jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nikolay L. Kazanskiy | Svetlana N. Khonina
Source: Computer Optics (article, 2025-06-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metasurfaces &amp; prospects of computer optics Metasurfaces represent a revolutionary paradigm shift in optics that has opened the era of multifunctional light transformations using ultra-thin elements. In contrast to the traditional approach based on refractive and diffractive optical elements, metasurfaces use complex arrays of subwavelength structures to control the phase, amplitude, and polarization of incident light waves. This revolutionary technology offers many advantages, including the creation of ultra-compact optical systems, providing polarization transformations and sharp focusing beyond the diffraction limit, as well as compensation for optical aberrations in a significantly thinner and lighter format. Metasurfaces have been successfully applied in a variety of applications: from high-quality imaging to augmented reality, spectroscopy, environmental monitoring, and improving technology and medical equipment. Due to their remarkable adaptability and exceptional properties, metasurfaces have enormous potential to break the seemingly insurmountable boundaries of light technology. This review presents main applications of metasurfaces in terms of expanding the capabilities of computer optics, discusses challenges and prospects for development, including based on reconfigurable devices and using artificial intelligence methods. Polarization (electrochemistry) ; Diffraction ; Adaptability ; Ray ; Compensation (psychology) ; Metamaterial ; Physical optics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Optical Wireless Communication Technologies
Authors: Marcin Szyniszewski | Elaheh Mostaani | Angelika Knothe | V. V. Enaldiev | Andrea C. Ferrari | Vladimir I. Fal’ko | N. D. Drummond
Source: ACS Nano (article, 2025-02-10)
Field: Materials Science — Materials Chemistry

Adhesion and Reconstruction of Graphene/Hexagonal Boron Nitride Heterostructures: A Quantum Monte Carlo Study We investigate interlayer adhesion and relaxation at interfaces between graphene and hexagonal boron nitride (hBN) monolayers in van der Waals heterostructures. The adhesion potential between graphene and hBN is calculated as a function of local lattice offset using diffusion quantum Monte Carlo methods, which provide an accurate treatment of van der Waals interactions. Combining the adhesion potential with elasticity theory, we determined the relaxed structures of graphene and hBN layers at interfaces, finding no metastable structures. The adhesion potential is well described by simple Lennard-Jones pair potentials that we parametrize using our quantum Monte Carlo data. Encapsulation of graphene between near-aligned crystals of hBN gives rise to a moiré pattern whose period is determined by the misalignment angle between the hBN crystals superimposed over the moiré superlattice previously studied in graphene on an hBN substrate. We model minibands in such supermoiré superlattices and find them to be sensitive to the 180° rotation of one of the encapsulating hBN crystals. We find that monolayer and bilayer graphene placed on a bulk hBN substrate and bulk hBN/graphene/bulk hBN systems do not relax to adopt a common lattice constant. The energetic balance is much closer for free-standing monolayer graphene/hBN bilayers and hBN/graphene/hBN trilayers. The layers in an alternating stack of graphene and hBN are predicted to strain to adopt a common lattice constant, and hence, we obtain a stable three-dimensional crystal with a distinct electronic structure. Graphene ; Materials science ; Heterojunction ; Hexagonal boron nitride ; Monte Carlo method ; Adhesion ; Nanotechnology ; Hexagonal crystal system ; Boron nitride ; Nitride ; Optoelectronics ; Composite material ; Crystallography ; Chemistry ; Mathematics Graphene research and applications ; Boron and Carbon Nanomaterials Research ; 2D Materials and Applications
Authors: Hana Stipanović | Gerald Koinig | Thomas Fink | Christian B. Schimper | David Lilek | Jeannie Egan | Alexia Tischberger-Aldrian
Source: Recycling (article, 2025-08-01)
Field: Chemistry — Analytical Chemistry

Quantifying Cotton Content in Post-Consumer Polyester/Cotton Blend Textiles via NIR Spectroscopy: Current Attainable Outcomes and Challenges in Practice Rising volumes of textile waste necessitate the development of more efficient recycling systems, with a primary focus on the optimization of sorting technologies. Near-infrared (NIR) spectroscopy is a state-of-the-art method for fiber identification; however, its accuracy in quantifying textile blends, particularly common polyester/cotton blend textiles, still requires refinement. This study explores the potential and limitations of NIR spectroscopy for quantifying cotton content in post-consumer textiles. A lab-scale NIR sorter and a handheld NIR spectrometer in complementary wavelength ranges were applied to a diverse range of post-consumer textile samples to test model accuracies. Results show that the commonly assumed 10% accuracy threshold in industrial sorting can be exceeded, especially when excluding textiles with &lt;35% cotton content. Identifying and excluding the range of non-linearity significantly improved the model’s performance. The final models achieved an RMSEP of 6.6% and bias of −0.9% for the NIR sorter and an RMSEP of 3.1% and bias of −0.6% for the handheld NIR spectrometer. This study also assessed how textile characteristics—such as color, structure, product type, and alkaline treatment—affect spectral behavior and model accuracy, highlighting their importance for refining quantification when high-purity inputs are needed. By identifying current limitations and potential sources of errors, this study provides a foundation for improving NIR-based models. Polyester ; Materials science ; Polymer science ; Spectroscopy ; Pulp and paper industry ; Composite material ; Engineering Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Haimin Zhai | Pan Xie | Xin Xie | S. Sha
Source: Frontiers in Plant Science (article, 2025-09-18)
Field: Chemistry — Analytical Chemistry

Deep learning-enabled hyperspectral imaging for high-accuracy non-destructive quantification of nutritional components in multi-variety apples Conventional methods for quantifying soluble solids content (SSC), vitamin C (VC), and soluble protein (SP) levels in apples are destructive and unsuitable for large-scale postharvest quality monitoring. This study aimed to develop a convolutional neural network-bidirectional gated recurrent unit-attention (CNN-BiGRU-Attention) model based on hyperspectral imaging (HSI) to achieve high-precision non-destructive quantification of VC, SSC, and SP in apples. The model was established using six apple varieties from diverse geographical origins, leveraging hyperspectral data spanning 400-1000 nm with 512 spectral bands. The model framework demonstrated superior performance with raw hyperspectral cube inputs. Optimal predictions for VC and SSC were achieved using full-spectrum modeling (test set: R²<sub>VC</sub>=0.891, R²<sub>SSC</sub>=0.807, RPD <sub>VC</sub>=3.117, RPD <sub>SSC</sub>=2.337). For SP quantification, feature wavelength selection (403, 430, 551, 617, and 846 nm) via successive projections algorithm (SPA) yielded R²=0.848, RPD=2.642, which aligned with the N-H/C-H vibrational overtones and aromatic amino acid absorption bands. Cross-year validation of 2024 hyperspectral dataset confirmed the robustness of the model, with R<sup>2</sup> values of 0.829, 0.779, and 0.835 (RPD>2.000) for VC, SSC, and SP, respectively. Taken together, this study resolves high-dimensional data redundancy through hybrid architectures and offers a deployable solution for multi-variety fruit quality monitoring. Hyperspectral imaging ; Convolutional neural network ; Robustness (evolution) ; Feature selection ; Near-infrared spectroscopy ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management ; Phytochemicals and Antioxidant Activities
Authors: Lorena Veliz | Cédric Lambin | Tyler T. Cooper | William Michael McCarvell | Gilles Lajoie | Lynne‐Marie Postovit | François Lagugné‐Labarthet
Source: Nanoscale (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Emerging SERS and TERS MoS<sub>2</sub> platforms for the characterization of plasma-derived extracellular vesicles Extracellular vesicles (EVs) play a crucial role in intercellular communication processes. In addition, their biomolecular cargoes such as lipids, proteins, and nucleic acids are useful for identifying potential biomarkers related to different stages of cancer disease. However, the small size and heterogenicity of tumor-related EVs represent a major challenge in properly identifying the content of EVs' cargoes with common characterization protocols. To address these issues, surface-enhanced Raman spectroscopy (SERS) and tip-enhanced Raman spectroscopy (TERS) are powerful alternatives to assign the vibrational fingerprints to the biomolecules contained in cancer EVs, providing high specificity and spatial resolution. Transition metal dichalcogenides are particularly interesting as SERS and TERS substrates due to the high sensitivity of their 2D surface through coulombic and van der Waals interactions when in contact with an analyte or small object such as the charged membranes of EVs. These interactions induce subtle changes in the work function of the flakes which can be measured through drastic changes of optical processes. We investigate the use of MoS<sub>2</sub> flakes synthesized by atmospheric pressure chemical vapor deposition as a potential label-free SERS and TERS platform for the identification of plasma EVs. To exemplify this technology, we isolated plasma EV samples from donors with early-stage [FIGO (I/II)] with high-grade serous carcinoma (HGSC) by size exclusion chromatography (SEC). Both surface- and tip-enhanced measurements were conducted individually, enabling the identification of a series of markers from ovarian cancer donors, highlighting the complementarity of SERS and TERS measurements. Extracellular vesicles ; Characterization (materials science) ; Plasma ; Vesicle ; Nanotechnology ; Extracellular ; Chemistry ; Materials science ; Biophysics ; Biochemistry ; Cell biology ; Membrane ; Biology ; Physics Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications
Authors: Esra Pamukçu
Source: Journal of Chemometrics (article, 2025-02-24)
Field: Chemistry — Analytical Chemistry

Efficient Wavelength Selection for Limited Near‐Infrared Spectral Data via Genetic Algorithm and Hybrid Regression ABSTRACT Spectral data often contains a large number of variables that are highly correlated. Although Partial Least Squares (PLS) regression is specifically designed to handle issues arising from limited sample sizes, its effectiveness may still diminish in e x tremely small datasets, making it challenging to construct a calibration model with high predictive performance. This study introduces a new framework, the Genetic Algorithm and Hybrid Regression Model (GAHRM), designed specifically for variable selection and regression in high‐dimensional, low‐sample‐size spectral datasets. GAHRM integrates Hybrid Regression, which constructs regression models using a covariance structure that is first stabilized through Thomaz Stabilization and then regularized, with Genetic Algorithm (GA), an efficient optimization technique for selecting the best subset of variables among a vast model space. Unlike traditional approaches that rely on exhaustive search for model selection criteria, GAHRM leverages GA to navigate the exponentially large search space, enabling computationally feasible and robust model construction. The effectiveness of GAHRM was validated on the benchmark “Gasoline” dataset, where it demonstrated superior performance compared to PLS in terms of prediction accuracy and model selection efficiency. These results highlight GAHRM as a powerful alternative for wavelength selection and calibration modeling in challenging data scenarios. Selection (genetic algorithm) ; Infrared ; Regression ; Chemometrics ; Computer science ; Algorithm ; Wavelength ; Genetic algorithm ; Regression analysis ; Artificial intelligence ; Pattern recognition (psychology) ; Mathematics ; Machine learning ; Statistics ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Xiaoliang Cui | Xuanwen Wang | Siying Liu | Lilei Wang | Menghong Li | Jun Zhang | Li Wang | Ke‐Qin Zhang
Source: BMEMat (article, 2025-08-12)
Field: Materials Science — Biomaterials

Silk fibroin‐based biomaterials for spinal cord injury repair: Recent advances and future prospects Abstract Spinal cord injury (SCI) presents significant challenges due to the profound damage it causes to motor functions and sensory. The post‐trauma environment, characterized by the formation of cystic lesions and the absence of extracellular matrix, hinders neural regeneration and compromises the survival of transplanted cells. Biomaterials offer a promising avenue by providing a supportive environment for nerve repair. Silk fibroin (SF), a natural protein extracted from the cocoons of Bombyx mori silkworms, stands out for its exceptional biodegradability, biocompatibility, and adjustable mechanical properties. SF can be fabricated into various formats, including sponges, hydrogels, and fibers, making it highly adaptable for numerous biomedical applications such as tissue engineering, wound healing, and drug delivery. Recent advancements in SF‐based biomaterials have highlighted their potential in SCI repair by mimicking the native cellular microenvironment, promoting axonal growth, and facilitating tissue repair. This review focuses on the structure and properties of SF, its environmentally friendly processing methods, and the strategies for designing composite scaffolds using SF‐based biomaterials for SCI repair. It also examines future challenges and prospects in this promising field. Fibroin ; Spinal cord injury ; SILK ; Spinal cord ; Engineering ; Nanotechnology ; Materials science ; Neuroscience ; Biology ; Composite material Silk-based biomaterials and applications ; Electrospun Nanofibers in Biomedical Applications ; Bone Tissue Engineering Materials
Authors: Clara Matte Borges Machado | Luciana Porto de Souza Vandenberghe | Ariane Fátima Murawski de Mello | Carlos Ricardo Soccol
Source: Polymers (article, 2025-01-25)
Field: Materials Science — Biomaterials

Corn or Soybean Oil as the Sole Carbon Source for Polyhydroxybutyrate Production in a Biofuel Biorefinery Concept The use of polyhydroxybutyrate (PHB) can help diminish fossil chemical dependency because it can partially replace petrochemical plastics due to its biodegradability and similar mechanical properties. However, its production costs are high compared with fossil-based plastics. Alternative carbon sources can be used in the fermentation media because they are renewable and low-cost. Vegetable oils are especially attractive due to their high carbon content, contributing to high production rates per gram of substrate. This work aimed to produce PHB from <i>Cupriavidus necator</i> LPB1421 using either corn or soybean oil as the sole carbon source. Urea was the best nitrogen source, enabling a DCW production of 4.35 g/L (corn oil) and 10.4 g/L (soybean oil). After media optimization, the DCW of corn oil reached 22.13 g/L, with 57.46% PHB accumulation (12.71 g PHB/L), whereas soybean oil led to a DCW of 19.83 g/L, with 54.91% PHB accumulation (10.89 g PHB/L). This media composition was employed in a kinetics assay, revealing similar fermentation parameters among both oils and a yield of 0.2118 g PHB/g for corn oil and 0.1815 g PHB/g for soybean oil. These results open the possibility of integrating PHB production with biofuel manufacturing in a bioethanol/biodiesel biorefinery concept. Polyhydroxybutyrate ; Biorefinery ; Biofuel ; Bioplastic ; Biodiesel ; Fermentation ; Pulp and paper industry ; Food science ; Soybean oil ; Cupriavidus necator ; Polyhydroxyalkanoates ; Petrochemical ; Chemistry ; Biotechnology ; Waste management ; Organic chemistry ; Catalysis ; Bacteria ; Biology biodegradable polymer synthesis and properties ; Biofuel production and bioconversion ; Catalysis for Biomass Conversion
Authors: Jelena Sjakste | Raja Sen | Nathalie Vast | Jérôme Saint-Martin | Mohammad Ghanem | Philippe Dollfus | F. Murphy‐Armando | J. Kanasaki
Source: The Journal of Chemical Physics (article, 2025-02-10)
Field: Materials Science — Materials Chemistry

Ultrafast dynamics of hot carriers: Theoretical approaches based on real-time propagation of carrier distributions In recent years, computational approaches which couple density functional theory (DFT)-based description of the electron-phonon and phonon-phonon scattering rates with the Boltzmann transport equation have been shown to obtain the electron and thermal transport characteristics of many 3D and 2D semiconductors in excellent agreement with experimental measurements. At the same time, progress in the DFT-based description of the electron-phonon scattering has also allowed to describe the non-equilibrium relaxation dynamics of hot or photo-excited electrons in several materials, in very good agreement with time-resolved spectroscopy experiments. In the latter case, as the time-resolved spectroscopy techniques provide the possibility to monitor transient material characteristics evolving on the femtosecond and attosecond time scales, the time evolution of photo-excited, nonthermal carrier distributions has to be described. Similarly, reliable theoretical approaches are needed to describe the transient transport properties of devices involving high energy carriers. In this review, we aim to discuss recent progress in coupling the ab initio description of materials, especially that of the electron-phonon scattering, with the time-dependent approaches describing the time evolution of the out-of-equilibrium carrier distributions, in the context of time-resolved spectroscopy experiments as well as in the context of transport simulations. We point out the computational limitations common to all numerical approaches, which describe time propagation of strongly out-of-equilibrium carrier distributions in 3D materials, and discuss the methods used to overcome them. Boltzmann equation ; Phonon ; Context (archaeology) ; Relaxation (psychology) ; Femtosecond ; Scattering ; Electron ; Physics ; Attosecond ; Time evolution ; Computational physics ; Statistical physics ; Excited state ; Spectroscopy ; Ultrashort pulse ; Condensed matter physics ; Atomic physics ; Quantum mechanics Thermal properties of materials ; Electronic and Structural Properties of Oxides ; Advanced Chemical Physics Studies
Authors: Ahmad Alsaber | Anurag Satpathi | Mariam Alsabah | Parul Setiya
Source: Scientific Reports (article, 2025-07-24)
Field: Chemistry — Analytical Chemistry

Optimizing potato yield predictions in Uttar Pradesh, India: a comparative analysis of machine learning models Potato as a staple food, plays a crucial role in ensuring a sustainable food supply and mitigating poverty and malnutrition in various regions across the globe. India, specifically holding the second position in global potato production, plays a significant role in the global potato market. The accurate prediction of potato yield is crucial for addressing global food security and sustainable farming practices. Looking into these facts, this study focused on seven districts in Uttar Pradesh, India, viz, Agra, Aligarh, Etawah, Farrukhabad, Firozabad, Hathras and Kannauj. This research aims to compare the performance of five machine learning models-Elastic Net (ELNET), Random Forest, Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) to identify the most effective approach for potato yield forecasting. Time series data spanning 16 years (2005-2021) was collected for seven districts in Uttar Pradesh, including potato yields and weather variables. The data was detrended, processed into weather indices, and split into 70% for training and 30% for testing. Each district was treated as a unique case, with models trained and validated independently. The ANN model demonstrated superior performance with the highest R<sup>2</sup> values and the lowest error metrics, establishing it as the most reliable model for potato yield prediction. The overall ranking of the robust model performance can be given as: ANN > XGBoost > Random Forest > ELNET > SVR. The study emphasizes the effectiveness of ANN for potato yield forecasting and underscores the importance of tailoring models to local conditions to improve accuracy. Based on the ANN model's current performance, future potato yields in the studied districts can be predicted with over 98% accuracy, enabling proactive planning for food supply, market stabilization, and input resource optimization in the coming growing seasons. These findings contribute to advancing machine learning applications in agriculture, offering actionable insights for policymakers and stakeholders to support sustainable agricultural practices. Uttar pradesh ; Yield (engineering) ; Biotechnology ; Computer science ; Biology ; Machine learning ; Socioeconomics ; Materials science ; Economics Spectroscopy and Chemometric Analyses ; Potato Plant Research ; Smart Agriculture and AI
Authors: Bar Favelukis | Barak Ratzker | Rebeca Miyar | Jürgen Jopp | Alexander Upcher | Pini Shekhter | Nitzan Maman | Maxim Sokol
Source: Nanoscale Advances (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Without a grain of salt: micropatterning clean MXene thin-film electronics MXenes exhibit remarkable electrical, mechanical, and thermal properties, positioning them as strong candidates for high-performance electrodes and interconnects. Deposited 2D MXene thin-films suffer from a persistent issue of crystalline salt residues that originate from dissolved intercalation salts used for the exfoliation process during synthesis. These 3D salt by-products can cause issues during further nanofabrication processing and be detrimental to integrated device performance. This study introduces a three-step approach involving spin-coating deposition, HCl spin-cleaning, and lift-off. Rigorous morphological characterization of the patterned MXene was performed, confirming that the spin-cleaning step effectively removed all halide salt residues. Transparent sub-10 nm-thick MXene thin-film electrodes, down to a width of 5 μm with ∼1.5 μm resolution, were produced. The electrical properties were probed, showcasing exceptional conductivity (∼1350 S cm<sup>-1</sup> for a 50 μm-wide electrode) with high photosensitivity at the MXene-Si junction. The proposed method yields clean patterned MXene thin films, enabling easier integration of MXene or other 2D materials into future microelectronic devices. Micropatterning ; Materials science ; Nanotechnology ; Electronics ; Salt (chemistry) ; Optoelectronics ; Chemistry ; Electrical engineering ; Engineering MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Advanced Sensor and Energy Harvesting Materials