jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dauren Zhambakin | M. Abilev | Almira Zhilkashinova | Aigerim Ichshanova | Leszek Łatka
Source: Molecules (article, 2026-04-02)
Field: Materials Science — Ceramics and Composites

Silicon Carbide Ceramics for Armor Applications: A Review of Sintering Methods and Additive Systems Silicon carbide (SiC) ceramics are among the most attractive materials for lightweight armor because they combine low density (3.0–3.2 g/cm3), high hardness, and high thermal and chemical stability; however, their densification remains challenging because of strong covalent bonding and low self-diffusion. This review analyzes the main sintering routes used for armor-grade SiC ceramics, including solid-state sintering, liquid-phase sintering, hot pressing, gas-pressure sintering, hot-isostatic pressing, ultra-high-pressure sintering, two-step sintering, and spark plasma sintering, together with additive systems based on B, C, Al2O3, Y2O3, MgO, CaO, and rare-earth oxides. Reported data show that solid-state sintering typically requires 2100–2300 °C and yields 90–95% relative density, whereas hot pressing and liquid-phase sintering achieve 96–99% density at lower temperatures, generally with a flexural strength of 350–800 MPa, fracture toughness of 3.5–7.0 MPa·m1/2, and hardness of 20–30 GPa. Among the reviewed methods, spark plasma sintering provides near-theoretical density (≥99%) together with the most favorable combination of strength (up to 850 MPa) and hardness (up to 35 GPa). Overall, liquid-phase sintering and spark plasma sintering offer the most favorable balance between densification, microstructural control, and armor-relevant mechanical performance. Sintering ; Spark plasma sintering ; Silicon carbide ; Relative density ; Ceramic ; Fracture toughness ; Flexural strength ; Armour Advanced ceramic materials synthesis ; High-Velocity Impact and Material Behavior ; Boron and Carbon Nanomaterials Research
Authors: I. V. Rozhansky | Michele Masseroni | Ricardo Pisoni | Suad Alshammari | Xue Li | Thomas Ihn | Klaus Ensslin | James McHugh | Vladimir Fal'ko
Source: ArXiv.org (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

Refined DFT recipe and renormalisation of band-edge parameters for electrons in monolayer MoS$_2$ informed by the measured spin-orbit splitting Conduction band-edge spin-orbit splitting (SOS) in monolayer transition metal dichalcogenides determines a competition between bright and dark excitons and sets conditions for spintronics applications of these semiconductors. Here, we report the SOS measurement for electrons in monolayer MoS$_2$, found from the threshold density, $n_*$, for the upper spin-orbit-split band population, which exceeds by an order of magnitude the values expected from the conventional density functional theory (DFT). Theoretically, half of the observed value can be attributed to the exchange enhancement of SOS in a finite-density electron gas, but explaining the rest requires refining the DFT approach. As the conduction band SOS in MoS$_2$ is set by a delicate balance between the contribution of sulphur $p_x$ and $p_y$ orbitals and $d_{z^2}-d_{xz}$ and $d_{z^2}-d_{yz}$ mixing in molybdenum, we use a DFT+U+V framework for fine-tuning the orbital composition of the relevant band-edge states. An optimised choice of Hubbard U/V parameters produces close agreement with the experimentally observed conduction band SOS in MoS$_2$, simultaneously resulting in the valence-band SOS and the quasi-particle band gap which are closer to their values established in the earlier-published experiments. Electron ; Monolayer ; Density functional theory ; Band gap ; Exciton ; Atomic orbital ; Thermal conduction ; Mixing (physics) 2D Materials and Applications ; Heusler alloys: electronic and magnetic properties ; Chemical and Physical Properties of Materials
Authors: Valentin Semkin | Kirill Kapralov | Ilya Mazurenko | Mikhail Kashchenko | Alexander Igorevich Morozov | Yakov Matyushkin | Dmitry Mylnikov | D. A. Bandurin | Li Lin | Alexey Bocharov | Dmitry Svintsov
Source: arXiv (Cornell University) (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Universal reconstructive polarimetry with graphene-metal infrared photodetectors Recent advent of smart photodetectors, where in-situ tuning of responsivity enables the reconstruction of light intensity, polarization and spectrum by a single device, has revolutionized the field of optoelectronics. So far, most such reconstructive detectors were realized with non-scalable technology of van der Waals stacking. Here, we demonstrate the infrared reconstructive polarimetry with photodetectors based on conventional gated graphene-metal junctions. The reconstruction exploits the gate tuning of polarization contrast, which enables the determination of both infrared power and polarization angle from photovoltage measurements at two different gate voltages. The physics enabling the polarimetry lies in polarization-dependent shift of the electron hot spot near the contact, and the gate tuning of photosensitive barrier width. We further show the universality of polarization reconstruction, i.e. its feasibility with different geometries of the junction, and with graphene of different quality, from boron-nitride encapsulated flakes to the scalable chemical vapor deposited films. Polarimetry ; Detector ; Polarization (electrochemistry) ; Photodetector ; Infrared ; Interferometry Graphene research and applications ; Topological Materials and Phenomena ; Thermal Radiation and Cooling Technologies
Authors: Eppakayala Karunya | P Nagem Shivakumar | Dayakar Thalla | Racharla Harish | R Karthika Hasini | Vasam Siddartha
Source: Journal of Science Engineering Technology and Management Sciences (article, 2026-04-01)
Field: Chemistry — Analytical Chemistry

AI-Driven Biscuit Defect Detection System for Advanced Quality Assurance in Food Manufacturing The quality assurance of biscuits in modern food manufacturing environments plays a vital role in maintaining product consistency, consumer satisfaction, and brand reliability.Conventional manual inspection methods have been widely used; however, they are often inefficient, time-consuming, and susceptible to human errors, especially when identifying minor or subtle defects.To overcome these limitations, an AI-driven biscuit defect detection system was developed to automate the inspection process and enhance quality control standards within the industry.The system utilized advanced image processing techniques combined with artificial intelligence to analyze biscuit images and classify them into categories such as color defects, shape defects, object contamination, and defectfree samples.Initially, the dataset was pre-processed through resizing, normalization, and transformation into numerical formats suitable for model training.Various machine learning models, including Extra Trees Classifier (ETC), Linear Discriminant Analysis (LDA), and Light Gradient Boosting Machine (LGBM), were implemented and evaluated.In addition, a ResNet-based Convolutional Neural Network (CNN) was proposed to automatically extract deep and complex visual features from the images.The performance of all models was assessed using evaluation metrics such as accuracy, precision, recall, and F1-score, along with visualization tools like confusion matrices and ROC-AUC curves.Experimental findings indicated that the ResNet CNN model significantly outperformed traditional approaches, achieving an accuracy of approximately 98.98%.Furthermore, the system was integrated with a user-friendly graphical interface that facilitated dataset handling, model training, real-time prediction, and result visualization.Additional features, including batch processing and remote monitoring through a Telegram bot, enhanced system usability. Visual inspection ; Quality assurance ; Convolutional neural network ; Visualization ; Artificial neural network ; Classifier (UML) ; Object detection ; Machine vision ; Image processing Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Food Supply Chain Traceability
Authors: Xi Zhang | Jufu Jiang | Jian Dong | Ying Wang | Jingbo Cui | Lingbo Kong
Source: Materials (article, 2026-01-25)
Field: Materials Science — Materials Chemistry

Synergistic Optimization of Microstructure and Mechanical Properties of 7075 Aluminum Alloy Sheet via Controlling Rolling Passes and Pass Reduction The pass reduction in hot rolling significantly influences the properties of 7075 alloy sheets, yet its quantitative effect requires systematic investigation. Multi-pass hot rolling experiments with 11% and 16% pass reductions were conducted on forged 7075 alloy. The microstructure, texture evolution, and mechanical properties were analyzed using SEM, EBSD, and mechanical testing. As the total thickness reduction increased, a clear correlation was observed with the enhanced mechanical properties of the hot-rolled 7075 alloy, demonstrated by the concurrent rise in both ultimate tensile strength (UTS) and yield strength (YS). When the total reduction exceeded 60%, the strengthening effect was most pronounced, with UTS and YS reaching 367.09 MPa and 332.82 MPa, respectively. The average grain sizes of 31.49 μm and 27.56 μm were achieved at the 12th pass (11% reduction per pass) and the 8th pass (16% reduction per pass), respectively. Under the condition of 11% reduction per pass, the texture intensity exhibited a non-monotonic trend with increasing passes. T6, T7, and RRA heat treatments were applied to the final rolled plates, and the maximum mechanical properties obtained in the hot-rolled 7075 plate following T6 heat treatment were UTS of 607.5 MPa, YS of 580.9 MPa, and elongation of 13.6%. Ultimate tensile strength ; Elongation ; Alloy ; Microstructure ; Reduction (mathematics) ; Grain size ; Texture (cosmology) ; Aluminium Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties ; Metallurgy and Material Forming
Authors: Indrianita Lionadi | Amir Farokh Payam
Source: ACS Nano Medicine (article, 2026-03-06)
Field: Materials Science — Biomaterials

Nanotechnology in Cancer Therapy: How Nanoparticles Are Shaping the Future of Personalized Treatment Nanotechnology has transformed cancer research, with nanoparticles emerging as powerful platforms for therapy and diagnosis due to their tunable physicochemical properties and biocompatibility. Nanoparticles enable targeted drug delivery, improved pharmacokinetics, and reduced systemic toxicity compared with conventional therapies while also supporting molecular imaging and theragnostic applications. More than eight nanoparticle-based formulations are currently FDA/EMA approved for oncology, highlighting the clinical relevance of nanomedicine. Despite these advances, clinical translation remains limited by biological and technical challenges, including variability in the enhanced permeability and retention effect, insufficient tumor penetration, and low manufacturing scalability. This review primarily focuses on preclinical and translational nanomedicine with selected clinical examples. We summarize recent progress in major nanocarrier classes, including liposomes and polymeric, inorganic, and hybrid nanoparticles, and their roles in cancer therapy. These findings underscore that effective nanotherapeutic strategies must be informed by tumor biology rather than relying on passive targeting alone, that combination and stimuli-responsive platforms represent the most promising therapeutic avenues, and that meaningful clinical translation will depend on overcoming tumor heterogeneity with patient-specific treatment options and achieving robust, reproducible manufacturing. Cancer ; Nanoparticle ; Cancer treatment ; Nanomedicine ; Applications of nanotechnology Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Yi Xu | Chang Xuan | Zaien Ying | Changjiang Wan | Huifang Zhang | Weimin Shi
Source: Recycling (article, 2026-02-19)
Field: Chemistry — Analytical Chemistry

Research on the Method of Near-Infrared Hyperspectral Classification of Cotton-Polyester Blended Waste Fabric Based on Deep Learning Despite the enormous amounts of waste textiles produced by the world’s textile industry’s explosive growth, resource utilization rates are still poor. Cotton/polyester blended waste fabrics make up a sizable share, and sorting them precisely is essential to increasing recycling value and promoting the circular economy in the textile industry. Traditional mechanical and human sorting techniques are ineffective and inaccurate; current spectral analysis algorithms mainly concentrate on quantitative composition prediction and are insufficiently capable of differentiating between waste fabrics with comparable content gradients. To address these challenges, this paper proposes an improved 1DCNN model (Dual-1DCNN-Residual-SE) integrated with Near-Infrared (NIR) hyperspectral imaging technology. This model takes raw spectral data and Savitzky-Golay (SG) smoothing data as dual-channel inputs, introducing residual connections to capture subtle spectral differences between similar fabric categories, and employs SE attention mechanisms to adaptively enhance key features. Comparative experiments with four traditional algorithms—KNN, RF, SVM, and PLS—demonstrate that the proposed model achieves a classification accuracy of 95.94%, surpassing the best traditional algorithm SVM (88.12%) by 7.82%. Ablation experiments confirm each enhanced module’s efficacy. This study achieves high-precision classification of cotton/polyester blended waste fabrics, providing technical support for intelligent sorting of industrial waste fabrics. Sorting ; Hyperspectral imaging ; Textile ; Deep learning ; Residual ; Smoothing ; Key (lock) ; Waste material Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Industrial Vision Systems and Defect Detection
Authors: Yaru Guo | Youxing Liu | Lu Li | Y. Xu | Zheng Lin | Zongqiang Sun | Mingchuan Luo | Shaojun Guo | Xuliang Deng
Source: Proceedings of the National Academy of Sciences (article, 2026-02-06)
Field: Materials Science — Materials Chemistry

Intramolecular noncovalent trans ring restricting free rotation of σ single bond enhances photosynthesis of hydrogen peroxide Solar energy-driven hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) synthesis from atmospheric oxygen and water represents a sustainable and highly promising avenue for the production of this essential chemical. Covalent organic frameworks (COFs) offer a molecular platform for the direct conversion of solar energy to H<sub>2</sub>O<sub>2</sub>, however, they are persistently plagued by the recombination of photogenerated charge carriers, a phenomenon induced by σ-bond rotation under light irradiation, which typically leads to sluggish conversion kinetics and suboptimal efficiency. We herein present a molecular engineering strategy involving the construction of noncovalent trans rings (Nc-TRs) within COFs. This approach entails the precise introduction of noncovalent interactions between donor and acceptor moieties, thereby constraining the free rotation of σ bonds and substantially suppressing the recombination of photogenerated charge carriers. Experimental and theoretical investigations demonstrate that the incorporation of Nc-TR within TAPT-DHBD COFs reduces the molecular dihedral angle from 37.33° to 0°, thereby optimizing molecular coplanarity and prolonging the photogenerated charge carrier lifetime by 820% compared to TAPT-TPD COFs devoid of Nc-TRs. Our findings further reveal that TAPT-DHBD COFs exhibit 5.0-fold and 3.6-fold enhancements in H<sub>2</sub>O<sub>2</sub> photocatalytic conversion kinetics and solar-to-chemical conversion (SCC) efficiency, respectively, relative to TAPT-TPD COFs. We further demonstrate that H<sub>2</sub>O<sub>2</sub> solutions generated in the flow-type photocatalytic system under solar irradiation exhibit a record-high antibacterial efficacy of 10<sup>7</sup> cfu s<sup>-1</sup>, and achieve a 100% wound healing rate within 7 d, markedly outperforming commercial physiological saline. Intramolecular force ; Non-covalent interactions ; Covalent bond ; Dihedral angle ; Hydrogen bond ; Coplanarity ; Kinetics ; Acceptor ; Photocatalysis Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; TiO2 Photocatalysis and Solar Cells
Authors: Lidija Ćurković | Sonja Jozić | Irena Žmak
Source: Materials (article, 2026-03-24)
Field: Materials Science — Polymers and Plastics

Advanced Materials and Processing Technologies This reprint gathers the fourteen research articles originally published in the Special Issue of Materials titled “Advanced Materials and Processing Technologies” [...] Reprint ; Materials processing ; Materials testing Polymer Foaming and Composites ; Injection Molding Process and Properties ; Aerogels and thermal insulation
Authors: Nandini Sipani
Source: International journal of high school research (article, 2026-01-29)
Field: Materials Science — Biomaterials

Application of Liposomes as a Cancer Drug Delivery Vehicle for Various Therapies Cancer is fast approaching as one of the leading causes of mortality in humans.Various treatment plans are available, with conventional ones being surgery, chemotherapy, and radiation, depending on the type of cancer.While effective, such treatment plans not only treat cancer cells but also adversely affect healthy tissues of the body, leading to a long-lasting, sometimes permanent, impact on the quality of life.Liposomes offer an effective drug delivery vehicle that delivers medicines to only specific tumor cells, significantly reducing the effect of these potent drugs on healthy cells.Liposomes consist of hydrophobic phospholipid vesicles with an aqueous core.With the versatility of their design and customization, liposomes are considered useful delivery systems for various types and sizes of drugs.With these nano-structures being adaptable, they can be tailored to address specific cancers.Numerous studies have been published that explore the biosynthesis of liposomes, making them compatible with the bodies of living beings, and several reports are available on the application of liposomes in drug delivery.In this paper, the author will provide a review of the manufacturability of liposomes, followed by an overview of their broad applications, focusing on cancer drug delivery. Liposome ; Drug delivery ; Cancer ; Drug ; Cancer therapy ; Cancer treatment Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Cancer Research and Treatment
Authors: Yuki Kanamaru | Norio Yoshida | Toru Matsui
Source: ACS Omega (article, 2026-01-06)
Field: Chemistry — Physical and Theoretical Chemistry

Comparison Among Solvent Electrostatic Potentials and a Proposal for Improving the Polarizable Continuum Model The polarizable continuum model (PCM) is a widely used solvation model. Although the computational cost of the PCM is low, significant energy errors tend to arise when dealing with charged solutes. We focused on solute-solvent electrostatic interactions and investigated the reaction field distribution. By comparing the reaction field in the PCM with those in other solvation models, such as the molecular dynamics and three-dimensional reference interaction site model, we clarified four PCM features that should be modified: the gradient of the reaction field is very small in water; the position of the reaction field representing hydrogen bonds involving solute protons is shifted; the reaction field peaks representing hydrogen bonds involving solvent protons are merged; and the solute-solvent electrostatic interaction energy is overestimated, especially for anions. These features indicate that modifying the PCM by simply retuning existing parameters is difficult. This study lays the groundwork for developing of subsequent reaction field correction methods. Solvation ; Polarizable continuum model ; Polarizability ; Field (mathematics) ; Solvent effects ; Hydrogen bond ; Molecular dynamics ; Implicit solvation ; Electrostatics Photochemistry and Electron Transfer Studies ; Crystallography and molecular interactions ; Free Radicals and Antioxidants
Authors: Chihou Lei | Yunya Liu
Source: Acta Mechanica (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Some recent advances in microstructures and multi-field coupling behaviors of advanced materials Coupling (piping) ; Microstructure ; Solid mechanics ; Nanomechanics Anodic Oxide Films and Nanostructures ; Heusler alloys: electronic and magnetic properties ; Nonlocal and gradient elasticity in micro/nano structures
Authors: Beatriz Juarez | Peijiang Wang | Daniel Carrica | Jaime Bueno | Alejandro Hernández-Medel | Dirk H. Ortgies | Manuel Rodriguez-Osorio | Daniel Granados | Celia Sousa | Carmen Munuera | Daniel Jaque | Alvaro Blanco | Sol Carretero Palacios | Emma Martín Rodríguez
Source: Research Square (preprint, 2026-03-20)
Field: Materials Science — Materials Chemistry

Far field metal-enhanced luminescence nanothermometry Luminescence ; Excitation ; Photonics ; Photoluminescence ; Near and far field ; Amplifier ; SIGNAL (programming language) ; Optical power ; Power (physics) Luminescence Properties of Advanced Materials ; Nanoplatforms for cancer theranostics ; Thermal Radiation and Cooling Technologies
Authors: Liqin Deng | Yali Sun | Wenjuan Geng | Hui Xu | Ming Wang | Zhigang Fang | Qi Liu | Fenfei Chu
Source: Agriculture (article, 2026-03-05)
Field: Chemistry — Analytical Chemistry

Prediction of Quality and Ripeness in ‘Weidi’ and ‘Fengweimeigui’ Apricot–Plum Using Near-Infrared Spectroscopy and Machine Learning Analysis To meet consumer demand for high-quality fruit and replace traditional subjective assessment methods, there is a growing interest in objective, quantitative, and non-destructive testing techniques within the agricultural and food industries. This study explores the integration of near-infrared (NIR) spectroscopy with machine learning for the quality detection of apricot–plum hybrids, aiming to provide a rapid and efficient technical approach. Two cultivars, ‘Fengweimeigui’ and ‘Weidi’, were selected for analysis. The relationships between various quality attributes were analyzed using analysis of variance (ANOVA) and Pearson correlation. Raw spectral data were preprocessed using Savitzky–Golay (SG) smoothing, and principal component analysis (PCA) was employed to reduce the high dimensionality of the spectral data. The scores of the first 15 principal components (PCs) were extracted as input features for the subsequent models. A comparative study was conducted between backpropagation neural network (BPNN) and support vector machine (SVM) models. The results indicated that during the color-break period, significant differences existed across all quality indicators except for dry matter content, with significant correlations observed among these parameters. The results demonstrated that BPNN achieved the best predictive performance for total phenols content, peel L*, peel b*, vitamin C content, flavonoids content, soluble solids content, soluble sugars content, and soluble protein content in ‘Weidi’ and ‘Fengweimeigui’ from the color-turning to the ripening stages. The RP2 values for these indicators were 0.968, 0.966, 0.950, 0.939, 0.939, 0.923, 0.921, and 0.905, respectively, with residual predictive deviation (RPD) values exceeding 3.0. These findings indicate that near-infrared (NIR) spectroscopy is a feasible tool for the rapid detection of plum–apricot quality. However, the model performance for Flesh a* requires further optimization. In conclusion, the combination of NIR spectroscopy and machine learning enables the rapid, efficient, and non-destructive quality assessment of plum–apricot hybrids, providing robust technical support for maturity prediction and quality control in commercial production. Principal component analysis ; Ripeness ; Artificial neural network ; Support vector machine ; Backpropagation ; Quality (philosophy) ; Chemometrics ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Plant Disease Management Techniques