jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maximilian Schebek | Nikolas M. Froböse | Bettina G. Keller | Jutta Rogal
Source: ArXiv.org (article, 2025-12-20)
Field: Materials Science — Materials Chemistry

Estimating Solvation Free Energies with Boltzmann Generators Accurate calculations of solvation free energies remain a central challenge in molecular simulations, often requiring extensive sampling and numerous alchemical intermediates to ensure sufficient overlap between phase-space distributions of a solute in the gas phase and in solution. Here, we introduce a computational framework based on normalizing flows that directly maps solvent configurations between solutes of different sizes, and compare the accuracy and efficiency to conventional free energy estimates. For a Lennard-Jones solvent, we demonstrate that this approach yields acceptable accuracy in estimating free energy differences for challenging transformations, such as solute growth or increased solute-solute separation, which typically demand multiple intermediate simulation steps along the transformation. Analysis of radial distribution functions indicates that the flow generates physically meaningful solvent rearrangements, substantially enhancing configurational overlap between states in configuration space. These results suggest flow-based models as a promising alternative to traditional free energy estimation methods. Solvation ; Sampling (signal processing) ; Energy (signal processing) ; Flow (mathematics) ; Solvent models ; Phase (matter) ; Process (computing) ; Distribution (mathematics) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Mohadeseh Tabeshfar | Sivagnana Sundaram Anandakrishnan | Yang Bai
Source: Research Square (preprint, 2025-08-05)
Field: Materials Science — Materials Chemistry

Recycled piezoelectric materials with competitive second-life functional properties Piezoelectricity ; Business ; Competitive advantage ; Materials science ; Industrial organization ; Composite material ; Marketing Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Dielectric materials and actuators
Authors: Amjad Ibrahim Oraibi | Ashour H. Dawood | Nawres Debbabi | Ali Majeed Ali Almukram | Khalid S. Almaary | Fakhreldeen Dabiellil | Leila Chekir‐Ghedira | Soumaya Kilani-Jaziri
Source: Open Chemistry (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Resveratrol-derived MDM2 inhibitors: Synthesis, characterization, and biological evaluation against MDM2 and HCT-116 cells Abstract This study investigates the biological potential of novel resveratrol-based derivatives targeting the MDM2 protein, a critical regulator of the tumour suppressor p53 in cancer therapy. We synthesized and characterized four derivatives of ( E )-2,4-dimethoxy-6-(4-methoxystyryl) benzaldehyde using mass spectrometry, proton nuclear magnetic resonance, and Fourier-transform infrared spectroscopy. These derivatives were specifically designed to enhance binding affinity, stability, and selectivity compared to inhibitors like Nutlin. Biological evaluation through MTT assays revealed varying antiproliferative activities against HCT-116 cancer cells. AMJ3 exhibited the strongest activity, with an IC50 value of 22.69 ± 2.47 µg/mL, outperforming that of the reference compound Nutlin (IC50: 62.72 ± 3.15 µg/mL). MDM2-p53 inhibitory activity confirmed AMJ3 as the most potent inhibitor (IC50: 0.24 ± 0.02 µM), followed by AMJ5 (IC50: 0.48 ± 0.04 µM), both surpassing Nutlin (IC50: 0.39 ± 0.03 µM). Molecular docking studies for AMJ3 and AMJ6 achieved superior glide scores of −5.6 and −4.9, respectively. Molecular dynamics simulations validated these findings, showing that AMJ3 formed a stable hydrogen bonding interaction with Leu33 of the MDM2 protein and hydrophobic interactions with Ile40 and Tyr79, while Nutlin-3a showed weaker interactions overall. These results highlight AMJ3 and AMJ5 as promising MDM2 inhibitors with enhanced specificity and efficacy and better activity than Nutlin. Resveratrol ; Mdm2 ; Characterization (materials science) ; Chemistry ; Pharmacology ; Computational biology ; Cancer research ; Medicine ; Biology ; Biochemistry ; Nanotechnology ; Materials science ; Apoptosis Click Chemistry and Applications ; Protein Degradation and Inhibitors ; HIV Research and Treatment
Authors: Kambale Christian Katasohire
Source: Open Journal of Polymer Chemistry (article, 2025-01-01)
Field: Materials Science — Biomaterials

QSPR Modeling for Mechanical Properties of Reinforced Thermoplastic Starch In this work, repeat units of Thermoplastic Starch (TPS) and Natural rubber (NR) were built on atomistic windows from Material Studio 2017 Software. The energy of the structures was minimized via geometry optimization option of “Forcite” module. By selecting its constituting repeat units, the polymer composite was defined. Mechanical properties of the computational bulk made of Thermoplastic Starch (TPS) were investigated at different contents of reinforcing agent (natural rubber). This investigation is based on complex quantitative structure-property relationship (QSPR) calculations executed by “Synthia” module. The Synthia module enables QSPR modelling of polymer systems based on molecular descriptor such as connectivity indices specific to every repeat unit constructed within the software. The results revealed a reduction in bulk modulus, Young’s and shear modulus; this implies that the blend became more pliable and softer due to addition of natural rubber (NR). In contrast, Poisson’s ratio shifted upward toward approximately 0.5, indicating a transition toward more elastic and softer material characteristics. These results are in agreement with experimental data, which revealed similar trends in the mechanical behavior of the blend in correlation with high natural rubber content. This study provides the first QSPR-based mechanical prediction of TPS-based biopolymer blends. The approach offers a rapid computational alternative to traditional mechanical testing and may guide material design for biodegradable packaging and controlled-release applications. Natural rubber ; Thermoplastic ; Polymer ; Starch ; Thermoplastic elastomer ; Composite number ; Material properties biodegradable polymer synthesis and properties ; Nanocomposite Films for Food Packaging ; Polymer Nanocomposites and Properties
Authors: Manon Chossegros | Amelia Hubbard | Megan Burt | Richard J. Harrison | Charlotte F. Nellist | Nastasiya F. Grinberg
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-07-18)
Field: Chemistry — Analytical Chemistry

Hyperspectral image analysis for classification of multiple infections in wheat Abstract Plant diseases can cause heavy yield losses in arable crops resulting in major economic losses. Effective early disease recognition is paramount for modern large-scale farming. Since plants can be infected with multiple concurrent pathogens, it is important to be able to distinguish and identify each disease to ensure appropriate treatments can be applied. Hyperspectral imaging is a state-of-the art computer vision approach, which can improve plant disease classification, by capturing a wide range of wavelengths before symptoms become visible to the naked eye. Whilst a lot of work has been done applying the technique to identifying single infections, to our knowledge, it has not been used to analyse multiple concurrent infections which presents both practical and scientific challenges. In this study, we investigated three wheat pathogens (yellow rust, mildew and Septoria), cultivating co-occurring infections, resulting in a dataset of 1,447 hyperspectral images of single and double infections on wheat leaves. We used this dataset to train four disease classification algorithms (based on four neural network architectures: Inception and EfficientNet with either a 2D or 3D convolutional layer input). The highest accuracy was achieved by EfficientNet with a 2D convolution input with 81% overall classification accuracy, including a 72% accuracy for detecting a combined infection of yellow rust and mildew. Moreover, we found that hyperspectral signatures of a pathogen depended on whether another pathogen was present, raising interesting questions about co-existence of several pathogens on one plant host. Our work demonstrates that the application of hyperspectral imaging and deep learning is promising for classification of multiple infections in wheat, even with a relatively small training dataset, and opens opportunities for further research in this area. However, the limited number of Septoria and yellow rust + Septoria samples highlights the need for larger, more balanced datasets in future studies to further validate and extend our findings under field conditions. Hyperspectral imaging ; Convolutional neural network ; Pattern recognition (psychology) ; Plant disease ; Image processing ; Convolution (computer science) ; Arable land ; Contextual image classification Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Célia Bouacha | Séverine Denoyelle | Sonia Cantel
Source: Advanced Therapeutics (article, 2025-09-12)
Field: Chemistry — Organic Chemistry

From Design to Outcome: The Role of Bi‐ and Multifunctional Ligands in Modern Therapeutics Abstract The multifunctional strategy offers significant benefits in developing highly selective targeting biomolecules for therapeutics, diagnostics, dynamics studies, or mapping processes in various environments. Bioorthogonal reactions constitute an engineering approach enabling the attachment of ligands targeting receptors involved in numerous physiological dysfunctions onto multifunctional scaffolds. Herein, the latest generation of derivatized probes are overviewed for high‐resolution screening in diagnosis or treatment. These compounds also represent valuable tools for investigating physiological roles, filling the gap of information in the mechanism of understanding various receptors and contributing to the design of effective drugs. Strategic linker design enhances ligand properties when developing dual‐modality probes, impacting their effectiveness across applications. Nanoparticles are emerging as revolutionary concepts, offering promising solutions to address limitations in the field and paving the way for advanced therapeutic approaches and improved drug delivery systems. Bioorthogonal chemistry ; Drug delivery ; Mechanism (biology) ; Drug discovery ; Targeted drug delivery ; Linker Click Chemistry and Applications ; RNA Interference and Gene Delivery ; Peptidase Inhibition and Analysis
Authors: Subrit Dikshit | Ritu Tiwari | Pooja Jain
Source: Research Square (preprint, 2025-07-18)
Field: Materials Science — Materials Chemistry

Multilingual Machine Translation with Quantum Encoder Decoder Attention-based Convolutional Variational Circuits Computer science ; Machine translation ; Pooling ; Encoder ; Quantum computer ; Language translation ; Computer engineering ; Theoretical computer science ; Natural language processing ; Artificial intelligence ; Computer hardware ; Quantum ; Operating system Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Quantum Computing Algorithms and Architecture
Authors: Putu Sugiartawan | Nobuo Funabiki | I Nyoman Darma Kotama | Amma Liesvarastranta Haz | Komang Candra Brata | Ni Wayan Wardani
Source: Applied Sciences (article, 2025-12-25)
Field: Chemistry — Analytical Chemistry

Proposal for Two-Stage Machine Learning-Based Algorithm for Dried Moringa Leaves Quality Classification Nowadays, dried Moringa leaves (M. oleifera) are increasingly in demand due to their health benefits. High-quality ones have shown remarkable positive effects as antioxidants, antidiabetics, and anti-inflammatory agents. However, in the industry, the quality classification process into six categories is performed manually by farmers, which is time-consuming and error-prone. Particularly, the two highest categories of Class A and Class B are hard to distinguish, since they are visually similar. In this paper, to automate the classification process, we introduce a new high-resolution dataset, extract color and texture features using the Gray-Level Co-occurrence Matrix (GLCM) method, and present a two-stage classification method using the Light Gradient Boosting Machine (LightGBM) algorithm with them. The experimental results show that the proposal improved classification accuracy from 82% by the baseline algorithm to 90% while maintaining high processing efficiency, demonstrating its potential for real-time and scalable industrial applications in dried Moringa leaves quality grading. Moringa ; Boosting (machine learning) ; Pattern recognition (psychology) ; Class (philosophy) ; Statistical classification ; Scalability ; Quality (philosophy) Spectroscopy and Chemometric Analyses ; Moringa oleifera research and applications ; Smart Agriculture and AI
Authors: Shuai Zhang | Xin Lai | Haiyan Niu | Lisheng Liu | Shifu Wang | Jinyong Zhang
Source: Computer Modeling in Engineering & Sciences (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Dynamic Compressive Behavior and Stress Wave Attenuation Characteristics of Ti-6Al-4V Lattice Structure This study investigates the dynamic compressive behavior of three periodic lattice structures fabricated from Ti-6Al-4V titanium alloy, each with distinct topologies: simple cubic (SC), body-centered cubic (BCC), and face-cen... | Find, read and cite all the research you need on Tech Science Press Materials science ; Attenuation ; Stress wave ; Compressive strength ; Lattice (music) ; Composite material ; Structural engineering ; Acoustics ; Physics ; Engineering ; Optics High-Velocity Impact and Material Behavior ; Advanced ceramic materials synthesis ; Aluminum Alloys Composites Properties
Authors: Azusa Kubota | Takaya Satoh | Masaaki Ubukata | Ayumi Kubo | Chikako Nakayama
Source: Mass Spectrometry (article, 2025-01-07)
Field: Chemistry — Spectroscopy

Characterization of Ultraviolet-Degraded Polyethylene Terephthalate Film Using a Complementary Approach: Reactive Pyrolysis–Gas Chromatography–Mass Spectrometry and Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Polyethylene terephthalate (PET) is widely used across various industries owing to its versatility and favorable properties, including application in beverage bottles, food containers, textile fibers, engineering resins, films, and sheets. However, polymer materials are susceptible to degradation from factors such as light, oxygen, and heat. Therefore, it is crucial to understand the structural changes that occur during degradation and the extent of these changes. This report investigates the structural alterations in PET films resulting from ultraviolet (UV) irradiation utilizing pyrolysis-gas chromatography time-of-flight mass spectrometry (Py-GC-TOFMS) and matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOFMS). Using the reactive Py-GC-TOFMS, we estimated the composition of the pyrolysis products resulting from UV degradation through electron ionization, soft ionization, and exact mass measurements. Additionally, artificial intelligence (AI)-based structure analysis was performed to evaluate these compounds' structures. Notably, most degradation products were not found in the National Institute of Standards and Technology database, underscoring the effectiveness of our approach. Using MALDI-TOFMS analysis, we determine the changes in the end groups before and after UV irradiation. This analysis confirmed the generation of a series of carboxylic acid end groups as a result of degradation, a polymer series not detected by reactive pyrolysis GC-MS. We also explored degradation in the depth direction, demonstrating that degradation progresses gradually to depths of several micrometers. Our findings highlight the importance of employing mass spectrometry techniques for a comprehensive analysis of polymer degradation. Mass spectrometry ; Pyrolysis ; Materials science ; Time-of-flight mass spectrometry ; Polymer ; Degradation (telecommunications) ; Matrix-assisted laser desorption/ionization ; Desorption ; Gas chromatography ; Polyethylene ; Direct electron ionization liquid chromatography–mass spectrometry interface ; Polyethylene terephthalate ; Sample preparation in mass spectrometry ; Analytical Chemistry (journal) ; Chemistry ; Ionization ; Chromatography ; Organic chemistry ; Chemical ionization ; Electrospray ionization ; Composite material Mass Spectrometry Techniques and Applications ; Microplastics and Plastic Pollution ; Forensic Fingerprint Detection Methods
Authors: Jayakumar Veeraraghavan | Harshini Vasanth | Heram Harikrishnan | Jacinth Medah | Joshua Albert | K. Sankar | Erdal Dіnç | Lakshmi Sivasubramanian
Source: Journal of Drug Delivery and Therapeutics (article, 2025-08-15)
Field: Chemistry — Analytical Chemistry

Machine Learning-Assisted Simultaneous Estimation of Aspirin and Dipyridamole Using UV–Visible Spectrophotometer Simultaneous estimation of Aspirin (ASP) and Dipyridamole (DIP) was developed using UV– visible spectrophotometer. Multicomponent analysis mode was used for the estimation of both drugs at their respective wavelength maxima. Both drugs were found to be linear within the concentration range of 5-30 µg/mL. The correlation coefficient (R2 value) of ASP and DIP were found to be 0.995 and 0.996 respectively. Method validation parameters such as accuracy, precision, and recovery studies were found to be within acceptable ICH limits. H point standard addition method (HPSAM) was developed for the estimation of both the drugs at the selected isosbestic points namely 218 and 226nm. Python code was developed to calculate the validation parameters of the developed methods. The percentage purity for both methods was found to be within the range of 94.4 to 106.84%. The relative standard deviation (RSD) of ASP and DIP was found to be 2.26 and 2.21 respectively and the percentage recovery was in the range from 93.41 to 94.59%. Green metrics was performed using AGREE software and the developed methods show greenness with a score of 0.79. Both the methods were found to be accurate and precise. The developed Python code offers additional support in plotting the graph and in the calculation of different parameters. An attempt was made to develop an APP using the optimized Python code for calculating HPSAM and Multi-component method validation parameters. Keywords: Simultaneous estimation, HPSAM, isosbestic points, UV Vis spectroscopy, Python, Multicomponent Analysis Dipyridamole ; Aspirin ; Chromatography ; Chemistry ; Computer science ; Medicine ; Internal medicine Analytical Methods in Pharmaceuticals ; Computational Drug Discovery Methods ; Antibiotics Pharmacokinetics and Efficacy
Authors: Ehsan Sheidaee | Pourya Bazyar
Source: Research in Agricultural Engineering (article, 2025-05-12)
Field: Chemistry — Analytical Chemistry

Enhancing the destructive egg quality assessment using the machine vision and feature extraction technique The rapid growth of the food industry necessitates rigorous quality control, particularly in egg production. This study explores advanced methodologies for egg quality assessment by integrating the Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and k-Nearest Neighbour (KNN) with machine vision techniques. While traditional destructive methods like measuring the Haugh unit (HU) offer direct insights, but render eggs unusable, non-destructive techniques, such as imaging and spectroscopy, allow continuous quality monitoring. Over a 20-day period, egg samples were evaluated using a digital camera to capture key parameters like the albumen and yolk heights. The study’s image processing involved noise reduction, feature extraction, and calibration. The PCA captured 90.18% of the data variability, while LDA achieved 100% classification accuracy, and KNN demonstrated 80% accuracy. These findings underscore the effectiveness of combining machine vision with statistical methods to enhance the egg grading accuracy, contributing to consumer safety and industry standards. Quality (philosophy) ; Feature extraction ; Artificial intelligence ; Computer vision ; Extraction (chemistry) ; Computer science ; Pattern recognition (psychology) ; Feature (linguistics) ; Engineering ; Chromatography ; Chemistry ; Physics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Animal Nutrition and Physiology
Authors: Ming Wu | Yiming Zhao | Yunfei Zhang | Zongqiang Ren | Yan Cui | Yongbin Liu | Lisheng Zhong | Xiaojie Lou | Jinghui Gao
Source: High Voltage (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

Developing Implantable High‐Sensitivity Piezoelectric Thin‐Film Sensor to Monitor Partial Discharge of Generator Stator Windings ABSTRACT Insulation failures resulting from partial discharges (PD) in generator stator windings represent a leading cause of generator breakdowns. Conventional acoustic emission sensors for detecting PD ultrasonic signals are typically made from bulky piezoelectric materials and are restricted to external placement on the generator. Their distant placement from the PD source limits sensitivity. This study presents an implantable piezoelectric thin‐film ultrasonic sensor designed for high‐sensitivity, real‐time monitoring of PD‐induced ultrasonic signals within generator stator windings. The sensor features a Pb(Zr 0.52 ,Ti 0.48 )O 3 thin film with an enlarged electrode area to improve sensitivity. In tests using a pencil‐lead break on a polyethylene plate as the ultrasonic source, the implantable sensor exhibited a higher peak‐to‐peak signal than a commercial broadband sensor and nearly twice that of a commercial polyvinylidene fluoride (PVDF) sensor. The implantable sensor demonstrated an upper frequency detection limit of 80 kHz for pencil‐break signals, compared to 30 kHz for both the broadband and PVDF sensors. Full‐scale generator experiments validated the sensor’s high‐fidelity detection of PD signals, emphasising its potential to enhance insulation condition monitoring in power systems. Stator ; Partial discharge ; Ultrasonic sensor ; Electromagnetic coil ; Piezoelectricity ; Generator (circuit theory) ; SIGNAL (programming language) ; Piezoelectric sensor ; Broadband High voltage insulation and dielectric phenomena ; Advanced Sensor and Energy Harvesting Materials ; Dielectric materials and actuators
Authors: Simon P. Ringer
Source: MRS Bulletin (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Solute atom control for materials design Abstract The spatial distribution of solute atoms—within and between phases—is a foundational aspect of microstructure. While alloying and doping have long been used to modify the properties of metals, ceramics, and semiconductors, less emphasis has been placed on controlling solute behavior at the atomic scale. This article highlights solute engineering as a powerful and emerging lever in materials design. Whether solute atoms cluster, segregate to defects, or partition between phases depends on the local chemical potential along a continuous energy landscape. Yet the resulting atomic scale solute topologies—short-range order, segregation, and partitioning—are distinct in form and consequence, offering diverse routes to tailor structure, function, and performance. A hierarchical framework is presented to map these solute-driven phenomena in both single-phase and multiphase systems, advancing the concept of microstructure-by-design toward atomically resolved control of materials properties. Graphical abstract Atom (system on chip) ; Partition (number theory) ; Atomic units ; Lever ; Doping ; Scale (ratio) ; Nanoscopic scale Nanoporous metals and alloys ; Machine Learning in Materials Science ; Advanced Materials Characterization Techniques