jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tejas Chaudhari | Tushar A. Shinde | Leena P. Shirsath | Sandip P. Patil
Source: International Journal of Environment Agriculture and Biotechnology (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Smart Extremozymes: The Next-Generation Biocatalysts for Sustainable Industrial Wastewater Management Industrial wastewater treatment face various challenges like high pollutant load, extreme environmental conditions and the need for cost-effective and eco-friendly solutions. These challenges could be sustainably managed using biocatalysts like enzymes. Although enzymes are sustainable approach towards this, but they often act futile due to the extreme conditions present in the wastewater. Extremozymes are specialized enzymes with the ability to function under extreme conditions and offer a promising approach to address these challenges. The types of extremozymes explored in this study include thermozymes, psychrozymes, halozymes, alkalozymes and acidozymes. Their mechanism of action enables the efficient breakdown of pollutants making them highly suitable for industrial applications. Extremozymes have proven effective in treating effluents from industries such as textiles, pulp and paper, pharmaceuticals and food. These enzymes provide high stability and efficiency in harsh wastewater environments. Moreover, these enzymes also offer advantages such as reduced operational costs and minimized environmental footprints. Recent advancements such as AI-driven enzyme design, synthetic biology and nano-immobilization strategies have further enhanced extremozyme performance which leads to the development of intelligent and reusable “bionanoextremozymes” capable of functioning efficiently under fluctuating industrial conditions. By leveraging extremozymes, industries can adopt a more sustainable and efficient approach to wastewater treatment. This review highlights their potential to revolutionize industrial effluent management, paving the way for sustainable, environmentally friendly and economically viable wastewater treatment solutions. Sustainability ; Effluent ; Environmentally friendly ; Wastewater ; Industrial wastewater treatment ; Pollutant Advanced Nanomaterials in Catalysis ; Enzyme Catalysis and Immobilization ; Graphene and Nanomaterials Applications
Authors: M. Tacke | M. Busch | Kevin Linka | Christian J. Cyron | Roland C. Aydin
Source: Frontiers in Artificial Intelligence (article, 2025-11-05)
Field: Materials Science — Materials Chemistry

Functional partitioning through competitive learning Datasets often incorporate various functional patterns related to different aspects or regimes, which are typically not equally present throughout the dataset. We propose a novel partitioning algorithm that utilizes competition between models to detect and separate these functional patterns. This competition is induced by multiple models iteratively submitting their predictions for the dataset, with the best prediction for each data point being rewarded with training on that data point. This reward mechanism amplifies each model's strengths and encourages specialization in different patterns. The specializations can then be translated into a partitioning scheme. We validate our concept with datasets with clearly distinct functional patterns, such as mechanical stress and strain data in a porous structure. Our partitioning algorithm produces valuable insights into the datasets' structure, which can serve various further applications. As a demonstration of one exemplary usage, we set up modular models consisting of multiple expert models, each learning a single partition, and compare their performance on more than twenty popular regression problems with single models learning all partitions simultaneously. Our results show significant improvements, with up to 56% loss reduction, confirming our algorithm's utility. Set (abstract data type) ; Modular design ; Stability (learning theory) ; Training set ; Competition (biology) ; Point (geometry) ; Limiting ; Data set ; Regression Machine Learning in Materials Science ; Machine Learning and Data Classification ; Domain Adaptation and Few-Shot Learning
Authors: Silvana Nisgoski | Helena Cristina Vieira | Joielan Xipaia dos Santos | Graciela Inês Bolzón de Muñiz
Source: Forest Systems (article, 2025-12-05)
Field: Chemistry — Organic Chemistry

NIR spectroscopy for tree species identification based on different samples: a review Near-infrared spectroscopy is applied in a wide range of forestry studies. Among them is tree species identification based on leaf, wood, bark and seed samples. There are different mathematical pretreatments applied to spectra and a great range of processing or classification methods, as well as variation in the accuracy/precision of the results. This paper is a review of the main manuscripts found by searching the databases of Google and the Office to Coordinate Improvement of University Personnel (CAPES), using the species names along with identification/distinction/discrimination and NIR as keywords. It describes the state of the art regarding the use of near-infrared techniques for tree species identification, from 1969 to the first months of 2024, encompassing the use of different samples, devices and main analytic methods. Bark (sound) ; Tree (set theory) ; Identification (biology) ; Range (aeronautics) ; Species identification ; Spectroscopy Wood and Agarwood Research ; Spectroscopy and Chemometric Analyses ; Wood Treatment and Properties
Authors: Zoe Marie B. Arquiza | Chae Lyul Lim | Phoemella Tricxy G. Mamaclay
Source: International Journal of Research Publication and Reviews (article, 2025-04-01)
Field: Chemistry — Inorganic Chemistry

Exploring Marang Seed (Artocarpus Odoratissimus) Extract as an Immunosuppressant: An In Silico Approach to Transplant Rejection and Autoimmune Disease Management Over the last 30 years, autoimmune diseases have significantly increased, affecting more than 10% of the global population.Some of the most prevalent conditions are multiple sclerosis (MS) and type 1 diabetes (T1D).Apart from that, 15% of kidney transplant patients develop acute rejection that causes inflammation within the first year.In response to this, healthcare providers prescribe immunosuppressants; however, they can lead to problems such as hypertension, infections, hyperlipidemia, and even cancer.This study examines Marang seed (Artocarpus odoratissimus) as a potential alternative to traditional immunosuppressants because of its high phenolic content, which includes flavonoids that are known to modulate immune responses.Using molecular docking, six phytochemicals from marang seed were docked with proteins 1A3Q, 2AZ5, and 1VLK.PyMol, Autodock, Chimera, and LigPlot were used for analysis.Among the six phytochemicals, artocarpin exhibited the lowest binding affinity, which indicates a successful binding.It is then followed by quercetin and catechin.These findings indicate that Marang seed could be a promising natural alternative to existing immunosuppressants. In silico ; Traditional medicine ; Artocarpus ; Moraceae ; Disease ; Calcineurin ; Biology ; Medicine ; Immunology ; Transplantation ; Botany ; Pathology ; Internal medicine ; Biochemistry Coconut Research and Applications ; Bioactive natural compounds
Authors: Emre Alan
Source: Journal of Sustainable Technologies and Materials (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

ENHANCING ECO-EFFICIENT LABORATORY-SCALE STEEL PRODUCTION VIA INNOVATIVE PROCESS AND EQUIPMENT DESIGN WITH CIRCULAR RESOURCE UTILIZATION &amp;lt;p&amp;gt;This study presents the development of an eco-efficient laboratory-scale production route for long&amp;lt;br /&amp;gt;steel products, focusing on sustainable casting and hot deformation processes. Conventional&amp;lt;br /&amp;gt;casting using 80x80 mm molds and gravity filling generated significant shrinkage defects, leading&amp;lt;br /&amp;gt;to scrap generation, repeated melting, and reduced material efficiency. Alternative mold designs&amp;lt;br /&amp;gt;with a 15&amp;amp;deg; inclination and exothermic feeders were evaluated via simulation software, minimizing&amp;lt;br /&amp;gt;defects and enabling &amp;amp;Oslash;70 mm castings with 20% less raw material. A new laboratory-scale hot&amp;lt;br /&amp;gt;rolling unit replaced hydraulic presses, achieving the target &amp;amp;Oslash;29 mm round cross-section in a single&amp;lt;br /&amp;gt;cycle with a 0.83 reduction ratio, increasing specimen yield and reducing energy consumption.&amp;lt;br /&amp;gt;Equipment selection prioritized the reuse of idle industrial motors and gearboxes, aligning with&amp;lt;br /&amp;gt;circular economy principles. Mechanical testing and microstructural outcomes obtained from the&amp;lt;br /&amp;gt;new process were closer to industrial production standards, demonstrating not only enhanced&amp;lt;br /&amp;gt;process yields but also improved product quality. Overall, the combined process modifications&amp;lt;br /&amp;gt;reduced scrap, minimized energy use, and leveraged existing resources, demonstrating a practical,&amp;lt;br /&amp;gt;sustainable and eco-friendly approach for laboratory-scale steel production.&amp;lt;/p&amp;gt; Scrap ; Reuse ; Raw material ; Process (computing) ; Shrinkage ; Production (economics) ; Casting ; Idle ; Yield (engineering) ; Mold Material Selection and Properties ; Aluminum Alloy Microstructure Properties ; Metallurgical Processes and Thermodynamics
Authors: W. J. Kim | Tae Young Kim | J. Do | H. Ma | Hee-Sun Yoon | Kanghee Won
Source: Materials (article, 2025-01-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Stripe Noise Removal in Blazed Grating Generation for Electrically Tunable Beam Deflector In this paper, we demonstrate a blazed phase grating to achieve tunable beam steering and propose a novel algorithm to reduce the stripe noise in wrapped phase. To control the diffraction angle to steer light to the desired direction, an electrically tunable transmission-type beam deflector based on liquid crystals is introduced, and electric fields are applied to the patterned indium tin oxide electrodes to change its phase retardation. Two different 2π phase-wrapping methods are applied to obtain various diffraction angles within the minimum cell-gap, and the method of equal interval of phase achieves a worthwhile diffraction efficiency compared to the methods based on equal interval of diffraction angle. The proposed method is able to completely eliminate the stripe noise in all steering angles that helps to improve the diffraction efficiency. Materials science ; Diffraction ; Optics ; Beam steering ; Diffraction efficiency ; Blazed grating ; Phase (matter) ; Grating ; Beam (structure) ; Optoelectronics ; Indium tin oxide ; Diffraction grating ; Noise (video) ; Transmission (telecommunications) ; Physics ; Computer science ; Layer (electronics) ; Nanotechnology ; Telecommunications Liquid Crystal Research Advancements ; Photonic and Optical Devices ; Advanced Optical Imaging Technologies
Authors: Abdulaziz Abogoda | J. A. Sauls
Source: ArXiv.org (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Trapping and Tunneling of Hydrogen, Deuterium and Oxygen in Niobium We investigate isolated O-H and O-D pairs trapped in BCC Nb using a machine-learning interatomic potential (MLIP) trained to density-functional theory (DFT). The MLIP enables large-supercell analysis and identification of trapping sites within BCC Nb, as well as efficient mapping of three-dimensional (3D) potential-energy surfaces. In addition to the pair of tetrahedral``face'' sites previously identified based on DFT, we identify a lower-energy pair of ``edge'' trapping sites and confirm the stability of H and D at these trapping sites with DFT. We solve the Schrödinger equation for H and D in the 3D potential that surrounds the trapping sites. Solutions based on the static-lattice limit yield tunnel splittings in the range $J/h \in\{3-100\}$ GHz for both trapping sites. Trapping ; Niobium ; Quantum tunnelling ; Yield (engineering) ; Deuterium ; Range (aeronautics) ; Interatomic potential Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Quantum many-body systems
Authors: Zhongming Zhang | Xiaojuan Xu
Source: Zeitschrift für Kristallographie - New Crystal Structures (article, 2025-12-31)
Field: Chemistry — Inorganic Chemistry

Crystal structure of ( <i>E</i> )-3′,6′-bis(diethylamino)-2-((1-(thiazol-2-yl)ethylidene)amino)spiro[isoindoline-1,9′-xanthen]-3-one, C <sub>33</sub> H <sub>35</sub> N <sub>5</sub> O <sub>2</sub> S Abstract C 33 H 35 N 5 O 2 S, monoclinic, P 2 1 / n (no. 14), a = 17.6679(5) Å, b = 9.2426(2) Å, c = 18.9866(5) Å, β = 106.604(3) ° , V = 2971.18(15) Å 3 , Z = 4, R gt ( F ) = 0.0467, wR ref ( F 2 ) = 0.1335, T = 100 K. Crystal structure ; Crystal (programming language) ; X-ray crystallography ; Orthorhombic crystal system ; Hexagonal crystal system Crystal structures of chemical compounds ; Multicomponent Synthesis of Heterocycles ; Supramolecular Chemistry and Complexes
Authors: Robert D. Schaller | Eric Johnson | Mark Sprowls | Leyla Sabet | W Alleman | R Shaw | Raymond Schaerf | Shunmuga Sundaram Ponnusamy | Vish Charan | Pugazhendhi Vijayaraman
Source: Heart Rhythm (article, 2025-04-01)
Field: Chemistry — Electrochemistry

PO-FP-039 STYLET-DRIVEN LEAD EXTRACTION OF LEFT BUNDLE BRANCH AREA PACING LEADS IN A CHRONIC PRECLINICAL MODEL Stylet ; Medicine ; Bundle ; Lead (geology) ; Extraction (chemistry) ; Cardiology ; Internal medicine ; Surgery ; Chromatography Electrochemical Analysis and Applications
Authors: Ludovico Musenich | Daniele Origo | Filippo Gallina | Markus J. Buehler | Flavia Libonati
Source: arXiv (Cornell University) (preprint, 2025-01-02)
Field: Materials Science — Biomaterials

Revealing diatom-inspired materials multifunctionality Diatoms have been described as nanometer-born lithographers because of their ability to create sophisticated three-dimensional amorphous silica exoskeletons. The hierarchical architecture of these structures provides diatoms with mechanical protection and the ability to filter, float, and manipulate light. Therefore, they emerge as an extraordinary model of multifunctional materials from which to draw inspiration. In this paper, we use numerical simulations, analytical models, and experimental tests to unveil the structural and fluid dynamic efficiency of the Coscinodiscus species diatom. Then we propose a novel 3D printable multifunctional biomimetic material for applications such as porous filters, heat exchangers, drug delivery systems, lightweight structures, and robotics. Our results demonstrate the role of Nature as a material designer for efficient and tunable systems and highlight the potential of diatoms for engineering materials innovation. Additionally, the results reported in this paper lay the foundation to extend the structure-property characterization of diatoms. Diatom ; Nanotechnology ; Business ; Computer science ; Materials science ; Geology ; Oceanography Diatoms and Algae Research
Authors: Faouzi Khedher | Thouraya Hamdi | Boubaker Jaouachi | Mohamed Jmali
Source: Autex Research Journal (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Predicted sewing thread consumption using neural network method based on the physical and structural parameters of knitted fabrics Abstract This article presents an experimental study on the influence of various parameters on sewing thread consumption. Four knitted samples, featuring different structures and thicknesses, were tested by sewing two- and three-layer using chain stitch type 401. This work allows for examining the effects of the sewing machine foot pressure height, surface mass, fabric thickness, and number of layers sewn on thread consumption. It was concluded that lower foot pressure results in a significant increase in the amount of thread used. In addition, accurately predicting thread consumption makes it easier to control the stock level of inventories, which is important for effective supply chain management. With optimized inventory, companies will save on storage expenses and minimize the downtime between operations, resulting in increased productivity. The combined application of neural nets and statistical techniques increases the accuracy of forecasts, which is essential for manufacturers in a highly competitive environment. Thread (computing) ; Downtime ; Artificial neural network ; Computer science ; Automotive engineering ; Engineering ; Mechanical engineering ; Reliability engineering ; Artificial intelligence Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Surface Roughness and Optical Measurements
Authors: Haonan Zhang
Source: Clinical Medicine and Pharmacology (article, 2025-11-30)
Field: Chemistry — Spectroscopy

Spatial Proteomics Imaging to Decode Disease Progression: A Perspective on Tumor Microenvironments Protein spatial imaging, rooted in spatial proteomics, enables the localization of proteins within their native environment, thus preserving structural and functional context. Its integration into cancer research has provided insights into tumor progression, especially in breast cancer, where ductal carcinoma in situ (DCIS) may transition into invasive breast cancer (IBC). Multiplexed ion beam imaging by time-of-flight (MIBI-TOF) has emerged as a powerful method to visualize protein distribution, tumor–stroma interactions, and immune cell dynamics. Evidence indicates that the tumor microenvironment (TME)—including stromal architecture, extracellular matrix (ECM), and immune cell infiltration—plays a central role in malignant progression. Here, we highlight recent advances in protein spatial imaging, discuss optimization strategies, and examine its implications for breast cancer progression and other diseases. Tumor microenvironment ; Extracellular matrix ; Breast cancer ; Immune system ; Stromal cell ; Proteomics ; Disease ; Ductal carcinoma ; Cancer Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; Cancer Immunotherapy and Biomarkers
Authors: Dalton Boutwell | Axiu Cao | Allison S. Walker | Lauren E. Buchanan
Source: ChemRxiv (preprint, 2025-05-14)
Field: Materials Science — Materials Chemistry

Refining Structural Analysis of Proteins: Automated Methods to Measure Transition Dipole Strength of Single Residues Transition dipole strength (TDS) analysis enhances two-dimensional infrared (2D IR) spectroscopy by probing protein structural differences that frequency alone cannot resolve. However, its application has been limited to strong signals due to challenges with low signal-to-noise and large backgrounds in the linear optical density. Manual baseline correction can suffer from user error and produce large artifacts that obscure signals of interest. Here, we introduce a new approach incorporating automated baseline correction via the airPLS algorithm to improve the accuracy and precision of TDS calculations across broad spectral windows. Using human islet amyloid polypeptide, we demonstrate TDS analysis of a single 13C=18O-labeled residue, enabling a more precise measure of protein structure at the single-residue level. Further, airPLS-corrected TDS spectra can be calculated throughout amyloid aggregation to resolve potential intermediate structures. This work establishes TDS as a robust tool for investigating the structural dynamics of proteins and other complex macromolecular assemblies. Measure (data warehouse) ; Refining (metallurgy) ; Dipole ; Chemistry ; Computer science ; Data mining ; Physical chemistry ; Organic chemistry Enzyme Structure and Function ; Protein Structure and Dynamics
Authors: Zixuan Wang
Source: Textile & Leather Review (article, 2025-11-28)
Field: Materials Science — Polymers and Plastics

Digital Processing of Ethnic Painting Patterns and Its Application in Jacquard Fabric Design The global textile industry is increasingly focused on innovative product design and sustainable development. This research addresses a key challenge in textile manufacturing: the effective integration of complex cultural art into modern automated weaving processes. This study proposes a systematic digital workflow for translating ethnic painting patterns into sophisticated Jacquard woven fabrics. This methodology is applicable to a wide range of natural and synthetic fibers, including wool and cotton. The process encompasses digital image processing, pattern element engineering, and a k-means clustering algorithm for color optimization based on available yarn palettes. A pivotal aspect of this work is the strategic mapping of the digital pattern onto specific Jacquard weave structures. This technique transforms the 2D artwork into a 3D fabric with varied textures, thereby enhancing the textile’s material properties and aesthetic value. The results, visualized and qualitatively assessed through high-fidelity simulations, suggest that this digital-to-fabric pipeline offers a promising framework for the high-value application of cultural heritage. This approach contributes to the sustainable development of the textile industry by creating unique fiber products and provides a robust technical framework for advancing modern textile technology and processing. Textile design ; Weaving ; Textile ; Workflow ; Pipeline (software) ; Cultural heritage ; Painting ; Yarn ; Process (computing) Textile materials and evaluations ; Crafts, Textile, and Design ; Music Technology and Sound Studies
Authors: Jiazhen Chen | Ziyi Guo | Tao Li | Yue Sun | Joanne Yip | Kit‐Lun Yick | Fengyuan Zou
Source: Technologies (article, 2025-10-28)
Field: Materials Science — Polymers and Plastics

Construction of Pattern Optimization Model Driven by Fabric Parameters in 3D Garment Development Using Artificial Neural Networks Fabric properties significantly influence the accuracy of pattern dimensions derived from 3D scanned garment samples. To enhance the generated pattern accuracy, a novel predictive model was proposed to estimate the pattern dimension change ratio by integrating fabric parameters using an artificial neural network (ANN). Thirty fabrics were tested for making flared skirts. The pattern generation involves 3D scanned garment samples, the Bowyer–Watson algorithm for surface reconstruction, and an energy model for surface development. After the pattern’s dimension change ratio was obtained, principal component analysis (PCA) was applied to reduce dimensionality before correlation analysis. Results indicated that thickness, bending rigidity, drapability, and shear performance were the primary fabric parameters influencing dimensional accuracy. Backpropagation (BP) neural networks were constructed to predict the pattern size change ratio using both full fabric parameters or a PCA-reduced set, including a 9-parameter input layer, four hidden layers, and a 12-parameter output layer. The BP ANN models outperformed the radial basis function (RBF) ANN models, achieving accuracies of 96.67% and 96.02% for the full-factor and dimension-reduced models, respectively. After parameter optimization, the dimension-reduced BP ANN model enhanced pattern accuracy by 5.11%, achieving a final 97.73% accuracy. Results validate utilizing fabric parameters and BP neural networks as a sophisticated pattern optimization method. Artificial neural network ; Backpropagation ; Curse of dimensionality ; Principal component analysis ; Pattern recognition (psychology) ; Dimensionality reduction ; Dimension (graph theory) Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Industrial Vision Systems and Defect Detection