jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Eleonora Longhin | Iván Ríos-Mondragón | Espen Mariussen | Congying Zheng | Martí Busquets | Agnieszka Gajewicz | Ole-Bendik Hofshagen | Neus G. Bastús | Víctor Puntes | Mihaela Roxana Cimpan | Sergey Shaposhnikov | Mária Dušinská | Elise Rundén‐Pran
Source: Particle and Fibre Toxicology (article, 2024-12-27)
Field: Materials Science — Materials Chemistry

Hazard assessment of nanomaterials: how to meet the requirements for (next generation) risk assessment The study highlights the potential of in silico models to effectively identify biologically active nanoforms based on their physico-chemical properties, reinforcing previous knowledge on the relevance of certain properties, such as aspect ratio. The potential of implementing in vitro methods into NGRA is underlined, cross-validating three cytotoxicity assessment methods, and showcasing their strength in terms of sensitivity and suitability for the testing of NMs. In silico ; Risk assessment ; Hazard analysis ; Computer science ; Hazard ; Biochemical engineering ; Nanotechnology ; Chemistry ; Materials science ; Reliability engineering ; Engineering Nanoparticles: synthesis and applications
Authors: Le Yu | Mark A. B. Kreutzberger | Thi H. Bui | Maria C. Hancu | Adam C. Farsheed | Edward H. Egelman | Jeffrey D. Hartgerink
Source: Nature Communications (article, 2024-11-29)
Field: Materials Science — Biomaterials

Exploration of the hierarchical assembly space of collagen-like peptides beyond the triple helix The de novo design of self-assembling peptides has garnered significant attention in scientific research. While alpha-helical assemblies have been extensively studied, exploration of polyproline type II helices, such as those found in collagen, remains relatively limited. In this study, we focus on understanding the sequence-structure relationship in hierarchical assemblies of collagen-like peptides, using defense collagen Surfactant Protein A as a model. By dissecting the sequence derived from Surfactant Protein A and synthesizing short collagen-like peptides, we successfully construct a discrete bundle of hollow triple helices. Amino acid substitution studies pinpoint hydrophobic and charged residues that are critical for oligomer formation. These insights guide the de novo design of collagen-like peptides, resulting in the formation of diverse quaternary structures, including discrete and heterogenous bundled oligomers, two-dimensional nanosheets, and pH-responsive nanoribbons. Our study represents a significant advancement in the understanding and harnessing of collagen higher-order assemblies beyond the triple helix. Triple helix ; Polyproline helix ; Collagen helix ; Sequence (biology) ; Peptide ; Helix (gastropod) ; Chemistry ; Peptide sequence ; Amino acid ; Biophysics ; Nanotechnology ; Materials science ; Stereochemistry ; Biochemistry ; Biology Collagen: Extraction and Characterization ; Marine Biology and Environmental Chemistry ; Protein Hydrolysis and Bioactive Peptides
Authors: Amir Ata Saei | Liangliang Sun | Morteza Mahmoudi
Source: PROTEOMICS (review, 2024-09-02)
Field: Materials Science — Biomaterials

The role of protein corona in advancing plasma proteomics The protein corona, a layer of biomolecules forming around nanoparticles in biological environments, critically influences nanoparticle interactions with biosystems, affecting pharmacokinetics and biological outcomes. Initially, the protein corona presented challenges for nanomedicine and nanotoxicology, such as nutrient depletion in cell cultures and masking of nanoparticle-targeting species. However, recent advancements have highlighted its potential in environmental toxicity, proteomics, and immunology. This viewpoint focuses on leveraging the protein corona to enhance the depth of plasma proteome analysis, addressing challenges posed by the high dynamic range of protein concentrations in plasma. The protein corona simplifies sample preparation, enriches low-abundance proteins, and improves proteome coverage. Innovations include using diverse nanoparticles and spiking small molecules to increase the number of quantified proteins. Reproducibility issues across core facilities necessitate standardized protocols. Moreover, top-down proteomics enables proteoform-specific measurements, providing deeper insights into protein corona composition. Future research should aim at improving top-down proteomics techniques and integrating protein corona studies and proteomics for personalized medicine and advanced diagnostics. Proteomics ; Proteome ; Nanomedicine ; Nanotechnology ; Nanotoxicology ; Computational biology ; Corona (planetary geology) ; Chemistry ; Nanoparticle ; Biology ; Materials science ; Biochemistry Nanoparticle-Based Drug Delivery ; Polymer Surface Interaction Studies ; Biosensors and Analytical Detection
Authors: Lakshmi Shenoy | Christopher D. Woodgate | J. B. Staunton | Albert P. Bartók | Charlotte Becquart | Christophe Domain | James R. Kermode
Source: Physical Review Materials (article, 2024-03-22)
Field: Materials Science — Materials Chemistry

Collinear-spin machine learned interatomic potential for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>Fe</mml:mi><mml:mn>7</mml:mn></mml:msub><mml:msub><mml:mi>Cr</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mi>Ni</mml:mi></mml:mrow></mml:math> alloy We have developed a machine learned interatomic potential for the prototypical austenitic steel <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"><a:mrow><a:msub><a:mi>Fe</a:mi><a:mn>7</a:mn></a:msub><a:msub><a:mi>Cr</a:mi><a:mn>2</a:mn></a:msub><a:mi>Ni</a:mi></a:mrow></a:math>, using the Gaussian approximation potential (GAP) framework. This GAP can model the alloy's properties with close to density functional theory (DFT) accuracy, while at the same time allowing us to access larger length and time scales than expensive first-principles methods. We also extended the GAP input descriptors to approximate the effects of collinear spins (spin GAP), and demonstrate how this extended model successfully predicts structural distortions due to antiferromagnetic and paramagnetic spin states. We demonstrate the application of the spin GAP model for bulk properties and vacancies and validate against DFT. These results are a step towards modeling the atomistic origins of ageing in austenitic steels with higher accuracy. Published by the American Physical Society 2024 Antiferromagnetism ; Materials science ; Spins ; Machine learning ; Paramagnetism ; Spin (aerodynamics) ; Austenite ; Density functional theory ; Interatomic potential ; Condensed matter physics ; Algorithm ; Thermodynamics ; Physics ; Computer science ; Quantum mechanics ; Molecular dynamics ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Athulya S. Palakkal | Saad Aldin Mohamed | Jianwen Jiang
Source: Chem & Bio Engineering (article, 2024-10-29)
Field: Chemistry — Inorganic Chemistry

Hydrostable Fluorinated Metal–Organic Frameworks for CO<sub>2</sub> Capture from a Wet Flue Gas: Multiscale Computational Screening Metal-organic frameworks (MOFs) are promising adsorbents for CO<sub>2</sub> capture due to readily tunable porosity and diverse functionality; however, their performance is deteriorated by the presence of H<sub>2</sub>O in a flue gas. Fluorinated MOFs (FMOFs) may impede H<sub>2</sub>O interaction with frameworks and enhance CO<sub>2</sub> adsorption under humid conditions. In this study, a multiscale computational screening study is reported to identify the top FMOFs for CO<sub>2</sub> capture from a wet flue gas. Initially, geometric properties as well as heats of H<sub>2</sub>O adsorption are used to shortlist FMOFs with a suitable pore size and weak H<sub>2</sub>O affinity. Then, grand-canonical Monte Carlo simulations are conducted for adsorption of a CO<sub>2</sub>/N<sub>2</sub>/H<sub>2</sub>O mixture with 60% relative humidity in 5061 FMOFs. Based on the adsorption performance, 19 FMOFs are identified as top candidates. It is revealed that the position of F atom, rather than the amount, affects CO<sub>2</sub> adsorption; moreover, N-decorated FMOFs are preferential for selective CO<sub>2</sub> adsorption. Finally, the hydrostability of the top FMOFs is confirmed by first-principles molecular dynamics simulations. From a microscopic level, this study provides quantitative structure-performance relationships, discovers hydrostable FMOFs with high CO<sub>2</sub> capture performance from a wet flue gas, and would facilitate the development of new MOFs toward efficient CO<sub>2</sub> capture under humid conditions. Flue gas ; Metal-organic framework ; Environmental science ; Materials science ; Environmental chemistry ; Chemical engineering ; Waste management ; Chemistry ; Adsorption ; Physical chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Inorganic Fluorides and Related Compounds
Authors: Dongyu Zhu | Junying Han | Chengzhong Liu | Jianping Zhang | Yanni Qi
Source: Frontiers in Plant Science (article, 2024-02-12)
Field: Chemistry — Analytical Chemistry

Modeling of flaxseed protein, oil content, linoleic acid, and lignan content prediction based on hyperspectral imaging Protein, oil content, linoleic acid, and lignan are several key indicators for evaluating the quality of flaxseed. In order to optimize the testing methods for flaxseed's nutritional quality and enhance the efficiency of screening high-quality flax germplasm resources, we selected 30 flaxseed species widely cultivated in Northwest China as the subjects of our study. Firstly, we gathered hyperspectral information regarding the seeds, along with data on protein, oil content, linoleic acid, and lignan, and utilized the SPXY algorithm to classify the sample set. Subsequently, the spectral data underwent seven distinct preprocessing methods, revealing that the PLSR model exhibited superior performance after being processed with the SG smoothing method. Feature wavelength extraction was carried out using the Successive Projections Algorithm (SPA) and the Competitive Adaptive Reweighted Sampling (CARS). Finally, four quantitative analysis models, namely Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Multiple Linear Regression (MLR), and Principal Component Regression (PCR), were individually established. Experimental results demonstrated that among all the models for predicting protein content, the SG-CARS-MLR model predicted the best, with and of 0.9563 and 0.9336, with the corresponding Root Mean Square Error Correction (RMSEC) and Root Mean Square Error Prediction (RMSEP) of 0.4892 and 0.5616, respectively. In the optimal prediction models for oil content, linoleic acid and lignan, the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msubsup><mml:mtext>R</mml:mtext><mml:mrow><mml:mtext>p</mml:mtext></mml:mrow><mml:mn>2</mml:mn></mml:msubsup></mml:mrow></mml:math> was 0.8565, 0.8028, 0.9343, and the RMSEP was 0.8682, 0.5404, 0.5384, respectively. The study results show that hyperspectral imaging technology has excellent potential for application in the detection of quality characteristics of flaxseed and provides a new option for the future non-destructive testing of the nutritional quality of flaxseed. Partial least squares regression ; Hyperspectral imaging ; Linoleic acid ; Mathematics ; Mean squared error ; Content (measure theory) ; Lignan ; Statistics ; Artificial intelligence ; Pattern recognition (psychology) ; Chemistry ; Computer science ; Botany ; Biology ; Fatty acid Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Phytochemicals and Antioxidant Activities
Authors: Changming Li | Yong Tan | Chunyu Liu | Wenjing Guo
Source: Sensors (article, 2024-05-09)
Field: Chemistry — Analytical Chemistry

Rice Origin Tracing Technology Based on Fluorescence Spectroscopy and Stoichiometry The origin of agricultural products is crucial to their quality and safety. This study explored the differences in chemical composition and structure of rice from different origins using fluorescence detection technology. These differences are mainly affected by climate, environment, geology and other factors. By identifying the fluorescence characteristic absorption peaks of the same rice seed varieties from different origins, and comparing them with known or standard samples, this study aims to authenticate rice, protect brands, and achieve traceability. The study selected the same variety of rice seed planted in different regions of Jilin Province in the same year as samples. Fluorescence spectroscopy was used to collect spectral data, which was preprocessed by normalization, smoothing, and wavelet transformation to remove noise, scattering, and burrs. The processed spectral data was used as input for the long short-term memory (LSTM) model. The study focused on the processing and analysis of rice spectra based on NZ-WT-processed data. To simplify the model, uninformative variable elimination (UVE) and successive projections algorithm (SPA) were used to screen the best wavelengths. These wavelengths were used as input for the support vector machine (SVM) prediction model to achieve efficient and accurate predictions. Within the fluorescence spectral range of 475-525 nm and 665-690 nm, absorption peaks of nicotinamide adenine dinucleotide (NADPH), riboflavin (B2), starch, and protein were observed. The origin tracing prediction model established using SVM exhibited stable performance with a classification accuracy of up to 99.5%.The experiment demonstrated that fluorescence spectroscopy technology has high discrimination accuracy in tracing the origin of rice, providing a new method for rapid identification of rice origin. Support vector machine ; Biological system ; Tracing ; Smoothing ; Principal component analysis ; Spectroscopy ; Traceability ; Fluorescence ; Computer science ; Analytical Chemistry (journal) ; Chemistry ; Pattern recognition (psychology) ; Artificial intelligence ; Optics ; Physics ; Biology ; Chromatography Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Identification and Quantification in Food
Authors: Yingying Zhang | Li Xue | Meiqing Wang | Tao Xu | Kai Huang | Yuanhao Sun | Quanchun Yuan | Xiaohui Lei | Yannan Qi | Xiaolan Lv
Source: Frontiers in Plant Science (article, 2024-10-08)
Field: Chemistry — Analytical Chemistry

Early detection and lesion visualization of pear leaf anthracnose based on multi-source feature fusion of hyperspectral imaging Pear anthracnose, caused by Colletotrichum bacteria, is a severe infectious disease that significantly impacts the growth, development, and fruit yield of pear trees. Early detection of pear anthracnose before symptoms manifest is of great importance in preventing its spread and minimizing economic losses. This study utilized hyperspectral imaging (HSI) technology to investigate early detection of pear anthracnose through spectral features, vegetation indices (VIs), and texture features (TFs). Healthy and diseased pear leaves aged 1 to 5 days were selected as subjects for capturing hyperspectral images at various stages of health and disease. Characteristic wavelengths (OWs1 and OWs2) were extracted using the Successive Projection Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) algorithm. Significant VIs were identified using the Random Forest (RF) algorithm, while effective TFs were derived from the Gray Level Co-occurrence Matrix (GLCM). A classification model for pear leaf early anthracnose disease was constructed by integrating different features using three machine learning algorithms: Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Back Propagation Neural Network (BPNN). The results showed that: the classification identification model constructed based on the feature fusion performed better than that of single feature, with the OWs2-VIs-TFs-BPNN model achieving a highest accuracy of 98.61% in detection and identification of pear leaf early anthracnose disease. Additionally, to intuitively and effectively monitor the progression and severity of anthracnose in pear leaves, the visualization of anthracnose lesions was achieved using Successive Maximum Angle Convex Cone (SMACC) and Spectral Information Divergence (SID) techniques. According to our research results, the fusion of multi-source features based on hyperspectral imaging can be a reliable method to detect early asymptomatic infection of pear leaf anthracnose, and provide scientific theoretical support for early warning and prevention of pear leaf diseases. PEAR ; Hyperspectral imaging ; Artificial intelligence ; Support vector machine ; Pattern recognition (psychology) ; Computer science ; Mathematics ; Biology ; Horticulture Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Essential Oils and Antimicrobial Activity
Authors: Yingtao Wang | Xian Zhang
Source: Micromachines (article, 2024-06-06)
Field: Materials Science — Materials Chemistry

Experimental and Theoretical Investigations of Direct and Indirect Band Gaps of WSe2 Low-dimension materials such as transition metal dichalcogenides (TMDCs) have received extensive research interest and investigation for electronic and optoelectronic applications. Due to their unique widely tunable band structures, they are good candidates for next-generation optoelectronic devices. Particularly, their photoluminescence properties, which are fundamental for optoelectronic applications, are highly sensitive to the nature of the band gap. Monolayer TMDCs in the room temperature range have presented a direct band gap behavior and bright photoluminescence. In this work, we investigate a popular TMDC material WSe<sub>2</sub>'s photoluminescence performance using a Raman spectroscopy laser with temperature dependence. With temperature variation, the lattice constant and the band gap change dramatically, and thus the photoluminescence spectra are changed. By checking the photoluminescence spectra at different temperatures, we are able to reveal the nature of direct-to-indirect band gap in monolayer WSe<sub>2</sub>. We also implemented density function theory (DFT) simulations to computationally investigate the band gap of WSe<sub>2</sub> to provide comprehensive evidence and confirm the experimental results. Our study suggests that monolayer WSe<sub>2</sub> is at the transition boundary between the indirect and direct band gap at room temperature. This result provides insights into temperature-dependent optical transition in monolayer WSe<sub>2</sub> for quantum control, and is important for cultivating the potential of monolayer WSe<sub>2</sub> in thermally tunable optoelectronic devices operating at room temperature. Photoluminescence ; Monolayer ; Direct and indirect band gaps ; Band gap ; Materials science ; Optoelectronics ; Raman spectroscopy ; Condensed matter physics ; Nanotechnology ; Optics ; Physics 2D Materials and Applications ; Graphene research and applications ; MXene and MAX Phase Materials
Authors: Katleho Keneuwe Khoaele | Ipoteng Justice Mphahlele | Oluwatoyin Joseph Gbadeyan | Bruce Sitholé | Viren Chunilall
Source: International Journal of Polymer Science (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Current Approaches on Natural Fiber Reinforcement Surface Treatment for Construction Material Application The conversion of waste into value‐added products is critical to long‐term sustainability as waste disposal costs, landfilling space, and climate change would be reduced. Research and production industries investigate the possible use of natural fibers as a substitute for fossil fuels. Natural fiber‐reinforced polymer composites’ physical and mechanical properties can be altered by natural fiber treatment, and the treatment choice relies on the desired properties and performance. In addition to cost‐profitability, environmental effects, and required performance, composites can be designed based on the product conditions. Disadvantages such as incompatibility of the hydrophilic natural fiber reinforcements with the hydrophobic polymer matrix often result in the tendency of the composite to have high moisture absorption, poor thermal stability, crystallinity, and mechanical strength. These factors critically impair the performance of the natural fibers as reinforcements in polymer composites, hence resulting in less‐favourable mechanical and physical properties. Physical, chemical, and biological treatments were examined to determine their effect on the durability and thermal, physical, and mechanical properties. It was observed that plasma treatment for bamboo fibers resulted in interfibral spaces and anchoring mechanisms between the resin and fibers, leading to increased load transfer. Chemical treatment at various concentrations improved the surface smoothness. The best mechanical, physical, thermal, and chemical properties for application as reinforcement in polymer composites were obtained at 6% for alkali treatments. Enzymatic treatment showed a significant impact on the impact and flexural strength of the composites. Factors such as duration, concentration, and extent of treatment need to be established for effective treatment, as prolonged treatments may damage the NFs’ properties. This review comprehensively highlights the potential utilization of current natural fiber treatment methods for the potential of natural fibers as value‐added products for construction materials, considering their economical affordability, production of lightweight materials, sustainability, utilization of readily available waste material, and nature preservation. Furthermore, the three natural fiber treatment approaches for improving fiber‐matrix adhesion are discussed. The physical and mechanical properties of composites are highlighted, which aids in evaluating suitability for construction materials. Materials science ; Reinforcement ; Natural (archaeology) ; Surface (topology) ; Fiber ; Composite material ; Construction engineering ; Engineering ; Geology ; Geometry ; Mathematics Natural Fiber Reinforced Composites ; Advanced Cellulose Research Studies ; Nanocomposite Films for Food Packaging