jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Khalid Reggab | Houssam Eddine Hailouf | Kingsley Onyebuchi Obodo | Mireille Merlise. Megnidio-Tchoukouegno | Mohammed Benali Kanoun | S. Goumri-Said
Source: Advanced Physics Research (article, 2026-04-13)
Field: Materials Science — Materials Chemistry

A Hybrid Semi‐Inverse Variational and Machine Learning Approach for the Schrödinger Equation ABSTRACT This study introduces a hybrid analytical–machine learning framework for solving the Schrödinger equation with complex potentials. The semi‐inverse variational method is first used to generate highly accurate eigenfunctions and eigenenergies for both 1D radial potentials (Yukawa and Cornell) and a 2D coupled anharmonic oscillator. Based on these rigorous, physics‐consistent results, we train supervised machine learning models; including Random Forest and Neural Network regressors; to predict energy eigenvalues across wide parameter ranges. Both models achieve near‐perfect predictive accuracy (R 2 &gt; 0.999) with errors of only a few millielectronvolts, while preserving fundamental quantum‐mechanical trends. Feature importance analysis confirms that the quantum number n and potential strength parameters dominate the energy scaling, in agreement with theoretical expectations. By integrating variational physics with data‐driven emulation, this hybrid framework reduces computational cost by orders of magnitude; enabling rapid, high‐throughput exploration of quantum systems across dimensions. The approach not only accelerates parameter screening but also serves as a discovery tool, uncovering emergent scaling laws and critical confinement behavior in mixed potentials. This synergy between analytical rigor and machine learning efficiency opens new pathways for quantum simulation, materials design, and the discovery of novel quantum phenomena. Eigenfunction ; Artificial neural network ; Quantum ; Eigenvalues and eigenvectors ; Energy (signal processing) ; Supervised learning ; Feature (linguistics) ; Scaling Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: K K Joshi | Pavan Swamy Hiremath | Shivashankar Hiremath | D. V. Ghewade | H. M. Vishwanatha | Kiran Keshyagol
Source: Scientific Reports (article, 2026-03-06)
Field: Materials Science — Polymers and Plastics

Quantitative assessment of alkali and carbon nanotube reinforcement effects on the tensile reliability of sustainable sisal fiber bio-based epoxy composites The present study investigates a two-stage reinforcement strategy to enhance the tensile performance and reliability of sisal fiber-reinforced bio-based epoxy composites, aligning material development with sustainability-driven design principles. In the first stage, sisal fiber mats were treated with 4 wt% and 5 wt% NaOH to improve fiber-matrix interfacial efficiency, while in the second stage, multi-walled carbon nanotubes (MWCNTs) were incorporated into the epoxy matrix at low weight fractions of 0.15, 0.25, and 0.35 wt% using a combined mechanical stirring and ultrasonication approach. Tensile testing conducted in accordance with ASTM D3039 revealed a systematic increase in ultimate tensile strength (UTS) from 71.24 MPa for untreated composites to 103.32 MPa for 5 wt% NaOH-treated composites, corresponding to an improvement of approximately 45% due to enhanced interfacial bonding. Subsequent CNT modification further improved tensile performance, with an optimum response observed at 0.25 wt% MWCNT, achieving a maximum UTS of 129.36 MPa and an elastic modulus of 8.1 GPa. Regression-based mathematical modelling captured the near-linear strengthening behavior induced by alkali treatment and the non-linear saturation-dominated response associated with CNT addition, with model predictions remaining within experimental scatter. Statistical reliability assessment using Weibull analysis demonstrated reduced strength variability for alkali-treated and optimally CNT-modified composites. Fracture surface analysis using scanning electron microscopy revealed a clear transition from interfacial debonding and fiber pull-out to cohesive fracture, crack bridging, and crack deflection mechanisms at optimized reinforcement levels. This study quantifies the combined effect of alkali treatment and low-loading CNTs on sisal bio-epoxy tensile behavior, achieving ~ 82% strength improvement with an optimum at 0.25 wt% CNT, while enhancing stiffness and maintaining controlled variability within the tested range. By integrating renewable natural fibers, low nanofiller content, and data-driven modelling, this study contributes to sustainable materials innovation (SDG 9), responsible material utilization (SDG 12), and reduced environmental impact through lightweight composite design (SDG 13). Ultimate tensile strength ; SISAL ; Epoxy ; Carbon nanotube ; Scanning electron microscope ; Reinforcement ; Response surface methodology Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Tribology and Wear Analysis
Authors: Chuanmao Zheng | Jieqing Li | Honggao Liu | Yuanzhong Wang
Source: Infrared Physics & Technology (article, 2026-03-26)
Field: Chemistry — Analytical Chemistry

Non-destructive geographical traceability of Gastrodia elata Blume with protected geographical indication using FT-NIR and FTIR combined with chemometrics Chemometrics ; Gastrodia elata ; Traceability ; Geographical indication ; Fourier transform infrared spectroscopy Spectroscopy and Chemometric Analyses ; Biological and pharmacological studies of plants ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Pujan Adhikari | Ya Sun | Mona Ghassemi | Jun Hu | Jinliang He
Source: IEEE Transactions on Dielectrics and Electrical Insulation (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Dielectric Breakdown Characteristics of Silicone Gel for (U)WBG Power Modules Under AC, DC, and High-Frequency Square Wave Stress As the adoption of (ultra) wide bandgap ((U)WBG) power modules accelerate, ensuring reliable dielectric performance in soft encapsulation materials such as silicone gel (SG) has become a pressing challenge. Existing studies on SG have primarily focused on partial discharge (PD) and electrical treeing under DC or low-frequency AC excitation, with limited attention to dielectric breakdown—despite its critical role in defining insulation failure thresholds. Moreover, few efforts have explored the effects of high-frequency waveforms that closely mirror the fast-switching transients typical of practical (U)WBG applications. This study investigates the dielectric breakdown characteristics of SG under three voltage stress conditions: DC, 60 Hz sinusoidal AC, and high-frequency bipolar square waveforms (10–50 kHz) with a fixed 50 ns rise time. Experimental results reveal a significant, frequency-dependent reduction in breakdown voltage under square wave stress, underscoring the vulnerability of SG to rapid voltage reversals. A two-parameter Weibull statistical analysis further quantifies this degradation, showing increasingly clustered failure distributions at higher frequencies. The breakdown behavior is interpreted through the interplay of space charge dynamics and the time-dependent self-healing response of SG. These findings highlight the importance of accounting for waveform characteristics—not just field strength—when evaluating insulation reliability in UWBG power modules and provide a data-driven basis for future encapsulation design strategies. Electrical treeing ; Waveform ; Dielectric strength ; Partial discharge ; Voltage ; Silicone ; Dielectric ; Square wave ; Breakdown voltage High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Power Transformer Diagnostics and Insulation
Authors: Mousa Bohlooli | Ali Akbar Saboury | Mansour Ghaffari-Moghaddam | Mostafa Khajeh | Halil Şenol
Source: Progress in Biophysics and Molecular Biology (article, 2026-03-31)
Field: Chemistry — Physical and Theoretical Chemistry

Classical and modern differential scanning calorimetry of proteins: Integrating equilibrium thermodynamics with emerging diagnostic and pharmaceutical applications Differential scanning calorimetry ; Equilibrium thermodynamics ; Calorimetry ; Non-equilibrium thermodynamics ; Work (physics) thermodynamics and calorimetric analyses ; Protein Structure and Dynamics ; Protein Interaction Studies and Fluorescence Analysis
Authors: Sujai Balasubramaniam
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-03-28)
Field: Materials Science — Polymers and Plastics

Techno-Economic Analysis of Yarn Clearer Cuts in the Autoconer Process: A Comprehensive Cost Evaluation Framework for the Indian Spinning Industry The Indian spinning industry, operating within compressed profit margins of approximately 4% of total sales value, faces an urgent requirement for precise micro-economic quantification of all conversion cost elements. This research article presents a rigorous, data-driven techno-economic model to calculate the exact financial burden of a single electronic yarn clearer (EYC) cut during the autoconer winding process. Through thermodynamic analysis of compressed air generation inefficiencies, electrical energy consumption during machine idling, raw material depletion via hard waste generation, mechanical component amortization, and opportunity cost quantification against SITRA-validated Net Output Value (NOV) benchmarks, the total direct cost of a single clearer cut on a standard Ne 40 combed compact cotton yarn is empirically determined to be INR 0.2908. The model further reveals that aggregated across a medium-scale 600-position winding installation operating at 1,300 m/min with 85% efficiency and a clearing rate of 50 cuts per 100 km, the annual financial haemorrhage attributable to clearer cuts exceeds INR 48.4 Million. A downstream paradox analysis demonstrates that the true, risk-adjusted economic penalty — inclusive of splice-induced loom stoppages and fabric quality degradation in downstream weaving — scales to approximately INR 100 per cut, validating the algorithms embedded in modern smart-clearing systems. The article provides actionable guidance for spinning mill management to deploy aerodynamically optimized winding technologies, inverter-driven vacuum systems, and algorithmic Yarn Body-based clearing curves as instruments of margin recovery. Spinning ; Weaving ; Yarn ; Clearing ; Profit margin ; Mill ; Net present value ; Profit (economics) ; Compressed air Textile materials and evaluations ; Research in Cotton Cultivation ; Global trade, sustainability, and social impact
Authors: Ahmed Neaz | KwangHyun Cho | Emdadul Hoque | Kanghyun Nam
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Polymers and Plastics

Adaptive Control and Fault Detection for Roll-to-Roll Yarn Processing System Using Artificial Intelligence Techniques Control theory (sociology) ; Fault detection and isolation ; Yarn ; Cascade ; Robustness (evolution) ; Adaptive control ; Servomotor ; Stiffness ; Tension (geology) Textile materials and evaluations ; Vibration and Dynamic Analysis ; Advanced Sensor and Energy Harvesting Materials
Authors: Fernando Fajardo-Rojas | Ryther Anderson | Katherine Ardila | Alexander J. Pak | Diego A. Gómez‐Gualdrón
Source: Chemistry of Materials (article, 2026-01-07)
Field: Chemistry — Inorganic Chemistry

Interactions of Common Synthesis Solvents with MOFs Studied via Free Energies of Solvation: Implications on Stability and Polymorph Selection Solvents are central to metal–organic framework (MOF) solvothermal synthesis. However, how solvent–MOF interplay impacts MOF stabilization individually and across polymorphs is not well understood. To address this knowledge gap, here we perform data-driven analysis of 20,532 heats of adsorption at dilute conditions (ΔHads) and 447 free energies of solvation (ΔFsol) for four solvents, namely, dimethylformamide (DMF), water (H2O), methanol (MeOH), and n-hexane (C6) (which was used as a control). To accelerate data collection, we developed a protocol to extrapolate ΔFsol from calculations with the MOFs only partially solvated. Free energies were obtained via thermodynamic integration. We found ΔFsol and ΔHads to be only moderately correlated due to solvent–solvent interactions coming into play when the MOF is solvated. In any case, trends in ΔFsol were ultimately explained on the basis of solvent kinetic diameter and polarity, as well as MOF void fraction (Vf) and functionalization polarity. For instance, the correlation between ΔFsol and Vf was one of the strongest correlations presented in this study (more so as the solvent size increases), indicating that small-pore MOFs are more easily stabilized by solvation than large-pore MOFs. We also found that solvation-induced MOF stabilization became more pronounced as solvent kinetic diameter (polarity) decreased (increased). We found differences in this solvation-induced stabilization between polymorphs capable of overcoming inherent (i.e., in vacuum) differences in polymorph stability, causing the most stable polymorph to “switch.” We found the probability to cause “switches” to increase as solvent kinetic diameter (polarity) decreased (increased). Inspection of multivariate linear regression coefficients suggested that differences in solvation-induced stabilization in polymorphs can be primarily explained by their differences in density, Vf, and, to a lesser extent, volumetric surface area. Solvation ; Solvent ; Kinetic energy ; Adsorption ; Solvent effects ; Dimethylformamide ; Methanol Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; X-ray Diffraction in Crystallography
Authors: Zhiqiang Wang | Kunfeng Chen | Guanqi Tang | Lijian Xu | Dongfeng Xue
Source: Journal of Crystal Growth (article, 2026-04-05)
Field: Materials Science — Materials Chemistry

From scalar indicators to full-field prediction: A multi-scale surrogate framework for thermal-field design in large-diameter Czochralski growth Gradient boosting ; Mean squared error ; Scalar (mathematics) ; Scalar field ; Generalization ; Thermal ; Temperature gradient ; Linear regression Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Solidification and crystal growth phenomena
Authors: Yunqing Zhu | Luqi Xie | Jing Wu | Yinghao Wei | Peng Wu
Source: Engineering Structures (article, 2026-01-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data-driven surrogate modeling of hysteretic responses combining mechanical principles and the improved Transformer Transformer ; Hysteresis ; Surrogate model ; Robustness (evolution) ; Control theory (sociology) ; Component (thermodynamics) Magnetic Properties and Applications ; Structural Health Monitoring Techniques ; Piezoelectric Actuators and Control
Authors: Sabrina Weber | Benedikt Prifling | R. K. Jeela | Andreas Prahs | Daniel Schneider | Britta Nestler | Volker Schmidt
Source: Repository KITopen (Karlsruhe Institute of Technology) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A time-continuous approach to analyzing anode aging in solid-oxide fuel cells via stochastic 3D microstructure modeling and physics-based simulations Microstructure ; Anode ; Robustness (evolution) ; Solid oxide fuel cell ; Computation ; Context (archaeology) ; Gaussian Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Karambir Singh | Manisha Dabla | Ankush Chauhan | Bijoy Kumar Kuanr | Khalid Mujasam Batoo | Pankaj Thakur | Vinod Kumar | Ritesh Verma
Source: Inorganic Chemistry Communications (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

Microwave absorption characteristics of hard and soft ferrite (SrFe12O19 and cu-doped co Zn ferrite) nanocomposites in Ku band Coercivity ; Crystallite ; Analytical Chemistry (journal) ; Nanocomposite ; Ferrite (magnet) ; Fourier transform infrared spectroscopy ; Scanning electron microscope ; Hexagonal phase ; Zinc ferrite ; Nanoparticle Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials ; Nanoparticle-Based Drug Delivery
Authors: Guoming Zhou | Hao Chang | Guangpu Fang | Xingshi Cai | Zhigang Rong | Wenlong Li
Source: Figshare (article, 2026-02-10)
Field: Chemistry — Analytical Chemistry

A Framework for Transferring Sinomenine Hydrochloride Sustained-Release Tablet from Batch to Continuous Direct Compression Manufacturing <b>Objective</b> The primary objective of this study was to establish and validate a practical framework for the transfer of a Sinomenine Hydrochloride sustained-release tablet from a legacy batch process to a pilot-scale Continuous Direct Compression (CDC) manufacturing line, without altering the product’s formulation. <b>Significance</b> The successful transfer of existing products to Continuous Manufacturing (CM) without reformulation represents a major industry challenge. In this work, a significant contribution is made through the validation of a data-driven pathway designed to de-risk this transition. A replicable strategy is demonstrated that aligns with Quality by Design principles and ICH Q13 guidelines, through which enhancements in cost-effectiveness and process sustainability for legacy pharmaceutical products can be achieved. <b>Methods</b> An integrated pilot-scale CDC line, operating at 1.9 kg/h, was utilized, comprising high-precision loss-in-weight feeders, a continuous blender, and a rotary tablet press. A Process Analytical Technology (PAT)-enabled control strategy was implemented, featuring a robust Partial Least Squares (PLS) model developed for real-time API quantification via in-line NIR spectroscopy. Process dynamics and material traceability were characterized using Residence Time Distribution (RTD) analysis to establish a state of control. <b>Results</b> Robust stability and control were demonstrated by the CDC process during a 300-minute steady-state run. Through real-time monitoring, API content uniformity was confirmed to be consistently maintained within a tight ± 3% tolerance. Critically, tablets produced via CDC were proven to be equivalent to the reference batch product across all critical quality attributes, including physical properties and functionally identical 24-hour dissolution profiles. <b>Conclusion</b> In this study, a comprehensive framework for the batch-to-continuous transfer of a sustained-release tablet is successfully validated. Process robustness is ensured by the PAT-enabled control strategy, which provides a reliable method for real-time quality verification. Based on the demonstrated product quality equivalence, the approach is confirmed as a low-risk, guideline-compliant pathway for modernizing the manufacturing of existing pharmaceutical products. Critical quality attributes ; Residence time distribution ; Pharmaceutical manufacturing ; Process analytical technology ; Quality by Design ; Process (computing) ; Traceability ; Process control Spectroscopy and Chemometric Analyses ; Innovative Microfluidic and Catalytic Techniques Innovation ; Berberine and alkaloids research
Authors: Zelan Li | Emilio Catelli | Jošt Stergar | Matija Milanič | Roberto Sáez-Hernández | Silvia Prati | Paolo Oliveri | Giorgia Sciutto
Source: ACS Measurement Science Au (article, 2026-02-12)
Field: Chemistry — Analytical Chemistry

Expanding the Capabilities of Portable Mapping in Macroscopic External Reflection FT-IR through a Targeted Data-Driven Spectral Enhancement and Denoising Strategy Reliable spectral quality is essential for extracting meaningful information from infrared reflectance data, particularly when using portable systems with limited scan numbers. This study presents a data-driven spectral enhancement workflow designed to improve the interpretability of portable macroscopic external reflection Fourier Transform Infrared (MA-rFT-IR) mapping systems developed by the Authors, operating in the near- and mid-infrared (NIR–MIR) ranges. Despite the growing use of reflectance imaging spectroscopy, limited attention has been devoted to the development of robust denoising strategies capable of minimizing noise and unwanted variability while preserving spectral quality and enabling more reliable and accurate data analysis. This study proposes a broadly applicable processing framework aimed at enhancing the efficiency and performance of reflectance-based spectral analysis. Denoising methods including Savitzky–Golay filtering and wavelet- and PCA-based denoising were tested and evaluated individually and in combination. Quantitative performance was assessed using arccosine similarity (ACOS) and derivative-based root-mean-square error (dRMSE) metrics across selected spectral regions of interest, with a derivative ACOS (dACOS) index applied to monitor band-shape variations. The evaluation results were integrated through Pareto analysis to identify the optimal trade-off between noise reduction and spectral-feature preservation. Application of the proposed approach to a multilayered painting mock-up demonstrated that the enhancing spectral data workflow preserves key diagnostic features revealing subtle spectral bands. Furthermore, applying multivariate curve resolution-alternating least-squares (MCR-ALS) to the denoised data enabled chemically meaningful separation of complex overlapping signals, improving the interpretability of compositional information compared with traditional denoising methods and data processing. The workflow strengthens the analytical reliability of low-scan reflection-mode data and provides a transferable framework for optimizing denoising strategies in portable infrared applications. Interpretability ; Noise reduction ; Workflow ; Noise (video) ; Reflection (computer programming) ; Pattern recognition (psychology) ; Reduction (mathematics) ; Data processing ; Dimensionality reduction ; Tikhonov regularization Spectroscopy and Chemometric Analyses ; Optical Imaging and Spectroscopy Techniques ; Remote-Sensing Image Classification
Authors: Mohammed Kuku | Mohamed Arishi | Ahmed S. Abutaleb | Ayman Yousef | M.M. El-Elhalwany
Source: Journal of King Saud University - Science (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Flexible electrospun Co–Cu/PVDF-HFP catalysts for hydrogen generation from NaBH₄ methanolysis This study explores the fabrication and application of Co 100-x –Cu x (x = 0, 10, 20, 40, 60, 80, 100) bimetallic catalysts supported on poly(vinylidene fluoride-co-hexafluoropropylene) nanofibers (PVHF NFs) for efficient H₂ generation via sodium borohydride (NaBH₄/SBH) methanolysis. The catalysts were synthesized using electrospinning, followed by in situ reduction, and their structural and morphological characteristics were confirmed using standard techniques. Catalytic performance was systematically evaluated under varying temperatures, catalyst doses, and SBH concentrations. The Co 60 Cu 40 catalyst exhibited the highest catalytic activity, significantly outperforming its monometallic counterparts. Kinetic studies revealed a pseudo-first-order reaction with an activation energy of 42.26 kJ/mol. The superior performance of the Co 60 Cu 40 catalyst is attributed to the synergistic effect between Co and Cu and the enhanced dispersion over the PVHF NF matrix. This study presents a promising method for designing flexible, high-performance, and cost-effective nanostructured catalysts for generating H₂ through the methanolysis of SBH. Catalysis ; Bimetallic strip ; Sodium borohydride ; Hydrogen production ; Dispersion (optics) ; Fabrication ; Activation energy Hydrogen Storage and Materials ; Electrocatalysts for Energy Conversion ; Metalloenzymes and iron-sulfur proteins