jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ramesh Koduru | Somedutta Maity | Dibakar Das | Ramachandra Reddy Pamuru | Arifullah Mohammed | Gooty Jaffer Mohiddin
Source: Journal of Nanobiotechnology (article, 2026-02-09)
Field: Materials Science — Biomaterials

Nanotechnology for reproductive healthcare: a comprehensive review The advancement of nanotechnology has revolutionized reproductive healthcare worldwide. It has enabled the treatment of various conditions, such as infertility, endometriosis, ectopic pregnancies, erectile dysfunction, sexually transmitted infections (STIs), and reproductive tissue cancers. Nanotechnology offers improved imaging, personalized drug administration, and early diagnosis, leading to more accurate treatment outcomes. It can enhance fertility preservation, improve individualized therapy, and improve diagnostic methods. Additionally, nanotechnology-powered drug delivery systems increase effectiveness while reducing side effects. Clinical trials utilizing nanoparticles for cellular treatments, targeted drug delivery, early infection detection, reproductive system malignancies and precise medication delivery are currently underway. Nanotechnology has opened new possibilities in areas such as disease detection, drug administration, diagnostic imaging, and cancer treatment. This review aims to provide an overview of the different types, characteristics, and synthesis methods of nanocarriers designed for medication delivery in the context of reproductive disorders and diseases. It seeks to enhance the understanding of the current state of the art and explore potential future advancements in this field. Nanocarriers ; Context (archaeology) ; Applications of nanotechnology ; Drug delivery ; Nanomedicine ; Personalized medicine ; Targeted drug delivery ; Impact of nanotechnology Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Nanoparticles: synthesis and applications
Authors: Tao Sun | Zhigang Wu | Wei Liu | Ruina Yang | Huimin Zhang | Ju Lu | Jian Shen | Ruihua Zhang | Xinqing Xiao
Source: Agriculture (article, 2026-02-26)
Field: Chemistry — Analytical Chemistry

Vis/NIR Based Flexible Non-Destructive Sensing for Almonds A flexible visible/near-infrared (Vis/NIR) sensing system (FVNS) was developed for the non-destructive assessment of almond composition. Almonds from four distinct varieties were measured under non-contact conditions, and the acquired spectra were preprocessed using Savitzky–Golay (S–G) smoothing and standard normal variate (SNV). Based on the spectral data captured by the FVNS, random forest (RF) regression models were established to quantify protein and fat contents. The optimized RF models achieved prediction coefficients of determination (R2p) of 0.91 for protein and 0.86 for fat, with corresponding residual predictive deviation (RPD) values of 3.32 and 2.67, respectively. These results demonstrate that the FVNS possesses reliable quantitative capability and can accurately capture compositional variations in almonds while maintaining low cost, portability, and real-time wireless operation. Residual ; Smoothing ; Standard deviation ; Random forest ; Random variate ; Relative standard deviation Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Longhai Ye
Source: Frontiers in Materials (article, 2026-04-07)
Field: Materials Science — Materials Chemistry

Study on dynamic constitutive relationship of 2D-C/SiC composite materials Introduction This study aims to investigate the tensile dynamic response of 2D-C/SiC composites over a broad strain rate range, elucidating their rate-dependent mechanical properties and damage evolution rules. Methods Tensile tests were conducted on 2D-C/SiC specimens at strain rates ranging from 1 × 10 −3 to 3 × 10 2 s −1 . A macroscopic damage-based constitutive model was developed via in-depth analysis of the obtained tensile experimental data to characterize the composite's mechanical response. Results Test results demonstrate that the tensile strength and elastic stiffness of 2D-C/SiC composites increase with strain rate, whereas failure strain decreases. A near-linear correlation is observed between the composite's elastic modulus and the logarithm of strain rate. Additionally, macroscopic damage evolution within the composite is jointly dominated by applied strain and strain rate. Discussion The distinct rate dependence of tensile mechanical properties and damage behavior confirms that loading rate is a pivotal factor governing the progressive failure mechanisms of 2D-C/SiC composites. The proposed constitutive model effectively captures the composite's failure evolution under diverse loading conditions, providing a theoretical foundation for its engineering application and safety assessment under dynamic loads. Ultimate tensile strength ; Strain rate ; Stiffness ; Constitutive equation ; Strain (injury) ; Tensile testing ; Elastic modulus ; Composite number High-Velocity Impact and Material Behavior ; Advanced ceramic materials synthesis ; Mechanical Behavior of Composites
Authors: Jianheng Tang | Shilong Tao | Zhe Chuan Feng | Haonan Sun | Menglu Wang | Zhanxing Zhu | Yunhuai Liu
Source: ArXiv.org (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

FilDeep: Learning Large Deformations of Elastic-Plastic Solids with Multi-Fidelity Data The scientific computation of large deformations in elastic-plastic solids is crucial in various manufacturing applications. Traditional numerical methods exhibit several inherent limitations, prompting Deep Learning (DL) as a promising alternative. The effectiveness of current DL techniques typically depends on the availability of high-quantity and high-accuracy datasets, which are yet difficult to obtain in large deformation problems. During the dataset construction process, a dilemma stands between data quantity and data accuracy, leading to suboptimal performance in the DL models. To address this challenge, we focus on a representative application of large deformations, the stretch bending problem, and propose FilDeep, a Fidelity-based Deep Learning framework for large Deformation of elastic-plastic solids. Our FilDeep aims to resolve the quantity-accuracy dilemma by simultaneously training with both low-fidelity and high-fidelity data, where the former provides greater quantity but lower accuracy, while the latter offers higher accuracy but in less quantity. In FilDeep, we provide meticulous designs for the practical large deformation problem. Particularly, we propose attention-enabled cross-fidelity modules to effectively capture long-range physical interactions across MF data. To the best of our knowledge, our FilDeep presents the first DL framework for large deformation problems using MF data. Extensive experiments demonstrate that our FilDeep consistently achieves state-of-the-art performance and can be efficiently deployed in manufacturing. Deformation (meteorology) ; Focus (optics) ; Computation ; Dilemma ; Bending ; Deep learning Machine Learning in Materials Science ; Model Reduction and Neural Networks ; 3D Shape Modeling and Analysis
Authors: Xiaofu Wei | Zhangyi Chen | Kuanglei Chen | Jinsen Shang | Li Gao | He Jiang | Huihui Yu | Mengyu Hong | Xiankun Zhang | Zheng Zhang | Yue Zhang
Source: Nature Communications (article, 2026-03-15)
Field: Materials Science — Materials Chemistry

Generic logic block based on bias-gated 2D MoS2 transistors Integrating two-dimensional (2D) semiconductors and CMOS technology is a promising pathway for next-generation integrated circuits. However, the implementation of this strategy is hindered by the limited polarity modulation of 2D semiconductors and the inherent characteristics of CMOS architectures. Here, we report a generic logic block (GLB) that uses bias-gated molybdenum disulfide transistors (BG-FET). The BG-FETs exhibit threshold-adaptive field-effect characteristics through simple bias manipulation, enabling good rail-to-rail operation in the inverter circuits. The GLB composed of four BG-FETs can perform reconfigurable logic operations in a single circuit, including basic gate circuits, half-adder, multiplexer, encoder and static-random-access-memory. In contrast, conventional CMOS circuits require at least 100 transistors to perform the same set of operations. We further integrate flip-flop circuits and arithmetic logic units by cascading as-fabricated GLBs, featuring a transistor count saving of over 60% compared to CMOS design. Collectively, BG-FET-based GLB emerges as a post-CMOS integration technology with compelling potential. Transistor ; Block (permutation group theory) ; Logic gate ; Pass transistor logic ; Logic block 2D Materials and Applications ; Advanced Memory and Neural Computing ; Neural Networks and Reservoir Computing
Authors: Tao Deng | Jiyang Xie | Zhen Liu | Liqiang He | Zhichao Hong | Haonan Peng | Dong Wang | Cosme Milesi-Brault | Teng Lu | Yonghong Chen | Zhisheng Lin | Wanbiao Hu | Brahim Dkhil | Yun Liu | Genshui Wang | Junhao Chu
Source: Nature Communications (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Superior energy storage performance via engineering crossover region with competing orders in high-entropy multilayer capacitors As promising candidates for next-generation energy storage devices in electrical and electronic systems, lead-free multilayer ceramic capacitors face increasingly high performance requirements. To counteract the usual trade-off between energy storage density and efficiency, we here propose a high-entropy design that directly harnesses diverse oxide symmetries to targetedly engineer competing orders and tune the composition into the crossover region between relaxor ferroelectric and superparaelectric states. Atomic-scale structural analysis reveals high-entropy ceramic develops pronounced local polarization fluctuation and dispersed oxygen octahedral rotations, which enhance relaxor behavior and reduce switching barrier. Consequently, superior recoverable energy density of 20.64 J cm<sup>-3</sup> and high efficiency of 94.2% are obtained in our designed high-entropy Bi<sub>0.5</sub>Na<sub>0.5</sub>TiO<sub>3</sub>-based multilayer ceramic capacitors, along with excellent thermal/anti-fatigue stability and charge-discharge capabilities. This work provides a transferable strategy to engineer competing orders in lead-free dielectric materials and successfully achieves high-entropy multilayer ceramic capacitors with superior energy storage performance. Capacitor ; Energy storage ; Dielectric ; Ceramic ; Ceramic capacitor ; Supercapacitor ; Ferroelectricity ; Polarization (electrochemistry) Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices ; Magnetic and transport properties of perovskites and related materials
Authors: Deepa Pandey | Widhi Dubey
Source:  (article, 2026-01-31)
Field: Materials Science — Materials Chemistry

COPPER OXIDE NANOPARTICLE-BASED SEED PRIMING ENHANCES GERMINATION, GROWTH, AND YIELD IN OKRA (Abelmoschus esculentus L.) Poor seed germination and weak early vigor limit growth and yield of okra (Abelmoschus esculentus L.). Seed priming is a promising strategy for improving crop establishment, and nanoparticle-based priming may enhance nutrient delivery compared to ionic sources. In this study, copper oxide nanoparticles (CuO NPs) were green-synthesized using radish (Raphanus sativus L.) leaf extract and characterized by FTIR, FESEM–EDS, and zeta potential analysis. The CuO NPs exhibited near-spherical morphology (150–250 nm), confirmed Cu–O bonding, and moderate colloidal stability (−23.21 mV). Seed priming did not significantly affect germination percentage; however, seedling growth responses were strongly treatment dependent. CuO NP priming at 25–50 ppm significantly enhanced root length and fresh biomass, while CuSO4 treatments caused growth inhibition. Pot experiments showed that CuO NP priming significantly increased plant height, pod number, pod length, and total yield, with maximum yield recorded at 50 ppm. Strong positive correlations were observed among vegetative growth, pod traits, and yield. In conclusion, green-synthesized CuO NPs applied through seed priming were more effective than ionic copper in enhancing okra growth and productivity, highlighting their potential as a nano-enabled strategy for sustainable crop improvement. Germination ; Point of delivery ; Priming (agriculture) ; Seedling ; Seed treatment ; Crop yield ; Yield (engineering) Nanoparticles: synthesis and applications ; Silicon Effects in Agriculture ; Seed Germination and Physiology
Authors: Selvarengan Paranthaman | B. Shanmuga Raja | Vedhavarshini Ramachandran
Source: Indian Journal of Pure & Applied Physics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A Machine Learning-Driven Methodology for the Precise Determination of the Ground State Energy of Helium Atom Here the ground state energy of helium atom is investigated and also a Machine Learning (ML) model is constructed using Gaussian Process Regression (GPR) algorithm for the same. The parameters free perturbative method in matrix representation approach is used,in which the approximation is improved by adding higher order p-orbital states. The error is reduced to be 1.93%.This allows us to confirm that the accuracy of the energy value will converge with respect to adding higher order states of p-orbital, d-orbital, etc. Since here prediction belongs to regression model, Gaussian Process Regression (GPR) is chosen. With small dataset we extend this work for ML energy prediction model using GPR technique which is used to inter or extrapolate the ground state energy value.Cross validation is also done using R2 evaluation metric. Ground state ; Helium atom ; Energy (signal processing) ; Kriging ; Helium ; Atom (system on chip) ; Representation (politics) ; Gaussian process ; Gaussian Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Quantum, superfluid, helium dynamics
Authors: Emran Hossain | Prasad Potluri | Chamil Abeykoon | Anura S. Fernando
Source: Textiles (article, 2026-01-07)
Field: Materials Science — Polymers and Plastics

From Material to Manufacture: A State-of-the-Art Review of Compression Garment Technologies for Medical and Sports Use Compression garments are widely employed in medical and sports contexts for their ability to promote venous return, manage oedema, support musculoskeletal function, and enhance athletic recovery. Advances in textile-based compression systems have been driven by innovations in fibres, yarn structures, fabric structure engineering, and design methods. This review critically examines the current literature on compression garments, highlighting the influence of raw materials and yarn architectures on performance, durability, and wearer comfort. Attention is given specially to fabric structures and manufacturing methods, where the evolution from traditional cut-and-sew methods to advanced seamless, flatbed, and circular knitting technologies is highlighted, along with their impact on pressure distribution and overall garment efficacy. The integration of 3D body scanning, finite element analysis, and predictive modelling, which enables more personalised and precise garment design, is also speculated upon. Moreover, the review highlights testing and evaluation methodologies, spanning both in vivo and in vitro based assessments, pressure sensor studies for real-time monitoring, and theoretical models mostly based on Laplace’s law. This literature survey provides a foundation for future innovations aimed at optimising compression garment design for both therapeutic and athletic use. Clothing ; Yarn ; Compression (physics) ; Foundation (evidence) ; Compression stockings ; Finite element method Textile materials and evaluations ; Exercise and Physiological Responses ; Diagnosis and Treatment of Venous Diseases
Authors: Ester Butera | Regina Maria Chiechio | Saad Akeel | Valérie Marchi | R. Reitano | F. Ruffino | Valentina Giglio | Grazia Maria Letizia Consoli | Salvatore Petralia.
Source: Applied Surface Science Advances (article, 2026-02-28)
Field: Materials Science — Materials Chemistry

One-step photochemical synthesis of NIR-emissive and photothermal gold nanoclusters for biosensing application An ecofriendly photochemical approach for the rapid and effective preparation of ultrasmall NIR-emissive gold-nanoclusters from photoirradiation of water-soluble glutathione (GSH) in the presence of HAuCl 4 is reported. The formation of gold nanoclusters with a glutathione shell (AuNCs-SG) is confirmed by optical measurements, mass spectrometry, NMR spectroscopy, and transmission electron microscopy. The AuNCs-SG exhibit good luminescence quantum yield (φ=1.1 %), long emission lifetime (1.22 μs), large Stoke’s shift (435 nm), and good photothermal yield conversion upon NIR-light excitation (η 808nm =33.9 %). The effect of the GSH ligand amount and AuNC compositions on the optical properties are extensively investigated. Bimetallic silver/gold-nanoclusters (AgAuNCs-SG) are also prepared and characterized to evaluate the effect of the Ag-doping on the optical properties of the nanocluster. The AuNCs-SG are successfully tested for the optical biosensing of Interleukine 6-antigen (IL6), with a LoD value of 57.5 fg mL −1 , through the formation of an IL6−antibody/nanocluster conjugate. The PL signal quenching is related to the amount of IL6 in the samples. Nanoclusters ; Biosensor ; Bimetallic strip ; Photothermal therapy ; Quenching (fluorescence) ; Luminescence ; Fluorescence ; Quantum yield Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: G. A. M. Alves | Mohan Dodeja | Fazal Khan | Mary Szocik | Arosh S. Perera Molligoda Arachchige
Source: Instruments (article, 2026-02-03)
Field: Chemistry — Spectroscopy

The Intelligent Knife (iKnife): Revolutionizing Intraoperative Tissue Diagnosis Through Rapid Evaporative Ionization Mass Spectrometry (REIMS) The intelligent surgical knife (iKnife), based on rapid evaporative ionization mass spectrometry (REIMS), represents a transformative advance in intraoperative tissue characterization. By integrating mass spectrometry with electrosurgical dissection, the iKnife enables real-time differentiation between cancerous and healthy tissues through molecular fingerprinting of the aerosol generated during cutting. This innovation significantly shortens operative time by eliminating delays associated with conventional histopathological analysis and enhances surgical precision by providing continuous feedback on tissue composition. Since its inception by Zoltán Takáts and colleagues, the iKnife has demonstrated remarkable diagnostic accuracy across multiple cancer types, including breast, ovarian, and colorectal malignancies, with reported sensitivities and specificities &gt; 90% in selected tumour types. Beyond oncology, REIMS technology also shows promise for microbial identification and metabolomic profiling. This review provides a comprehensive overview of the iKnife’s development, underlying principles, clinical validation, and emerging applications, as well as its integration into surgical workflows and the challenges remaining for widespread clinical adoption. Future perspectives include miniaturization, AI-driven spectral interpretation, and expansion into robotic and image-guided surgery. Mass spectrometry ; Workflow ; Clinical Practice ; Identification (biology) ; Metabolomics ; Ambient ionization Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Ion-surface interactions and analysis
Authors: Yuliya Aleksandrova | Maria Kucherenko | Inna Shagina | Luisa Savina | Roman Begunov | A. A. Zubishina | M. E. Neganova
Source: From Chemistry Towards Technology Step-By-Step (article, 2026-03-24)
Field: Chemistry — Organic Chemistry

In silico profiling of 3,4-dihydro-1H-[1,4]oxazino[4,3-a]benzimidazole derivatives: prediction of cytotoxic properties and ADME profile Previously, we synthesised a series of 3,4-dihydro-1H-oxazino[4,3-a]benzimidazole derivatives by functionalising a benzimidazole derivative with an annulated morpholine ring in an SEAr reaction. Some compounds were not previously described in the literature. To assess their potential as pharmaceutical substances, the authors conducted an in silico analysis of the cytotoxic profile and pharmacokinetic parameters. Predicted cytotoxicity revealed a high probability of toxic effects on the nervous and respiratory systems for compounds with toxicity classes II to IV and LD50 in the range of 10-1500 mg/kg. The data obtained suggest a potential antitumour effect of the compounds against gliomas and lung cancer. Analysis of the ADME profile of the compounds showed that all molecules comply with Lipinski's rule. Therefore, further investigation of their antitumour potential in in vitro studies is prospective. ADME ; In silico ; Cytotoxicity ; Morpholine ; In vitro ; Quantitative structure–activity relationship ; Cytotoxic T cell ; Polar surface area Synthesis and biological activity ; Cholinesterase and Neurodegenerative Diseases ; Synthesis and bioactivity of alkaloids
Authors: Vid Kavaš | Carlos Contreras-Martel | Stane Pajk | Damijan Knez | Alexandre Martins | Thomas A. Gould | David I. Roper | Irena Zdovc | Andréa Dessen | Martina Hrast Rambaher | Stanislav Gobec
Source: Journal of Medicinal Chemistry (article, 2026-02-03)
Field: Chemistry — Organic Chemistry

Structure–Activity Relationship and Crystallographic Study of New Monobactams Monobactams, a subclass of β-lactam antibiotics with a monocyclic scaffold, are uniquely resistant to hydrolysis by metallo-β-lactamases, providing a distinct therapeutic advantage. Here, we report an <i>in silico</i>-based structure-activity relationship (SAR) investigation of aztreonam-related monobactams. A focused library of monobactam derivatives was synthesized and evaluated for inhibition of penicillin-binding proteins (PBPs) and antibacterial activity. Ten compounds, including aztreonam, were crystallized with truncated PBP1b from <i>Streptococcus pneumoniae</i>, used as a model PBP. Potent PBP1b inhibitors were developed, although high enzymatic potency was not always reflected in strong antibacterial activity. Certain derivatives showed activity against <i>Staphylococcus aureus</i>, which is typically resistant to monobactams. 2D similarity search identified potent inhibitors active against <i>Escherichia coli</i>, <i>Klebsiella pneumoniae</i>, and <i>Acinetobacter baumannii</i>. Crystal structures revealed previously unrecognized binding interactions, including a halogen bond with a conserved threonine residue, underscoring the potential of these interactions to support the development of more potent PBP inhibitors. Structure–activity relationship ; Antibacterial agent ; Enzyme ; Antibacterial activity ; Threonine ; Hydrolase ; Antibiotics ; Structural similarity ; Enzyme inhibitor ; Depsipeptide Synthesis of β-Lactam Compounds ; Antibiotic Resistance in Bacteria ; Pneumonia and Respiratory Infections