composites
Authors: Can Polat | Hasan Kurban | Erchin Serpedin | Mustafa Kurban
Source:  (article, 2025-05-20)
Field: Materials Science — Materials Chemistry

Data-Efficient Hydrogen Adsorption Prediction in Copper Nanoclusters: A Computer Vision-Based Transfer Learning Approach This study investigates the size-dependent hydrogen $\left(\mathrm{H}_{2}\right)$ storage capacity of magic-sized copper nanoclusters (Cu NCs) by analyzing their structural and electronic properties along with formation energy. Density functional tight binding (DFTB) calculations were performed to determine the HOMO and LUMO energy levels, energy gap ($E_{g}$), Fermi level ($E_{f}$), and formation energy ($E_{F}$) for different $\mathbf{C u}$ NC sizes interacting with H<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>. The results reveal a strong correlation between cluster size and H<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> storage potential, with smaller clusters exhibiting more favorable adsorption characteristics. However, as the system size increases, the computational cost of DFT calculations rises significantly. To address this, an machine learning approach was employed using image-based representations of Cu NCs with transfer learning. This method enabled rapid predictions of electronic properties with DFT-level accuracy while significantly reducing computational time. The model converged in just a few epochs, capturing the size-dependent trends in H<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> adsorption. These findings highlight the potential of AI-driven techniques for accelerating material discovery and optimizing nanoscale hydrogen storage systems. Nanoclusters ; Copper ; Adsorption ; Computer science ; Transfer of learning ; Hydrogen ; Materials science ; Artificial intelligence ; Machine learning ; Chemistry ; Nanotechnology ; Metallurgy ; Physical chemistry Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Nanomaterials for catalytic reactions
Authors: Zheng Su | Ying Wu | Peng Wu | Danny X. Xiao
Source: Engineering Failure Analysis (article, 2025-05-03)
Field: Materials Science — Materials Chemistry

Investigation of mechanical property of polyethylene pipes with scratch defects under vehicle-road-pipe coupling Scratch ; Materials science ; Coupling (piping) ; Polyethylene ; Property (philosophy) ; Composite material ; Structural engineering ; Forensic engineering ; Engineering ; Automotive engineering Material Properties and Failure Mechanisms ; Industrial Engineering and Technologies ; Engineering Diagnostics and Reliability
Authors: Han Zheng | Guangfei Qu | Keheng Pan | Junyan Li | Yuchen Shi | Cheng Guo | Wei Du
Source: Journal of Water Process Engineering (review, 2025-12-04)
Field: Chemistry — Electrochemistry

Application of functional graphene composites in heavy metal ion monitoring: A review Graphene ; Electrochemistry ; Reliability (semiconductor) ; Electrochemical gas sensor ; Metal ions in aqueous solution ; Scalability ; Electrode ; Fabrication Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Nanopore and Nanochannel Transport Studies
Authors: Lingli Deng | Xiangyu Zhao | Xian Wei | Qihui Sun | Jie Xu
Source: Journal of Alloys and Compounds (article, 2025-11-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning-aided optimization of 3D-printed MWCNTs/NiFe/PETG osteospermum stepped-structure via full-wave simulation for broadband microwave absorption The microwave absorption materials with broadband and wide-angle absorption characteristics represent a crucial prospect for addressing electromagnetic pollution. This study employs CST Studio and machine learning model to predicate the broadband absorption performance and corresponding optimal structure parameter. The specific optimum structure, called bio-inspired Osteospermum stepped structure, fabricated by 3D printing technology. The stepped structure demonstrates exceptional isotropic behavior and broadband absorption performance: across the 2-40 GHz frequency range, it achieves an impressive EAB of 31.4 GHz (spanning 8.6 to 40 GHz), while maintaining outstanding absorption capability even at large oblique incidence angles up to 65°. Macroscopically, the step-like interfaces generate multiple reflected waves with varying phase differences, leading to phase cancellation interference and effective electromagnetic wave attenuation. Microscopically, the incorporation of multi-walled carbon nanotubes (MWCNTs) and NiFe particles into the PETG matrix introduces additional conduction loss and magnetic loss. Ultimately, combination of 3D printing and theoretical simulation present a novel approach to design and fabricate unique structures with simultaneous broadband and wide-angle performance. • MWCNTs/NiFe/PETG composite filaments with 3 wt% MWCNTs loading demonstrated optimal microwave absorption performance. • A bio-inspired, 3D-printable periodic stepped absorber was designed based on the Osteospermum structure. • The metastructure's geometric parameters were optimized using an XGBoost-Bayesian Optimization framework. • An EAB of 31.4 GHz (8.6–40 GHz) was achieved across 2–40 GHz, with excellent microwave absorption maintained even at 65° incidence. Microwave ; Broadband ; Absorption (acoustics) ; Reflection loss ; Electromagnetic radiation ; Bandwidth (computing) ; Isotropy Electromagnetic wave absorption materials ; Metamaterials and Metasurfaces Applications ; Aluminum Alloys Composites Properties
Authors: Sourav Ghosh | Ashwath Sibi | Tiju Thomas
Source: Journal of Power Sources (article, 2025-12-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning and explainable artificial intelligence reveal physical insights into biomass-derived carbon for high-performance supercapacitors Supercapacitor ; Random forest ; Pyrolysis ; Capacitance ; Carbon fibers ; Stability (learning theory) ; Work (physics) Supercapacitor Materials and Fabrication ; Catalysis for Biomass Conversion ; Electrocatalysts for Energy Conversion
Authors: Cunliang Pan | Chengxin Qiao | Shi Feng | Yunyao Li | Yonggang Zheng | Hongfei Ye
Source: Colloids and Surfaces A Physicochemical and Engineering Aspects (article, 2025-11-20)
Field: Materials Science — Surfaces, Coatings and Films

Robust measurement of surface tension and contact angle using physics-informed autoencoders Contact angle ; Surface tension ; Sessile drop technique ; Curvature ; Surface roughness ; Drop (telecommunication) ; Surface finish ; Sandpaper Surface Modification and Superhydrophobicity ; Nanomaterials and Printing Technologies ; Fluid Dynamics and Thin Films
Authors: Yutao Li | Xiaoqi Zhang | Xin Jin | Berend Smit
Source: ChemRxiv (article, 2025-10-09)
Field: Chemistry — Inorganic Chemistry

Generalized Machine Learning Potentials for Water Adsorption in Al-Based Metal–Organic Frameworks Considering Framework Flexibility Metal–organic frameworks (MOFs) are promising materials for gas adsorption and separation owing to their tunable structures and large capacities. Given the ubiquity of water vapor in the environment, predicting water adsorption within MOFs is critical, but the classical force fields, such as the UFF, often fail. To overcome this problem, we propose a strategy to fine-tune a machine learning potential (MLP) from the pretrained MACE model for water adsorption within MOFs. We test our strategy on a dataset of 420 Al-MOFs with 439 distinct linkers. The fine-tuned model can predict heats of adsorption and Henry coefficients of six well-known Al-MOFs, in good agreement with experiments. We also show that these accurate predictions require not only the accurate description of MOF-H2O interactions but also the framework flexibility, which are missing in previous screening studies. From a case study on MIL-160, we established the accuracy thresholds of 8 kJ/mol on total energies and 50 meV/Å on atomic forces for heat of adsorption calculations, and our final model achieves it on 409 Al-MOFs. Using this model, we can escape the local minimum and obtain optimal configurations with significantly lower DFT-energy than DFT-optimized ones. To identify optimal MOFs for water harvesting, we also applied this model to calculate DFT-level heat of adsorption. Compared with these DFT-level results, UFF underestimates the heat of adsorption by more than 3.5 kJ/mol in 92.4\% of MOFs. Thus, our work demonstrates that the MLP can provide DFT-level predictions for high-throughput screening studies. Adsorption ; Flexibility (engineering) ; Work (physics) ; Water vapor ; Water model ; Test data Metal-Organic Frameworks: Synthesis and Applications
Authors: Yue Tang | Na Sui | Wenliang Xiong | Kun Huang
Source: Process Safety and Environmental Protection (article, 2025-02-20)
Field: Chemistry — Spectroscopy

A novel approach for predicting the separation in column chromatography elution using microchannel countercurrent reverse extraction: Essential correlation of confined mass transfer kinetics Countercurrent exchange ; Chromatography ; Elution ; Microchannel ; Chemistry ; Extraction (chemistry) ; Column (typography) ; Mass transfer ; Kinetics ; Materials science ; Thermodynamics ; Mathematics ; Nanotechnology Analytical Chemistry and Chromatography ; Chromatography in Natural Products ; Microfluidic and Capillary Electrophoresis Applications
Authors: Alsha Subash | Vaishnavi Gajare | Minoo Naebe | Balasubramanian Kandasubramanian
Source: ChemistrySelect (article, 2025-09-26)
Field: Materials Science — Materials Chemistry

Experimental and Machine Learning Investigation of Poly‐ε‐caprolactone‐MXene Composites for Methylene Blue Capture Abstract The upcycling of polymeric waste into high‐performance adsorbents presents a sustainable strategy for wastewater remediation. In this study, a nanofibrous adsorbent was fabricated via electrospinning using recovered poly(ε‐caprolactone) (PCL) waste sourced from 3D printing residues. To enhance adsorption performance, Ti 3 C 2 T x MXene and a microwave‐assisted ionic liquid‐functionalized composite (MW‐Ti 3 C 2 T x ‐IL) were incorporated, respectively, resulting in the development of 3PM and 3PMIL. MW‐assisted functionalization significantly improved methylene blue (MB) adsorption, with 3PMIL demonstrating an adsorption capacity of 360 ± 2.65 mg/g, which substantially surpassed that of pristine PCL (105.81 ± 0.013 mg/g) and 3PM (291.93 ± 1.57 mg/g). Adsorption kinetics followed a pseudo‐second‐order model, while the Langmuir isotherm indicated monolayer chemisorption, attributed to electrostatic interactions and enhanced surface functionalities. A machine learning (ML) model, trained using bootstrapped datasets, accurately predicts adsorption performance (R 2 = 0.9), with predictions closely aligning with experimental values. Correlation analysis reveals that m/v ratio, dosage, pH, and dye concentration are key descriptors influencing adsorption behavior. Structural and surface analyses confirm successful MXene incorporation and functionalization, contributing to enhanced adsorption. The nanofibers retain biodegradability in simulated environments, highlighting their environmental compatibility. This work presents a scalable, data‐driven strategy for designing biodegradable, high‐performance adsorbents from plastic waste, advancing sustainable solutions for wastewater treatment. Adsorption ; Methylene blue ; Monolayer ; Electrospinning ; Wastewater ; Langmuir adsorption model ; Surface modification ; Nanofiber MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Advanced Photocatalysis Techniques
Authors: Xiaotian Zhang | Yue Shang | Entao Yang | Ge Zhang
Source: ArXiv.org (preprint, 2025-05-16)
Field: Materials Science — Materials Chemistry

Is Grokking a Computational Glass Relaxation? Understanding neural network's (NN) generalizability remains a central question in deep learning research. The special phenomenon of grokking, where NNs abruptly generalize long after the training performance reaches a near-perfect level, offers a unique window to investigate the underlying mechanisms of NNs' generalizability. Here we propose an interpretation for grokking by framing it as a computational glass relaxation: viewing NNs as a physical system where parameters are the degrees of freedom and train loss is the system energy, we find memorization process resembles a rapid cooling of liquid into non-equilibrium glassy state at low temperature and the later generalization is like a slow relaxation towards a more stable configuration. This mapping enables us to sample NNs' Boltzmann entropy (states of density) landscape as a function of training loss and test accuracy. Our experiments in transformers on arithmetic tasks suggests that there is NO entropy barrier in the memorization-to-generalization transition of grokking, challenging previous theory that defines grokking as a first-order phase transition. We identify a high-entropy advantage under grokking, an extension of prior work linking entropy to generalizability but much more significant. Inspired by grokking's far-from-equilibrium nature, we develop a toy optimizer WanD based on Wang-landau molecular dynamics, which can eliminate grokking without any constraints and find high-norm generalizing solutions. This provides strictly-defined counterexamples to theory attributing grokking solely to weight norm evolution towards the Goldilocks zone and also suggests new potential ways for optimizer design. Entropy (arrow of time) ; Physical system ; Computation ; Artificial neural network ; Generalizability theory ; Memorization ; Deep learning ; Goldilocks principle Machine Learning in Materials Science ; Stochastic Gradient Optimization Techniques ; Quantum many-body systems
Authors: Said A. Hassan | Sarah H. Abdelaal | Noha F. El Azab | Amira M. El-Kosasy
Source: Journal of Food Composition and Analysis (article, 2025-11-20)
Field: Chemistry — Analytical Chemistry

A new era in dietary supplement regulation: sustainable chemometric approach for quality control of four dietary ingredients in slimming preparations Chemometrics ; Dietary supplement ; Partial least squares regression ; Quality (philosophy) ; Generally recognized as safe ; Food products ; Control (management) Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Pesticide Residue Analysis and Safety
Authors: Mayur Chaudhary | Aswin kumar Anbalagan | Kai‐Wei Chuang | Sumayah Shakil Wani | Zi‐Liang Yang | Bo-Chao Huang | Shaham Quadir | Chieh-Ting Chen | Ruei‐Hong Cyu | Bushra Rehman | Ming‐Jin Liu | Ching‐Yu Chiang | Li–Chyong Chen | Kuei‐Hsien Chen | Peter V. Sushko | Chih‐Hao Lee | Ya‐Ping Chiu | Yu‐Lun Chueh
Source: Materials Today (article, 2025-02-27)
Field: Materials Science — Materials Chemistry

Phase transition in two-dimensional monolayer (1L)-molybdenum disulfide induced by atomic S-basal plane gliding via synchrotron X-ray monochromatic beam radiation for superior electronic performance Molybdenum disulfide ; Synchrotron radiation ; Monochromatic color ; Monolayer ; Materials science ; Molybdenum ; Basal plane ; Synchrotron ; Beam (structure) ; Phase (matter) ; X-ray ; Optics ; Atomic physics ; Optoelectronics ; Chemistry ; Crystallography ; Physics ; Nanotechnology ; Composite material ; Metallurgy ; Organic chemistry 2D Materials and Applications ; Perovskite Materials and Applications ; Graphene research and applications