jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kristjan Eimre | Matthew L. Evans | Bud Macaulay | Xing Wang | Jusong Yu | Nicola Marzari | Gian-Marco Rignanese | Giovanni Pizzi
Source: arXiv (Cornell University) (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

optimade-maker: Automated generation of interoperable materials APIs from static data Atomistic structural data are central to materials science, condensed matter physics, and chemistry, and are increasingly digitised across diverse repositories and databases. Interoperable access to these heterogeneous data sources enables reusable clients and tools, and is essential for cross-database analyses and data-driven materials discovery. Toward this aim, the OPTIMADE (Open Databases Integration for Materials Design) specification defines a standard REST API for atomistic structures and related properties. However, deploying and maintaining compliant services remains technically demanding and poses a significant barrier for many data providers. Here, we present optimade-maker, a lightweight toolkit for the automated generation of OPTIMADE-compliant APIs directly from raw atomistic structure and property data. The toolkit supports a wide range of raw datasets, enables conversion to a standardised OPTIMADE data representation, and allows for rapid deployment of APIs in both local and production environments. We further demonstrate it through an automated service on the Materials Cloud Archive, which automatically creates and publishes OPTIMADE APIs for contributed datasets, enabling immediate discoverability and interoperability. In addition, we implement data transformation pipelines for the Cambridge Structural Database (CSD) and the Inorganic Crystal Structure Database (ICSD), enabling unified access to these curated resources through the OPTIMADE framework. By lowering the technical barriers to interoperable data publication, optimade-maker represents an important step toward a scalable, FAIR materials data ecosystem integrating both community-contributed and curated databases. Interoperability ; Discoverability ; Cloud computing ; Raw data ; File format ; SOAP ; Software deployment ; Data access ; Standardization ; Data exchange Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Scientific Computing and Data Management
Authors: K. W. Yasintha | B.M. Jinendra
Source: Journal of the National Science Foundation of Sri Lanka (article, 2026-01-30)
Field: Chemistry — Analytical Chemistry

Use of near infrared spectroscopy for rapid and non-destructive identification of morphologically similar seeds of different paddy varieties [Oryza sativa L.] Rice production systems and breeding programmes often require handling large numbers of paddy samples, where maintaining sample identity is critical. Conventional identification methods, such as morphological and molecular analyses, are reliable but laborious and time-consuming. To address this, the present study evaluated the use of near infrared (NIR) spectroscopy (588–1091 nm) combined with soft independent modeling of class analogy (SIMCA) for rapid discrimination of paddy seed varieties. A total of 750 seed samples representing five varieties (Bg300, Bg352, Bg357, Bg359, and Bw363) were scanned, and SIMCA prediction models were developed using Pirouette 3.11 software. Model parameters were optimized to enhance classification performance. The influence of moisture content (MC) at four levels (10%, 12%, 15%, and 23%) during seed handling was also investigated. The SIMCA models achieved variety-specific classification accuracies of 89% (Bg300), 83% (Bg352), 88% (Bg357), 87% (Bg359), and 84% (Bw363). When averaged across all varieties, correct identification rates were 82% at 10% MC, 83% at 12% MC, 88% at 15% MC, and 92% at 23% MC. The results confirm an increasing trend in predictive accuracy with elevated MC, attributable to stronger variety-specific water-related absorbance features in the NIR region. Overall, the study demonstrates that NIR spectroscopy combined with SIMCA provides a rapid, non-destructive, and feasible approach for varietal identification of paddy seeds, particularly effective under equalized moisture levels. This method holds strong potential for field-level application, offering faster and more reliable variety authentication compared to conventional practices. Near-infrared spectroscopy ; Absorbance ; Moisture ; Identification (biology) ; Water content Spectroscopy and Chemometric Analyses ; Rice Cultivation and Yield Improvement ; Smart Agriculture and AI
Authors: Volodymyr Kukhar | Andrii Kostryzhev | O. Dykha | Oleg Makovkin | Ihor Kuziev | Roman Vakulenko | Viktoriia Kulynych | Khrystyna Malii | Eleonora Butenko | Natalia Hrudkina | Oleksandr Shapoval | Sergiu Mazuru | Oleksandr Hrushko
Source: Metals (article, 2026-01-25)
Field: Materials Science — Materials Chemistry

Technological and Chemical Drivers of Zinc Coating Degradation in DX51d+Z140 Cold-Formed Steel Sections This study investigates the technological and chemical causes of early zinc-coating degradation on cold-formed steel sections produced from DX51D+Z140 galvanized coils. Commercially manufactured products exhibiting early corrosion symptoms were used in this study. The entire processing route, which included strip preparation, cold rolling, hot-dip galvanizing, passivation, multi-roll forming, storage, and transportation to customers, was analyzed with respect to the residual surface chemistry and process-related deviations that affect the coating integrity. Thirty-three specimens were examined using electromagnetic measurements of coating thickness. Statistical analysis based on the Cochran’s and Fisher’s criteria confirmed that the increased variability in zinc coating thickness is associated with a higher susceptibility to localized corrosion. Surface and chemical analysis revealed chloride contamination on the outer surface, absence of detectable Cr(VI) residues indicative of insufficient passivation, iron oxide inclusions beneath the zinc coating originating from the strip preparation, traces of organic emulsion residues impairing wetting and adhesion, and micro-defects related to deformation during roll forming. Early zinc coating degradation was shown to result from the cumulative action of multiple technological (surface damage during rolling, variation in the coating thickness) and environmental (moisture during storage and transportation) parameters. On the basis of the obtained results, a methodology was proposed to prevent steel product corrosion in industrial conditions. Galvanization ; Coating ; Zinc ; Corrosion ; Degradation (telecommunications) ; Wetting ; Chemical composition ; Deformation (meteorology) Corrosion Behavior and Inhibition ; Metal Forming Simulation Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Mike Hardy | Pooja P. Kanade | Emma Buchan | Pola Goldberg Oppenheimer | Cillian P. T. McPolin | Robert Bowman
Source: Lab on a Chip (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Miniaturisation of Raman spectroscopy systems: from benchtop to backpocket The global portable spectroscopy market is rapidly expanding, with optical technologies composing the greatest market share. One prominent light-based technology is Raman spectroscopy, which confers benefits in terms of high selectivity and thus untargeted detection and multiplexing capabilities. Alongside this, plasmon-enhanced Raman technologies can provide ultra-low, even single molecule, detection. In this article, we analyse the current trends in the miniaturisation of Raman systems, including constituent components, evaluating current market needs, and providing a prospective on likely developments within Raman spectroscopy systems in the coming years. In particular, the progress from handheld Raman systems, which have surged in popularity since 2010 in a plethora of application spaces, to fully integrated on-chip Raman devices, is surveyed. Such palm-sized devices offer potentially easy integration into, for example, consumer white goods at home, and lightweight drone systems, and thus could transform the portable sensing landscape. Raman spectroscopy ; Multiplexing ; Consumer market ; Surface-enhanced Raman spectroscopy ; Current (fluid) Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Photonic and Optical Devices
Authors: Chenxi Zhang | Mengya Pan | Qiankai Hong | Shengyuan Shen | Conghui Guo | Yanpeng Shi | Yifei Zhang
Source: Sensors (article, 2026-03-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Evolutionary Diffusion Framework Empowering High-Performance Freeform Terahertz Metasurface Sensing Metasurfaces offer an unprecedented avenue to facilitate light-matter interactions. However, traditional design methodologies rely on computationally intensive trial-and-error processes. Moreover, existing deep learning (DL) schemes are predominantly hindered by their massive data requirements and limited exploration of freeform design spaces. To overcome these challenges, a multi-model-driven generative-evolutionary strategy (GES) is proposed, for the on-demand inverse design of bespoke Terahertz (THz) metasurface sensors. By leveraging a Conditional Diffusion Generator (CDG) and an Attention-Enhanced Residual Network (ARN), this framework enables the exploration of an expansive design space encompassing 2100 possible configurations. The GES effectively overcomes the data bottleneck by selectively generating high-potential data in stages. Full-wave simulations confirm that the inversely designed metasurfaces exhibit high-contrast resonance peaks and exceptional sensitivity across low, mid, and high THz bands. This work provides a versatile paradigm for the efficient design of high-performance functional metamaterials, significantly accelerating the advancement of application-specific THz sensing. Terahertz radiation ; Bottleneck ; Bespoke ; Expansive ; Sensitivity (control systems) ; Generator (circuit theory) ; Common emitter ; Scheme (mathematics) Metamaterials and Metasurfaces Applications ; Terahertz technology and applications ; Plasmonic and Surface Plasmon Research
Authors: A. P. Malyushevskaya | Olena Mitryasova | Michał Koszelnik | Ivan Šalamon | Andrii Mats | Andżelika Domoń | Eleonora Sočo
Source: Preprints.org (preprint, 2026-04-09)
Field: Materials Science — Materials Chemistry

Sustainable Iodometric Assessment of Electric Discharge Cavitation for Eco-Friendly Water Purification Electric discharge cavitation is an effective method for water treatment that combines physical and chemical effects within a single process. It enables water disinfection, extraction acceleration, dispersion of solid particles, and enhancement of porous material permeability. Compared to conventional chemical treatment, it reduces the demand for reagents and minimizes secondary pollution. This new and developing technology significantly contributes to the preservation of natural aquatic ecosystems by providing a sustainable alternative to traditional decontamination methods, thereby reducing the overall anthropogenic pressure on the environment. This study focuses on developing a reliable method for assessing electric discharge cavitation intensity and controlling water purification processes. The proposed approach is based on the oxidation of iodide ions to molecular iodine by reactive species generated during electric discharge cavitation. The adapted iodometric method is sensitive, reproducible, and does not require complex optical or acoustic equipment. Experimental results confirmed that iodometry provides accurate evaluation of cavitation intensity, allowing control of specific energy consumption and optimization of treatment parameters. Optimal operating conditions were established to control the water processing by electric discharge cavitation: stainless-steel electrodes, specific input energy not exceeding 280 kJ·L-1, the presence of a free liquid surface in the working chamber, and a discharge pulse frequency below 10 Hz. The proposed method supports the development of energy-efficient, low-waste technologies for wastewater and natural water treatment and facilitates the integration of electric discharge systems into existing water treatment infrastructure, particularly under resource-limited conditions. Iodometry ; Water treatment ; Portable water purification ; Electric discharge ; Reagent ; Wastewater ; Electric energy ; Cavitation Ultrasound and Cavitation Phenomena ; Advanced oxidation water treatment ; Water Treatment and Disinfection
Authors: Ganesh Chandrasekaran
Source: International Journal on Science and Technology (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Heat Transfer Mechanisms and Thermal Control Strategies in Vacuum Based Semiconductor Process Equipment- A Comprehensive Engineering Review Thermal management and heat transfer engineering are foundational to the performance, reliability, and yield of semiconductor manufacturing equipment [1]. As device geometries shrink and substrate sizes increase, maintaining precise temperature control becomes increasingly challenging and increasingly critical. Semiconductor processes such as CVD, PECVD, ALD, RTP, PVD, and plasma etch depend on tightly regulated thermal environments to ensure uniform film growth, stable plasma behavior, controlled chemical reactions, and defect free device fabrication [2]. This paper examines the principles of heat transfer in vacuum environments, the engineering strategies used to achieve thermal uniformity, and the hardware innovations that enable advanced thermal control in modern semiconductor tools. It highlights the consequences of poor thermal management and outlines future directions for next generation equipment design. Heat transfer ; Thermal management of electronic devices and systems ; Semiconductor device fabrication ; Fabrication ; Process (computing) ; Semiconductor ; Thermal ; Semiconductor device Thermal properties of materials ; Heat Transfer and Boiling Studies ; Silicon and Solar Cell Technologies
Authors: Anqi Liu | Zibo Meng | Jiayi Ma | Jinfeng Liu | Haonan Wang | Yingbo Li | Yufan Yang | Na Liu | Ming Hui | Dandan Zhai | Peng Li
Source: Foods (article, 2026-01-23)
Field: Chemistry — Analytical Chemistry

Non-Destructive Geographical Traceability and Quality Control of Glycyrrhiza uralensis Using Near-Infrared Spectroscopy Combined with Support Vector Machine Model Licorice (<i>Glycyrrhiza uralensis</i> Fisch.) is a widely used natural sweetener and functional food ingredient. Its sensory profile, nutritional value, and bioactive composition are strongly affected by geographical origin and cultivation mode, particularly the distinction between wild and cultivated resources. Consequently, developing a rapid and robust method for origin traceability is imperative for rigorous quality control and product standardization. This study proposes a non-destructive traceability framework integrating near-infrared (NIR) spectroscopy with a Support Vector Machine (SVM). The method's validity was rigorously evaluated using a comprehensive dataset collected from China's three primary production regions-Gansu Province, the Inner Mongolia Autonomous Region, and the Xinjiang Uygur Autonomous Region, encompassing both wild and cultivated resources. Experimental results demonstrated that the proposed framework achieved an overall classification accuracy exceeding 99%. The results show that the proposed method offers a rapid, efficient, and environmentally friendly analytical tool for the quality assessment of licorice, providing a scientific basis for rigorous quality control and standardization in the functional food industry. Traceability ; Support vector machine ; Standardization ; Quality (philosophy) ; Glycyrrhiza uralensis ; Food quality ; Product (mathematics) Spectroscopy and Chemometric Analyses ; Pharmacological Effects of Natural Compounds ; Metabolomics and Mass Spectrometry Studies
Authors: Guanzi Zhou | Haotian Ji | Rongyu Pan | Xiuli Yang | Suhui Zhao | Lili Yang | Xiaohan Shang | Huijie Zhang | Hanchi Zhang | Xiaojun Liu | Yuanchun Ma | Xujun Zhu | Jie Jiang | WANPING FANG
Source: Plants (article, 2026-03-31)
Field: Chemistry — Analytical Chemistry

Integrating Hyperspectral Data and Deep Learning for Non-Destructive Prediction of Tea Quality Parameters Across Different Physical States of Tea Leaves and Growth Periods Achieving rapid and non-destructive assessment of tea quality is essential for intelligent tea production and quality control. In this study, an integrated hyperspectral and deep learning framework was developed to estimate tea quality constituents across seasons and physical states. Samples included field fresh leaves, dried tea leaves, and tea powder, were collected in spring, summer, and autumn. Tea polyphenols and catechins were predicted using original reflectance, harmonic features, and wavelet features fused into multi-domain indices. Extreme gradient boosting, Gaussian process regression, and convolutional neural networks (CNN) were systematically compared to construct the quality estimation models. The result showed that three-feature indices consistently outperformed two-feature indices, yielding R<sup>2</sup> from 0.48 to 0.71. CNN achieved the best overall performance among the three modeling approaches, with its optimal accuracy obtained for tea powder samples in autumn, yielding R<sup>2</sup> values of 0.81 and 0.76 for tea polyphenols and catechins, respectively. This framework provides an accurate, non-destructive tool for tea quality evaluation and traceability, offering technical support for intelligent agriculture and quality control across the tea industry chain. Hyperspectral imaging ; Camellia sinensis ; Deep learning ; Quality (philosophy) ; Artificial neural network ; Polyphenol ; Convolutional neural network Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Beatriz G. del Rio | Joël Martín Dalmas | Nonia Vaquero Sabater | D. J. González | L. González
Source: Journal of Physics Condensed Matter (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Structure and motion of tritium and helium in the breeding-blanket-relevant liquid lithium–lead eutectic alloy: <i>ab-initio</i> molecular dynamics simulations The lead-rich liquid lithium-lead eutectic alloy, Li<sub>17</sub>Pb<sub>83</sub>, is one of the candidates to be used in breeding blanket modules of future fusion reactors where tritium breeding is essential to provide the necessary fuel for fusion. It has several properties that favour its use, such as the breeding capacity of Li, the neutron multiplication capacity of Pb, the ease of circulation for off-site tritium recovery, and the capability to, at least partly, refrigerate the system. It is therefore important to understand the properties of the tritium generated in the breeding reactions, and its effects on the hosting liquid. The same applies to helium nuclei that are generated in a 1:1 ratio to tritium in the breeding reactions. We have performed first principles molecular dynamics simulations to study the structural changes observed in liquid Li<sub>17</sub>Pb<sub>83</sub>when tritium or helium is added. In one set of simulations we have made calculations for several amounts of tritium, with molar concentrations ranging from 0.20 to 0.03, without any He atoms. In the other set of simulations we have included helium atoms with molar concentration 0.11, and no tritium. Tritium atoms are found to bind preferentially with Li, modifying substantially the Li-Li correlation functions. We also observe the presence of long-lasting di-tritium molecules when tritium concentration is not too low, which also tend to bind to Li atoms. The velocity autocorrelation functions of tritium, Li and Pb are evaluated, and analysed in order to obtain the corresponding vibrational properties of the different species. Helium atoms tend to aggregate together forming a cluster whose characteristics are reported, together with the correlation functions of He atoms with Li and Pb. The motion of He atoms within this cluster is found to be sub-diffusive, while an estimate of He mobility outside the aggregate is also given. Tritium ; Eutectic system ; Blanket ; Helium ; Breeder (animal) ; Molecular dynamics ; Fusion power ; Neutron Fusion materials and technologies ; Thermodynamic and Structural Properties of Metals and Alloys ; Hydrogen Storage and Materials
Authors: Laihu Peng | Zhiwen Wu | Yubao Qi | Jianqiang Li | Xin Ru
Source: Applied Sciences (article, 2026-03-10)
Field: Materials Science — Polymers and Plastics

Multi-Objective Optimization and Entropy-Weighted Technique for Order of Preference by Similarity to Ideal Solution Decision Making for Cotton Sliver Drawing Process Based on Particle Swarm Optimization–Backpropagation Neural Network and Non-Dominated Sorting Genetic Algorithm II In recent years, vortex spinning has garnered significant attention owing to its high efficiency and superior yarn quality. However, the drafting process involves multiple interrelated parameters, and different combinations of parameters can considerably influence subsequent spinning performance. To address this, the present study introduces a novel hybrid optimization algorithm to enhance spinning quality by rationalizing the coordination of drafting parameters. First, orthogonal experiments were conducted with the draft ratio and roller center distance as variables, using the mean grayscale value and grayscale standard deviation of the post-experiment silver images as multi-objective functions to evaluate drafting effectiveness. Subsequently, a regression model between drafting parameters and drafting outcomes was constructed using the Particle Swarm Optimization–Backpropagation Neural Network (PSO-BP) algorithm, followed by multi-objective optimization via the Non-dominated Sorting Genetic Algorithm II (NSGA-II) genetic algorithm to obtain a Pareto-optimal solution set. Finally, the entropy-weighted Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method was applied to comprehensively evaluate the Pareto-optimal set and determine the optimal combination of process parameters. The results demonstrate that, under the optimal parameter combination, the deviation between the measured quality indicators of the drafted sliver and the predicted values remains within 6%, confirming the effectiveness of the proposed model as a viable approach for optimizing drafting parameter configurations. Particle swarm optimization ; Sorting ; Genetic algorithm ; Ideal solution ; Spinning ; Similarity (geometry) ; Artificial neural network ; Process (computing) ; Grayscale ; Set (abstract data type) Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Research in Cotton Cultivation