jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hao Meng | Tobias Karmainski | Aziz Ben Ammar | Anka Sieberichs | Yannick Branson | Peer Vossen | Tobias Schwanemann | Hendrik Ballerstedt | Uwe T. Bornscheuer | Ren Wei | Lars M. Blank
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-03-25)
Field: Materials Science — Biomaterials

Engineering <i>Pseudomonas putida</i> KT2440 for open-loop upcycling of mixed plastics Abstract Current mechanical and chemical recycling strategies address less than 10% of global plastic waste, necessitating alternative valorization routes. Biological upcycling via enzymatic depolymerization combined with microbial conversion of the resulting monomers offers a promising pathway to transform mixed plastic waste into valuable alternatives. Here, we employed a single engineered Pseudomonas putida KT2440 for simultaneous co-utilization of five plastic monomers including ethylene glycol, terephthalic acid, adipic acid, 1,4-butanediol, and L-lactic acid, which can be derived from enzymatic hydrolysis of polyethylene terephthalate (PET), polybutylene adipate-co-terephthalate (PBAT), polyester-polyurethanes (PUs), and polylactic acid (PLA). Continuous fermentation over 21 days with alternating mixed-monomer feeds achieved steady state growth and complete substrate depletion, yielding adaptive mutations that informed iterative strain improvement. Further engineering enabled the biosynthesis of ( R )-3-hydroxybutyrate (R-3HB), and 0.70 g L −1 R-3HB was produced directly from enzymatic hydrolysates of blended PET, PBAT, and TPU. These results establish a viable bio-based approach for upcycling realistic mixed plastics into value-added bioproducts. Adipic acid ; Depolymerization ; Polylactic acid ; Pseudomonas putida ; Terephthalic acid ; Polyester ; Polyethylene terephthalate ; Fermentation ; Hydrolysis biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Microbial Metabolic Engineering and Bioproduction
Authors: Guogang Gao | Fangshen Zhang | Lihua Huang | Yasong Wang | Xin Zhang | Yiping Wang
Source: Agronomy (article, 2026-01-24)
Field: Materials Science — Polymers and Plastics

Research Progress on Machine Vision Detection Technology for Foreign Fibers in Cotton Foreign fiber (FF, plural: FFs) contamination has been demonstrated to have a substantial impact on the quality and profitability of cotton textiles. Machine vision technology, characterized by its non-contact approach and high efficiency, has emerged as the primary solution for detecting FFs in cotton. This paper commences with a precise definition and classification of FF and a concomitant analysis of the mechanisms of contamination. Subsequently, a systematic review of global research advancements in imaging technologies and the evolution of algorithms is conducted. This paper emphasizes the use of X-ray, ultraviolet fluorescence, line laser, polarized light, infrared imaging, and hyperspectral imaging techniques for FF detection. Through a comparative analysis, it reveals the applicable scope and effectiveness of various imaging schemes. Regarding the evolution of algorithms, this paper expounds on the technical development process from traditional image processing to machine learning (ML) and deep learning (DL). The study meticulously examines the strengths and weaknesses of each algorithmic stage. In conclusion, this paper synthesizes the prevailing technical challenges confronting machine vision detection of FFs in cotton and proffers recommendations for future research directions in this domain, emphasizing multi-technology integration, algorithm optimization, and hardware innovations. Hyperspectral imaging ; Machine vision ; Scope (computer science) ; Strengths and weaknesses ; Process (computing) ; Image processing ; Profitability index ; Quality (philosophy) Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Dyeing and Modifying Textile Fibers
Authors: Aswin Karkadakattil
Source: Preprints.org (preprint, 2026-03-13)
Field: Materials Science — Materials Chemistry

Physics-Informed Surrogate Modelling of Finite-Size Scaling and Curie Temperature Suppression in Ferroelectric Perovskite Nanostructures Finite-size suppression of the Curie temperature (Tc) in ferroelectric perovskite nanostructures remains an important yet insufficiently resolved problem, with reported scaling exponents varying considerably across experimental and theoretical studies. Although density functional theory provides atomistic insight into size-dependent behaviour, its high computational cost limits systematic exploration across broad size ranges. Conversely, purely empirical fitting approaches often lack physical interpretability and formal uncertainty quantification. In this work, a physics-informed surrogate modelling framework is developed to investigate finite-size scaling in BaTiO₃ and KNbO₃ nanostructures using a structured dataset compiled from the literature. The model is based on thermodynamically motivated scaling behaviour, enabling extraction of physically meaningful size-dependent parameters. Bootstrap resampling is employed to quantify statistical robustness, yielding scaling exponents of 1.59 (95% confidence interval: 1.43–1.72) for BaTiO₃ and 1.40 (95% confidence interval: 1.31–1.52) for KNbO₃. Gaussian Process regression is further integrated to provide uncertainty-aware predictions across the nanoscale domain. In addition to forward prediction, the framework enables inverse estimation of the minimum particle size required to preserve ferroelectric stability at a specified operating temperature. For a threshold of 300 K, the predicted critical sizes are approximately 4.96 nm for BaTiO₃ and 2.89 nm for KNbO₃. Extension to a coupled size–strain formulation produces a two-dimensional stability map, demonstrating tunable interactions between confinement and strain. Overall, the proposed methodology provides a transparent, statistically rigorous, and computationally efficient framework for predictive analysis and rational design of nanoscale ferroelectric materials. Scaling ; Curie temperature ; Ferroelectricity ; Perovskite (structure) ; Stability (learning theory) ; Interpretability ; Inverse Machine Learning in Materials Science ; Ferroelectric and Piezoelectric Materials ; X-ray Diffraction in Crystallography
Authors: Yusong Wang | Youjun Xu | Wentao Li | Haoyu S. Yu | Wenjuan Tan | Shaoning Li | Qiaojing Huang | Nanjun Chen | Xuan Wu | Qilong Wu | Kai Liu
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Learning the All-Atom Equilibrium Distribution of Biomolecular Interactions at Scale Abstract Biomolecular functions are governed by dynamic conformational ensembles rather than static structures. While models like AlphaFold have revolutionized static structure prediction, accurately capturing the equilibrium distribution of all-atom biomolecular interactions remains a significant challenge due to the high computational cost of molecular dynamics (MD). We present AnewSampling, a transferable generative foundation framework designed for the high-fidelity sampling of all-atom equilibrium distributions, which is the first model to faithfully reproduce MD at the all-atom level. It uses a novel quotient-space generative framework to ensure mathematical consistency and leverages the largest self-curated database of protein-ligand trajectories to date, with over 15 million conformations. Statistically, AnewSampling consistently outperforms all prior generative methods on the ATLAS monomer benchmark, and the all-atom capabilities of AnewSampling enable close statistical alignment with ground-truth MD for evaluating atomic biomolecular interactions in protein-ligand dynamics. Furthermore, AnewSampling successfully recovers coupled ligand and side-chain motions in CDK2 systems, overcoming a major sampling hurdle inherent to conventional MD. AnewSampling enables rapid exploration of conformational landscapes prior to intensive simulations, elucidating fundamental biophysical mechanisms and accelerating the broader design of functional biomolecules. Consistency (knowledge bases) ; Generative grammar ; Sampling (signal processing) ; Molecular dynamics ; Scale (ratio) ; Set (abstract data type) ; Conformational ensembles Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Deeksha Satyabola | Shuai Feng | Prathamesh Chopade | Abhay Prasad | L. Wang | Nimar Bamrah | Liangxiao Chen | S. Sanoj Kumar Roy | Aaron Sakai | Chao Wang | Petr Šulc | Rizal F. Hariadi | Hao Yan | Sui Yang
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-01-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

DNA-templated Chiral Metamaterial Array as Information Bits Abstract The growth of digital information demands physically secure information bits that combine intrinsic randomness with multi-layered optical readout. Digital metamaterials with physical unclonable function characteristics are promising, but nearly all examples operate at microwave or terahertz frequencies. Extending digital metamaterials to the visible range requires a physical property that allows binary (0/1) encoding via nanoscale geometry and is orthogonal to intensity-or color-based imaging. Chiral plasmonic metamaterials satisfy these criteria perfectly: their broken mirror symmetry yields chirality polarity in the visible spectrum whose sign and magnitude directly encode bit values (“0”, spin-up; “1”, spin-down), while remaining invisible under linear polarization. Here, we realize visible-range digital metamaterials by programming self-assembled DNA origami templates with asymmetrically placed gold nanorods to create discrete 3D chiral metamolecules. Using on-surface DNA origami assembly and the Silica Microsphere-Assisted Patterning by Liquid Elimination (SiMPLE) method, we fabricate large-scale bit arrays on optical active glass substrate in solution with ∼1.33 μm lattice spacing, ∼86% site occupancy, and ∼12-month stability after silicification. These bottom-up fabricated digital metamaterial array exhibits two independent security layers: (1) a macroscopic spatial pattern only visible by dark-field microscopy, and (2) hidden binary information stored in the single-particle chiroptical response, read out by position-resolved circular dichroism spectroscopy. Quantitative analysis confirms reliable bit encoding and high optical randomness arising from slight structural variations. By leveraging the polarity of plasmonic chirality to translate molecular-scale handedness into robust visible-range digital signals, this work establishes a scalable nanophotonic platform for secure optical information storage, authentication, and encryption. Metamaterial ; Nanophotonics ; DNA origami ; Randomness ; Photonics ; Chirality (physics) ; Plasmon ; Binary number Metamaterials and Metasurfaces Applications ; Advanced biosensing and bioanalysis techniques ; Plasmonic and Surface Plasmon Research
Authors: Ha-Eun Yang | Hong-Gu Lee | Jeong-Eun Lee | Jeong‐Yong Shin | Wan-Gyu Sang | Byoung-Kwan Cho | Changyeun Mo
Source: Agriculture (article, 2026-03-17)
Field: Chemistry — Analytical Chemistry

Deep Learning-Enabled Nondestructive Prediction of Moisture Content in Post-Heading Paddy Rice (Oryza sativa L.) Using Near-Infrared Spectroscopy Rapid non-destructive evaluation of the moisture content of freshly harvested paddy rice in the field is essential for determining the optimal harvest timing, ensuring high-quality rice production and energy savings. This study developed a non-destructive prediction model for the moisture content of paddy rice using near-infrared (NIR) spectroscopy combined with machine learning and deep learning techniques. Rice samples were collected weekly during the ripening period after heading, and NIR reflectance spectra were acquired in the range of 950–2200 nm. Seven spectral preprocessing techniques were applied; and the prediction models developed, using partial least squares regression, support vector regression, deep neural network, and one-dimensional convolutional neural networks (1D-CNNs) based on VGGNet and EfficientNet architectures. Among these, the EfficientNet-based 1D-CNN combined with Savitzky–Golay 1st order derivative preprocessing showed the highest performance, achieving an Rp2 of 0.999 and an RMSEP of 0.001 (Friedman test, p &lt; 0.001; Kendall’s W = 0.97), significantly outperforming previous traditional machine learning models. The results demonstrate that the proposed prediction model enables highly accurate estimation of moisture content in freshly harvested paddy rice without requiring drying or milling. The proposed approach can be implemented across various agricultural operations, enabling optimal harvest timing, quality control during storage, energy efficient drying, and real-time monitoring via on-combine sensor systems. Water content ; Partial least squares regression ; Preprocessor ; Paddy field ; Moisture ; Near-infrared spectroscopy ; Convolutional neural network Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Food Drying and Modeling
Authors: Yucheng Liao | Han Wen | Weinan E | Weijie Zhang
Source: arXiv (Cornell University) (article, 2026-02-02)
Field: Chemistry — Spectroscopy

DIA-CLIP: a universal representation learning framework for zero-shot DIA proteomics Data-independent acquisition mass spectrometry (DIA-MS) has established itself as a cornerstone of proteomic profiling and large-scale systems biology, offering unparalleled depth and reproducibility. Current DIA analysis frameworks, however, require semi-supervised training within each run for peptide-spectrum match (PSM) re-scoring. This approach is prone to overfitting and lacks generalizability across diverse species and experimental conditions. Here, we present DIA-CLIP, a pre-trained model shifting the DIA analysis paradigm from semi-supervised training to universal cross-modal representation learning. By integrating dual-encoder contrastive learning framework with encoder-decoder architecture, DIA-CLIP establishes a unified cross-modal representation for peptides and corresponding spectral features, achieving high-precision, zero-shot PSM inference. Extensive evaluations across diverse benchmarks demonstrate that DIA-CLIP consistently outperforms state-of-the-art tools, yielding up to a 45% increase in protein identification while achieving a 12% reduction in entrapment identifications. Moreover, DIA-CLIP holds immense potential for diverse practical applications, such as single-cell and spatial proteomics, where its enhanced identification depth facilitates the discovery of novel biomarkers and the elucidates of intricate cellular mechanisms. Overfitting ; Generalizability theory ; Representation (politics) ; Identification (biology) ; Profiling (computer programming) ; Feature learning ; Training set Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Dirlei Nico | Daniel Clemente de Moraes | Anna Claudia Silva | Igor Taveira | Yasmin da Silva Fontes | Rosângela S. C. Lopes | Cláudio C. Lopes | Antônio Ferreira‐Pereira
Source: Pharmaceuticals (article, 2026-03-05)
Field: Chemistry — Organic Chemistry

7-Prenyloxycoumarins as Promising Antileishmanial Agents: In Vitro, In Vivo, and In Silico Evaluation Against Leishmania amazonensis <b>Background/Objectives:</b> Leishmaniasis remains a major neglected tropical disease, and current chemotherapeutic options are limited by toxicity and resistance in <i>Leishmania</i> species, including <i>L. amazonensis</i>. Prenylated coumarins have emerged as promising bioactive scaffolds. Altissimacoumarin D and its analogues inhibit fungal efflux pumps associated with resistance. However, their antileishmanial potential and mechanisms of action remain unclear. Here, we evaluated the in vitro, in vivo, and in silico effects of altissimacoumarin D and seven analogues against <i>L. amazonensis</i>. <b>Methods:</b> In vitro assays were performed to identify active compounds and assess toxicity in keratinocytes. In vivo experiments in hamsters evaluated antileishmanial activity and renal and hepatic toxicity. In silico analyses were conducted to investigate the mechanism of action of the substances. <b>Results:</b> In vitro assays showed that ACS47, ACS48, and ACS51 were the most active and safe compounds. In a hamster infection model, daily administration of ACS47 and ACS48 (2.5 mg/kg) significantly reduced parasite burden and lesion size, while maintaining normal renal and hepatic biochemical parameters. Histological analysis correlated reduced lesion size with marked decreases in amastigote density. Based on in silico analysis, spermidine synthase was supported as a plausible molecular target. <b>Conclusions:</b> Collectively, these findings identify ACS47 and ACS48 as promising lead compounds for future antileishmanial drug development. In silico ; Amastigote ; Leishmania ; In vivo ; Miltefosine ; Hamster ; Mechanism of action ; Leishmania mexicana ; Leishmaniasis Synthesis and biological activity ; Plant chemical constituents analysis ; Hops Chemistry and Applications
Authors: Yanping Li | Sitong Huo | Neng Yan
Source: Environmental Science Nano (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A transformative perspective on aggregation-induced emission bioimaging: illuminating the complex pathways of metal nanomaterial toxicology The expanding use of metal nanomaterials (MNMs) demands balancing their potential with rigorous biological safety. Transformative learning ; Perspective (graphical) ; Nanomaterials ; Dual (grammatical number) ; Nanotoxicology Nanoparticles: synthesis and applications ; Nanocluster Synthesis and Applications ; Melamine detection and toxicity
Authors: Xianmin Zhou | Xinyun Dong | Qinxuan Dai | Youyu Jiang | Yinhua Zhou
Source: Chinese Science Bulletin (Chinese Version) (article, 2026-01-01)
Field: Materials Science — Polymers and Plastics

Alcohol-Dispersed Conducting Polymer PEDOT:F Formulation: Synthesis, Properties, and Applications 水相聚（3,4-乙烯二氧噻吩）：聚（苯乙烯磺酸）（PEDOT:PSS）导电聚合物具有高导电、光学透明、可涂布加工等优点。自 1990 年发明以来，已被广泛应用于固态电容、抗静电涂层、印刷光电子、热电器件、柔性电子以及生物电子等领域。然而，PEDOT:PSS 存在以下不足：酸性强、易于吸湿、浸润性差，这些缺点限制了 PEDOT:PSS 的应用。如何克服 PEDOT:PSS 的前述不足受到领域的广泛关注。2022 年，醇相导电聚合物配方 PEDOT:F 的成功开发为该领域带来突破。研究人员通过抗衡离子替代策略，采用可醇溶的全氟磺酸离子聚合物（PFSA）替代 PSS 实现了醇相分散的导电聚合物配方，该配方克服了PEDOT:PSS润湿性差、酸性强、易吸湿的问题，同时兼具高功函、高透光率以及优异印刷特性。本文首先介绍了醇相PEDOT:F的合成，其次介绍了PEDOT:F的酸碱性、浸润性、吸湿性、电导率及功函数等光电性质，然后综述了其在有机太阳能电池、钙钛矿太阳能电池、叠层太阳能电池、柔性太阳能电池以及大面积全印刷太阳能电池等器件中的应用进展，最后展望了醇相PEDOT导电聚合物发展方向及其在其他领域的应用前景。 Conductive polymer ; Polymer ; Polyaniline ; Electrical conductor ; Conductivity Conducting polymers and applications ; Analytical Chemistry and Sensors ; Organic Electronics and Photovoltaics
Authors: Güven Budak | Christian Riedel | Akashdeep Kamra | Patrick Rinke | C. H. Back | Matthias Stosiek | Florian Dirnberger
Source: ArXiv.org (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

Role of photonic interference in exciton-mediated magneto-optic responses Coupled optical and magnetic excitations can give rise to remarkably strong magneto-optic responses. This is particularly evident in van der Waals magnets, such as the antiferromagnet CrSBr, where excitons and magnons emerge from the same electronic orbitals. While previous work has primarily focused on uncovering the magneto-electric origin of the resulting exciton-magnon interactions, the influence of photonic effects has received comparatively little attention. Here, we use numerical simulations to disentangle exciton-magnon coupling from the exciton-mediated magnon-photon interactions observed in optical experiments. Our simulations show the strong dependence of these interactions on photonic interference and dispersion effects near excitonic resonances. Such effects shape the optical response to coherent magnons and make it intrinsically non-linear in the magnon-induced exciton energy shift. Thermal magnons, which have a particularly pronounced impact on excitons, are found to even produce qualitatively different trends in optical signatures. Depending on weak or strong coupling of excitons and photons, the same exciton-magnon interaction can lead to a red-shift of optical modes, a nearly vanishing response, or their blue-shift. Finally, we demonstrate first steps towards optimizing the multi-parameter problem of efficient magnon-photon transduction using a machine-learning approach. Exciton ; Magnon ; Photonics ; Coupling (piping) ; Interference (communication) ; van der Waals force ; Dispersion (optics) Chemical and Physical Properties of Materials ; Strong Light-Matter Interactions ; Mechanical and Optical Resonators
Authors: Neiry-Dayán Martínez-Lara | Annamaría Filomena-Ambrosio | Claudia L. Garzón‐Castro
Source: Journal of Food Measurement & Characterization (article, 2026-03-04)
Field: Chemistry — Analytical Chemistry

Use of computer vision systems in baked products: potential tool for measuring physical properties In response to the growing market demand for healthier bakery products, recent studies in the baking industry have focused on evaluating different types of bread using standardized methods and specialized instruments such as alveographs, colorimeters, and volume meters, as well as sensory tests. However, these methods require resources and specific equipment, which limits their replicability. This review addresses alternative, noninvasive tools and methodologies for measuring physical properties of bakery products through its visual appearance. An initial literature review identified four main categories of properties studied in bakery products: internal, external, nutritional, and sensory. A second structure review was made with a Boolean equation to explore the use of imaging technologies, including computer vision systems, to measure some of these properties. These systems are characterized by fast and accurate tools for inspecting, measuring, and classifying food. The structured review revealed a growing trend in the use of image-based analysis at the macro and microscopic levels to evaluate the physical properties of bread. However, the wider use of computer vision systems is limited by the lack of standardized hardware for image processing, which makes replication in different environments difficult. Although there are some advanced programs that can preprocess, segment, and extract data from images, their effectiveness often depends on user expertise. This review highlights the potential of digital image processing as a non-invasive tool in the baking industry and the challenges that must be addressed to enable wider implementation. Macro ; Measure (data warehouse) ; Replication (statistics) ; Image processing ; Machine vision ; Volume (thermodynamics) Spectroscopy and Chemometric Analyses ; Food composition and properties ; Food Drying and Modeling