jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Toru Yamada | Yohei MORINISHI
Source: The Journal of Chemical Physics (article, 2025-04-03)
Field: Materials Science — Materials Chemistry

Application of Nosé–Hoover dynamics for coarse-graining molecular systems: An evaluation of reproducibility in Lennard-Jones systems This study proposes an application of Nosé-Hoover (NH) dynamics as a coarse-graining (CG) method for molecular simulations, offering an alternative to traditional Langevin-based approaches. The NH dynamics, known for its deterministic temperature control without stochastic forces, is adapted here to model a monoatomic Lennard-Jones system at different coarse-grained levels. The CG particle's equation of motion is derived from atomic-level dynamics, linking NH thermostat terms with system properties obtained from molecular dynamics (MD) simulations. Key parameters, including the thermostat coefficient and thermal inertia, are calibrated using the MD data to assess their impact on the dynamic and structural reproducibility of the CG model. The calibration results suggest the potential application of NH dynamics as a coarse-graining method. The effectiveness of the proposed method is then evaluated through a set of CG simulations. The CG results show stable energy regulation and promising accuracy in reproducing system properties, particularly for mass diffusion, with opportunities for further refinement in representing structural reproducibility and momentum diffusion. Thermostat ; Granularity ; Molecular dynamics ; Reproducibility ; Statistical physics ; Calibration ; Langevin dynamics ; System dynamics ; Momentum (technical analysis) ; Physics ; Thermodynamics ; Computer science ; Chemistry ; Computational chemistry Block Copolymer Self-Assembly ; Protein Structure and Dynamics ; Material Dynamics and Properties
Authors: Ivan G. Grozev | Dobromir A. Kalchevski | Dimitar V. Trifonov | Stefan K. Kolev | Hristiyan A. Aleksandrov | V. N. Popov | T. I. Milenov
Source: Nanomaterials (article, 2025-05-11)
Field: Materials Science — Materials Chemistry

A Complete Energy Model for Graphene Flake Growth with the Fewest Possible Dangling Bonds This work presents a complete energy model for graphene flakes' growth with the fewest possible dangling bonds. The model is based on a simple equation that describes the binding energy of graphene flakes consisting of up to 10,000 carbon atoms. Moreover, we demonstrate that the model can accurately calculate the binding energy of a topologically and geometrically diverse array of graphene flakes. According to our calculations, the model can predict the binding energy of a graphene flake with a deviation error of about 2-3%. Hence, we envision that the complete energy model for graphene flakes presented here could be utilized as a novel alternative to conventional Monte Carlo simulation methods used to study graphene growth. Flake ; Dangling bond ; Graphene ; Materials science ; Energy (signal processing) ; Nanotechnology ; Composite material ; Optoelectronics ; Physics ; Silicon Graphene research and applications ; Graphite, nuclear technology, radiation studies ; Recycling and Waste Management Techniques
Authors: Ching‐Seng Ang | Nicholas A. Williamson | Chelsea Dumesny | Michael G. Leeming | Keshava K. Datta | Swati Varshney | Mehrdad Nikfarjam | Hong He
Source: Cancers (article, 2025-11-25)
Field: Chemistry — Spectroscopy

Evaluation of Nanoparticle-Based Plasma Enrichment on Individuals with Primary and Metastatic Pancreatic Cancer <b>Background and Methods</b>: Using a nanoparticle-based enrichment (Proteonano) methodology on human plasma samples, we achieved a substantial increase in identified proteins from ~700 to >5000 proteins compared to neat plasma digest. In a small-scale pilot test, we applied this methodology to a small cohort of plasma samples from pancreatic cancer (PC) patients with different disease stages: (I) primary tumor and (II) metastases, and compared them with healthy controls. Most identified proteins are within the Human Plasma Proteome Project (HPPP) database, and more than 300 proteins are on the list of FDA-approved drug targets. <b>Results</b>: We observed a large and significant increase in ribosomal proteins in the plasma of patients with metastatic PC. ADH1C and ADH1B, both members of the alcohol dehydrogenase family, were particularly upregulated in patients with liver metastasis. Fifteen other predicted secreted and/or cell surface-associated proteins with known cancer associations are also significantly altered and would otherwise go undetected in neat, digested plasma. <b>Conclusions</b>: The significant increase in proteome depth allows a strong foundation for future large-scale experimental and comparative analysis. Lastly, similar conclusions could be reached from comparing different mass spectrometers (Orbitrap Astral and Orbitrap Ascend) and columns (depth and throughput) setups on the same dataset, although the depth approach on the newer Orbitrap Astral instrumentations can reveal additional insights in the plasma proteome. Proteome ; Pancreatic cancer ; Blood proteins ; Cohort ; Cancer ; Proteomics ; Orbitrap ; Ribosomal protein Advanced Proteomics Techniques and Applications ; Clusterin in disease pathology ; vaccines and immunoinformatics approaches
Authors: Labh Singh
Source: International Journal For Multidisciplinary Research (article, 2025-06-17)
Field: Materials Science — Materials Chemistry

The Promise and Pitfalls of Nanotechnology in Environmental Remediation Nanotechnology has emerged as a promising tool in the field of environmental remediation, offering unique solutions to some of the most pressing pollution challenges of our time. With nanomaterials exhibiting exceptional surface properties, reactivity, and specificity, they have been widely explored for removing heavy metals, organic pollutants, and other contaminants from air, water, and soil. However, despite these advantages, significant scientific, regulatory, and ethical concerns persist regarding their use in real-world environments. This perspective critically explores both the potential and pitfalls of applying nanotechnology in remediation, highlighting its groundbreaking prospects while emphasizing the need for caution, long-term risk assessment, and policy development. Bridging the gap between research and practical implementation will require interdisciplinary collaboration, safer design strategies, and global regulatory frameworks. Responsible innovation one that balances performance with precaution is essential to ensure that nanotechnology becomes a tool not only of remediation but also of sustainable progress. Environmental remediation ; SAFER ; Nanotechnology ; Bridging (networking) ; Environmental planning ; Sustainable development ; Engineering ethics ; Risk analysis (engineering) ; Environmental science ; Engineering ; Computer science ; Business ; Political science ; Materials science ; Ecology ; Contamination Nanoparticles: synthesis and applications
Authors: Xiangjun Chen | Qinhong Shi
Source:  (article, 2025-05-09)
Field: Materials Science — Materials Chemistry

Research on Optimization of Food Process Parameters Based on Machine Learning The article is limited to machine learning and discusses parameter optimisation in the bread production process. This paper introduces the basic technical parameters used in bread manufacturing: the moisture ratio, yeast fermentation time, kneading rate and baking temperature, as well as baking time, and how these parameters affect bread quality. The research collected plenty of process parameters and quality index through experiments, and established a prediction model of regression analysis, support vector machine and random forest based on machine learning theory. Meanwhile, the parameters were modified with genetic optimization and particle swarm optimization. It is possible to accurately analyze the relationship between the different process parameters and the quality of bread, optimize the production process, and upgrade the product quality, which has applicational value to the food production. Particle swarm optimization ; Support vector machine ; Upgrade ; Process (computing) ; Production (economics) ; Product (mathematics) ; Quality (philosophy) Machine Learning in Materials Science
Authors: Jakob Heyer‐Müller | Robin Schiemer | Matthias Lopinski | Cunlong Wang | Fabian Willems | Lars Robbel | Michael Schmitt | Jürgen Hubbuch
Source: Engineering in Life Sciences (article, 2025-09-01)
Field: Chemistry — Analytical Chemistry

A Novel Raman‐Chromatography Assembly for Automated Calibration and In‐Line Monitoring in Bioprocessing Optical spectroscopic techniques have been successfully employed in bioprocessing as process analytical technology for real-time process monitoring in numerous applications. The implementation of spectroscopy-based PAT techniques commonly necessitates the generation of representative process data used for calibration and validation of multivariate statistical models for analyzing the sample composition in real-time. To automate the generation of such data, we present a novel assembly of a commercially available chromatography system in combination with a Raman spectrometer for fast and accurate acquisition of Raman spectra. Using the ultra-/diafiltration (UF/DF) process as a case study, our methodology involved the preparation of representative calibration and validation mixtures of phosphate and citrate buffer and lysozyme as a model protein. Chemometric PLS models were calibrated and validated using these datasets, and applied to in-line recorded Raman spectra during a UF/DF experiment. The primary results demonstrated that the novel assembly provides robust and precise offline measurement of Raman spectra, which directly compare with in-line record data. The chemometric PLS models showed good alignment in calibration and validation datasets (R2 and Q2), and could be used to simultaneously monitor the buffer and protein concentrations in real-time during UF/DF. This study provides a simple, commercially available setup for automated acquisition of Raman spectra and demonstrates its straightforward application to bioprocess monitoring. Bioprocess ; Process analytical technology ; Calibration ; Chemometrics ; Raman spectroscopy ; Process (computing) ; Partial least squares regression Spectroscopy and Chemometric Analyses ; Viral Infectious Diseases and Gene Expression in Insects ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Rui Qiao | Hechen Li | He Bian | Xin Lei | Baozhen Shan
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-06-02)
Field: Chemistry — Spectroscopy

De Novo sequencing-assisted homology search for DIA data analysis enables low abundance peptide variants discovery Abstract Data-independent acquisition mass spectrometry (DIA-MS) has emerged as a powerful approach for comprehensive proteome profiling. Spectral library search and library-free search are the two major approaches for DIA data analysis. The spectral library search requires high-quality spectral libraries derived from the search results of data-dependent acquisition (DDA) experiments, while library-free approaches rely on prediction models to generate in silico libraries. Both methodologies constrain the search space to the peptide list in the database, limiting the discovery of variant peptides arising from genetic variations or mutations. We present a novel computational method DIAVariant designed to identify peptide sequence variants directly and solely from complex DIA spectra while rigorously controlling the false discovery rate. Our experimental results demonstrate that DIAVariant successfully identifies sequence variants previously detected through proteogenomic approaches, while maintaining high specificity across multiple datasets. When integrated with existing DIA database search solutions, our approach constitutes a comprehensive analytical workflow capable of identifying peptides both represented within reference protein databases and those arising from sequence variations not captured in standard databases. Computational biology ; Homology (biology) ; Biology ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Glycosylation and Glycoproteins Research
Authors: Fangyuan Zhao | Zhi-Gang Feng | Shuonan Shan
Source: Sensors (article, 2025-08-06)
Field: Materials Science — Surfaces, Coatings and Films

High-Density Arrayed Spectrometer with Microlens Array Grating for Multi-Channel Parallel Spectral Analysis To enable multi-channel parallel spectral analysis in array-based devices such as micro-light-emitting diodes (Micro-LEDs) and line-scan spectral confocal systems, the development of compact array spectrometers has become increasingly important. In this work, a novel spectrometer architecture based on a microlens array grating (MLAG) is proposed, which addresses the major limitations of conventional spectrometers, including limited parallel detection capability, bulky structures, and insufficient spatial resolution. By integrating dispersion and focusing within a monolithic device, the system enables simultaneous acquisition across more than 2000 parallel channels within a 10 mm × 10 mm unit consisting of an <i>f</i> = 4 mm microlens and a 600 lines/mm blazed grating. Optimized microlens and aperture alignment allows for flexible control of the divergence angle of the incident light, and the system theoretically achieves nanometer-scale spectral resolution across a 380-780 nm wavelength range, with inter-channel measurement deviation below 1.25%. Experimental results demonstrate that this spectrometer system can theoretically support up to 2070 independently addressable subunits. At a wavelength of 638 nm, the coefficient of variation (CV) of spot spacing among array elements is as low as 1.11%, indicating high uniformity. The spectral repeatability precision is better than 1.0 nm, and after image enhancement, the standard deviation of the diffracted light shift is reduced to just 0.26 nm. The practical spectral resolution achieved is as fine as 3.0 nm. This platform supports wafer-level spectral screening of high-density Micro-LEDs, offering a practical hardware solution for high-precision industrial inline sorting, such as Micro-LED defect inspection. Microlens ; Spectrometer ; Grating ; Optics ; Materials science ; Spectral resolution ; Diffraction grating ; Optoelectronics ; Digital micromirror device ; Wavelength ; Michelson interferometer ; Interferometry ; Physics ; Spectral line ; Lens (geology) Optical Coatings and Gratings ; Photonic and Optical Devices ; Advanced optical system design
Authors: Mingxuan Gao | Shubham Gupta | Wenxian Yang | Rongshan Yu | Hannes Röst
Source: Communications Chemistry (article, 2025-11-20)
Field: Chemistry — Spectroscopy

Scoring information integration with statistical quality control enhanced cross-run analysis of data-independent acquisition proteomics data The peptide-centric strategy is widely applied in data-independent acquisition (DIA) proteomics to analyze multiplexed MS2 spectra. However, current software tools often rely on single-run data for peptide peak identification, leading to inconsistent quantification across heterogeneous datasets. Match-between-runs (MBR) algorithms address this by aligning peaks or elution profiles post-analysis, but they are often ad hoc and lack statistical frameworks for controlling peak quality, causing false positives and reduced quantitative reproducibility. Here we present DreamDIAlignR, a cross-run peptide-centric tool that integrates peptide elution behavior across runs with a deep learning peak identifier and alignment algorithm for consistent peak picking and FDR-controlled scoring. DreamDIAlignR outperformed state-of-the-art MBR methods, identifying up to 21.2% more quantitatively changing proteins in a benchmark dataset and 36.6% more in a cancer dataset. Additionally, DreamDIAlignR establishes an improved methodology for performing MBR compatible with existing DIA analysis tools, thereby enhancing the overall quality of DIA analysis. False positive paradox ; Benchmark (surveying) ; Identifier ; Quantitative proteomics ; Software ; Proteomics ; Data acquisition ; Data quality Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; vaccines and immunoinformatics approaches
Authors: Javaria Kousar | Liping Yang | Jiale Xiang | Qingwei Mao | Xihui Bian
Source: Foods (article, 2025-12-11)
Field: Chemistry — Analytical Chemistry

Beluga Optimization Algorithm for Near-Infrared Spectral Variable Selection of Complex Samples Near-infrared (NIR) spectroscopy combined with multivariate calibration methods is widely used for the quantitative analysis of complex samples. However, the high-dimensional redundancy of spectra may compromise model predictive accuracy, making it necessary to select variables before modeling. The beluga whale optimization (BWO) algorithm is known for its fast convergence speed, high accuracy and few parameters. The present study employed the discretized BWO (DBWO) algorithm in conjunction with partial least squares (PLS) for spectral quantitative analysis of complex samples. After the optimal number of iterations and transfer function were determined, the PLS models were established based on the randomization test (RT), uninformative variable elimination (UVE) and Monte Carlo uninformative variable elimination (MC-UVE). The predictive performance of DBWO-PLS was compared with full-spectrum PLS, RT-PLS, UVE-PLS and MC-UVE-PLS using wheat, tablet and cocoa bean samples. The results show that all four variable selection methods enhanced model prediction accuracy, with the DBWO-PLS model notably achieving superior performance. Feature selection ; Variable elimination ; Partial least squares regression ; Monte Carlo method ; Variable (mathematics) ; Convergence (economics) ; Selection (genetic algorithm) ; Calibration Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: José Pedro Carvalho | Ana Rita O. Rodrigues | Tiago Ferreira | Vânia Gomes | Francisco B. Pereira | Charles Ferreira Martins | Madalena Martins | Tarsila G. Castro | F.A.M.M. Gonçalves | André da Costa | Ana Tinoco | Artur Cavaco-Paulo
Source: Cell Reports Physical Science (article, 2025-10-01)
Field: Materials Science — Biomaterials

Peptide-keratin interactions for enhanced hair properties Summary Fusion peptides inspired by natural elastomeric proteins are fused to a keratin peptide (KP) and tested on damaged hair. Here, we report thermal and mechanical tests conducted on treated damaged hair in conjunction with the peptides physicochemical properties, resulting in a statistical multiparametric analysis. Among the parameters, the Youngs modulus and the keratin helix denaturation temperature show high potential for selecting peptides with notable hair recovery performance. Here, we demonstrate that KP::Resilin(1) is the best candidate for recovering or enhancing hairs mechanical and thermal properties. Additionally, KP::Resilin(1) shows color-protection potential against high-temperature procedures. The cosmetics industry is being revolutionized by bioinspired peptides, which stand out as sustainable and viable alternatives to chemical ingredients. They enable the creation of safer and more effective cosmetics, reduce the need for synthetic ingredients, and provide consumers with high-performance products that minimize irritation while lowering environmental impact throughout the products life cycle. Keratin ; Cosmetics ; Peptide ; Skin irritation ; Elastomer ; Helix (gastropod) Silk-based biomaterials and applications ; Dyeing and Modifying Textile Fibers ; melanin and skin pigmentation
Authors: Cong Zhang | Heng Zhong | Zikui Shen | Hongyan Zheng | Yibo Yang | Jun Su | Xiaotao Fu
Source: Materials (article, 2025-09-08)
Field: Materials Science — Materials Chemistry

Accelerated Corrosion and Multimodal Characterization of Steel Pins in High-Voltage AC Insulators Under Multi-Stress Conditions Ensuring the long-term electro-mechanical reliability of high-voltage alternating current (HVAC) insulator strings requires a detailed understanding of how multiple environmental and electrical stressors influence the corrosion behavior of hot-dip galvanized steel fittings. In this study, a three-factor, three-level L9(3<sup>3</sup>) orthogonal accelerated corrosion test was conducted to systematically evaluate the individual and interactive effects of marine salt deposition (0-10 g m<sup>-2</sup> day<sup>-1</sup>), acetic acid pollution (0-8 µg m<sup>-3</sup>), and 50 Hz AC leakage current (0-10 mA) on miniature pin-type assemblies. A comprehensive post-corrosion characterization approach was employed. The results revealed that chloride loading from salt deposition was the dominant contributor to corrosion. However, the synergistic interaction between salt and leakage current led to an acceleration in zinc depletion compared to the additive effect of the individual factors. A quadratic regression model with a high correlation coefficient was developed to predict corrosion volume per unit area. The findings offer a mechanistic explanation for field-reported failures in coastal power grids and provide actionable guidance for optimizing corrosion-resistant coatings and implementing electrical mitigation strategies. Corrosion ; Galvanization ; Deposition (geology) ; Insulator (electricity) ; Leakage (economics) ; Zinc ; Characterization (materials science) ; Chloride High voltage insulation and dielectric phenomena ; Concrete Corrosion and Durability ; Corrosion Behavior and Inhibition
Authors: Miri Park | Annette Somborn | Dennis Schlehuber | Hyun Jeong Lim | Volkmar Keuter
Source: Food Chemistry (article, 2025-08-22)
Field: Chemistry — Analytical Chemistry

Non-destructive quantification of lutein and beta-carotene in spinach by Raman spectroscopy under optimized conditions for linear discriminant analysis Leafy vegetables present challenges for Raman-based carotenoid analysis due to strong fluorescence from chlorophyll and the coexistence of complex biomolecules. This study introduces a non-destructive approach combining Raman spectroscopy with Linear Discriminant Analysis (LDA) to classify carotenoid content levels. Arabidopsis thaliana mutants with controlled carotenoid levels were used to build and validate the model, which was then applied to cultivated Spinacia oleracea. Various spectral preprocessing methods and Raman shift subsets were tested to optimize model performance. The LDA model successfully distinguished lutein and β-carotene concentration levels, achieving up to 95.45 % accuracy in Arabidopsis and 90.91 % in spinach. This classification-based strategy offers practical advantages over continuous quantification, particularly in food quality monitoring and nutritional labeling. The findings demonstrate the potential of LDA-assisted Raman spectroscopy as a selective and reliable tool for carotenoid analysis in chlorophyll-rich vegetables, with strong applicability for non-destructive quality control across the food production and distribution chain. Lutein ; Linear discriminant analysis ; Spinach ; beta-Carotene ; Raman spectroscopy ; Chemistry ; Carotenoid ; Food science ; Chromatography ; Analytical Chemistry (journal) ; Mathematics ; Biochemistry ; Physics ; Statistics ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Bee Products Chemical Analysis