jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: André Mazega | Montserrat Fortuny | Giovana Signori-Iamin | Roberto Aguado | Quim Tarrés | Alexandre F. Santos | Marc Delgado‐Aguilar
Source: Cellulose (article, 2024-03-16)
Field: Materials Science — Biomaterials

Near-infrared spectroscopy and multivariate analysis as real-time monitoring strategy of TEMPO-mediated oxidation of cellulose fibers from different feedstocks Abstract The regioselective oxidation of the primary hydroxyl groups of cellulose, usually mediated by the (2,2,6,6-tetramethylpiperidin-1-yl)oxyl radical (TEMPO), is highly popular in the scientific literature. However, the lack of efficient monitoring techniques imposes a severe limitation to its upscaling. This work involves a portable, user-friendly near-infrared spectroscopy device, optimized preprocessing techniques, and multivariate calibration to quickly estimate the carboxyl group content of modified cellulose ( i.e. , the extent of the oxidation). For that, bleached pulps from eucalyptus, pine, hemp, and sisal were submitted to TEMPO-mediated oxidation, varying the dosage of spent oxidizer (NaClO) and thus attaining samples of different values of carboxyl group content. These values were related to near-infrared spectra (908–1676 nm) by Partial Least Squares regression, yielding cross-validation coefficients (R CV 2 ) above 0.97 for wood pulps, 0.95 for sisal pulp, and 0.91 for hemp pulp. Based on the residual prediction deviation, the model for each pulp was found to show good predictability. Nonetheless, the overall regression model, comprising the four different materials, was unreliable. In light of this, spectra were submitted to principal components analysis (PCA), hinting that pulps could be classified in terms of their hemicellulose to cellulose ratio. Considering all the statistical parameters, the overall proposal presented here begins with a PCA—Linear Discriminant Analysis model to classify the sample by its fiber type, subsequently selecting a specific regression model for that class. Overall, the presented models in this work allow the determination of the extent of oxidation of different cellulosic feedstocks, expressed as carboxyl content, in a fast and simple approach using a benchtop near-infrared equipment. Cellulose ; Materials science ; Infrared spectroscopy ; Infrared ; Spectroscopy ; Cellulose fiber ; Multivariate statistics ; Fourier transform infrared spectroscopy ; Chemical engineering ; Analytical Chemistry (journal) ; Composite material ; Chemistry ; Organic chemistry ; Computer science ; Optics ; Engineering ; Machine learning Advanced Cellulose Research Studies ; Spectroscopy and Chemometric Analyses ; Dyeing and Modifying Textile Fibers
Authors: Yu‐Liang Tsai | Jialei Song | Rachel Shi | Bernd Knöll | Christopher V. Synatschke
Source: Advanced Healthcare Materials (review, 2024-11-14)
Field: Materials Science — Biomaterials

A Roadmap of Peptide‐Based Materials in Neural Regeneration Injuries to the nervous system lead to irreversible damage and limited functional recovery. The peripheral nervous system (PNS) can self-regenerate to some extent for short nerve gaps. In contrast, the central nervous system (CNS) has an intrinsic limitation to self-repair owing to its convoluted neural microenvironment and inhibitory response. The primary phase of CNS injury, happening within 48 h, results from external impacts like mechanical stress. Afterward, the secondary phase of the injury occurs, originating from neuronal excitotoxicity, mitochondrial dysfunction, and neuroinflammation. No golden standard to treat injured neurons exists, and conventional medicine serves only as a protective approach to alleviating the symptoms of chronic injury. Synthetic peptides provide a promising approach for neural repair, either as soluble drugs or by using their intrinsic self-assembly propensity to serve as an extracellular matrix (ECM) mimic for cell adhesion and to incorporate bioactive epitopes. In this review, an overview of nerve injury models, common in vitro models, and peptide-based therapeutics such as ECM mimics is provided. Due to the complexity of treating neuronal injuries, a multidisciplinary collaboration between biologists, physicians, and material scientists is paramount. Together, scientists with complementary expertise will be required to formulate future therapeutic approaches for clinical use. Neuroscience ; Excitotoxicity ; Neuroinflammation ; Regeneration (biology) ; Central nervous system ; Nervous system ; Neural cell ; Medicine ; Biology ; Inflammation ; Cell ; Glutamate receptor ; Cell biology ; Immunology ; Internal medicine Supramolecular Self-Assembly in Materials ; RNA Interference and Gene Delivery ; Antimicrobial Peptides and Activities
Authors: P. Govindarajan | S. Rajesh | Karthikeyan Subramanian | Sivasubramanian Palanisamy | Carlo Santulli | Cristiano Fragassa
Source: International Journal of Polymer Science (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Effect of Stacking Sequence on Mechanical and Water Absorption Characteristics of Jute/Banana/Basalt Fabric Aluminium Fibre Laminates With Diamond Microexpanded Mesh This research focuses on hybrid woven fibre metal laminates (HWFMLs), specifically on the innovative hybridization of natural woven fibres (NWFs) with aluminium diamond microexpandable mesh (Al‐DMEM) in thin laminates. More specifically, it investigates the effect on the mechanical and water absorption properties of incorporating basalt (B), jute (J), banana (Bn) fibres, and Al‐DMEM as reinforcing materials in a traditional epoxy resin. Several stacking sequences were compared and discussed. The basalt‐banana‐aluminium‐banana‐basalt (BBnABnB) (five layers) laminate exhibited remarkable compressive strength (25.7 MPa), interlaminar shear strength (25.2 MPa), Izod impact strength (2.98 J), tensile strength (42.8 MPa), and tensile modulus (3.1 GPa). Other laminates, despite their limited tensile strain, still offered advantages in other loading modes. In particular, basalt‐jute‐aluminium‐jute‐basalt (BJAJB) (five layers) laminate showed notable performance with a compressive strength of 16.70 MPa and interlaminar shear strength of 20.85 MPa. In comparison, the basalt‐aluminium‐basalt (BAB) (three layers) demonstrated compressive strength equal to 6.87 MPa and interlaminar shear strength (ILSS) equal to 9.07 MPa. Materials science ; Stacking ; Basalt ; Aluminium ; Absorption of water ; Composite material ; Diamond ; Absorption (acoustics) ; Geology ; Geochemistry Natural Fiber Reinforced Composites ; Advanced Cellulose Research Studies ; Electrospun Nanofibers in Biomedical Applications
Authors: Yiyi Shen | Daoming Zhang | Yiran Lu | Qingli Liang | Ziyue Zhao | Xiaoxing Li | Miaomiao Wang | Baokun Tang
Source: Separation Science Plus (article, 2024-09-10)
Field: Chemistry — Spectroscopy

Investigation of the Extraction of Chlorogenic Acid From Honeysuckle and Its Biological Activity Based on Deep Eutectic Solvents ABSTRACT Honeysuckle, a traditional Chinese medicine, holds significant medicinal value, making it ubiquitous in the medical field. However, the efficient extraction of chlorogenic acid from Chinese herbal medicine while maintaining its pharmacological activity remains a pressing challenge. To address the issues of environmental pollution and inefficient extraction using traditional solvents, there is a need to develop a novel solvent. Due to their low cost, nontoxic, and environmentally friendly characteristics, deep eutectic solvents can be used for extracting traditional Chinese medicinal materials while preserving the activity of their active ingredients during the extraction process. In this study, we examined the antibacterial activity of chlorogenic acid, a key component of honeysuckle, after extraction using a deep eutectic solvent. We gained preliminary insights into the antibacterial activity of the extract against various bacteria, including Escherichia coli , Staphylococcus aureus , and Pseudomonas aeruginosa . Furthermore, we analyzed the antibacterial activity of extracts of different concentrations in liquid medium, and the morphology of the bacteria after inhibition was analyzed. These findings suggest that deep eutectic solvent (DES) is an efficient, environmentally friendly, and cost‐effective method for extracting the active components of honeysuckle. Moreover, the active components extracted using DES retain robust physiological activity. Honeysuckle ; Chlorogenic acid ; Eutectic system ; Extraction (chemistry) ; Chemistry ; Chromatography ; Organic chemistry ; Medicine Analytical Chemistry and Chromatography ; Chemical and Physical Properties in Aqueous Solutions ; Chromatography in Natural Products
Authors: Ali Sobhani‐Nasab | Hamid Reza Banafshe | Amir Atapour | Mahmood Khaksary Mahabady | Maryam Akbari | Abdolreza Daraei | Yaser Mansoori | Amin Moradi Hasan‐Abad
Source: Frontiers in Medical Technology (review, 2024-01-23)
Field: Materials Science — Materials Chemistry

The use of nanoparticles in the treatment of infectious diseases and cancer, dental applications and tissue regeneration: a review The emergence of nanotechnology as a field of study can be traced back to the 1980s, at which point the means to artificially produce, control, and observe matter on a nanometer level was made viable. Recent advancements in technology have enabled us to extend our reach to the nanoscale, which has presented an unparalleled opportunity to directly target biomolecular interactions. As a result of these developments, there is a drive to arise intelligent nanostructures capable of overcoming the obstacles that have impeded the progress of conventional pharmacological methodologies. After four decades, the gradual amalgamation of bio- and nanotechnologies is initiating a revolution in the realm of disease detection, treatment, and monitoring, as well as unsolved medical predicaments. Although a significant portion of research in the field is still confined to laboratories, the initial application of nanotechnology as treatments, vaccines, pharmaceuticals, and diagnostic equipment has now obtained endorsement for commercialization and clinical practice. The current issue presents an overview of the latest progress in nanomedical strategies towards alleviating antibiotic resistance, diagnosing and treating cancer, addressing neurodegenerative disorders, and an array of applications, encompassing dentistry and tuberculosis treatment. The current investigation also scrutinizes the deployment of sophisticated smart nanostructured materials in fields of application such as regenerative medicine, as well as the management of targeted and sustained release of pharmaceuticals and therapeutic interventions. The aforementioned concept exhibits the potential for revolutionary advancements within the field of immunotherapy, as it introduces the utilization of implanted vaccine technology to consistently regulate and augment immune functions. Concurrently with the endeavor to attain the advantages of nanomedical intervention, it is essential to enhance the unceasing emphasis on nanotoxicological research and the regulation of nanomedications' safety. This initiative is crucial in achieving the advancement in medicine that currently lies within our reach. Nanotechnology ; Commercialization ; Risk analysis (engineering) ; Medicine ; Engineering ethics ; Intensive care medicine ; Computer science ; Engineering ; Business ; Materials science Nanoparticles: synthesis and applications ; Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics
Authors: Giovanni Pireddu | Connie J. Fairchild | Samuel P. Niblett | Stephen J. Cox | Benjamin Rotenberg
Source: Proceedings of the National Academy of Sciences (article, 2024-04-25)
Field: Chemistry — Electrochemistry

Impedance of nanocapacitors from molecular simulations to understand the dynamics of confined electrolytes Nanoelectrochemical devices have become a promising candidate technology across various applications, including sensing and energy storage, and provide new platforms for studying fundamental properties of electrode/electrolyte interfaces. In this work, we employ constant-potential molecular dynamics simulations to investigate the impedance of gold-aqueous electrolyte nanocapacitors, exploiting a recently introduced fluctuation-dissipation relation. In particular, we relate the frequency-dependent impedance of these nanocapacitors to the complex conductivity of the bulk electrolyte in different regimes, and use this connection to design simple but accurate equivalent circuit models. We show that the electrode/electrolyte interfacial contribution is essentially capacitive and that the electrolyte response is bulk-like even when the interelectrode distance is only a few nanometers, provided that the latter is sufficiently large compared to the Debye screening length. We extensively compare our simulation results with spectroscopy experiments and predictions from analytical theories. In contrast to experiments, direct access in simulations to the ionic and solvent contributions to the polarization allows us to highlight their significant and persistent anticorrelation and to investigate the microscopic origin of the timescales observed in the impedance spectrum. This work opens avenues for the molecular interpretation of impedance measurements, and offers valuable contributions for future developments of accurate coarse-grained representations of confined electrolytes. Molecular dynamics ; Electrical impedance ; Electrolyte ; Dynamics (music) ; Chemical physics ; Chemistry ; Computational biology ; Biophysics ; Statistical physics ; Computer science ; Computational chemistry ; Physics ; Biology ; Physical chemistry ; Electrode Electrochemical Analysis and Applications ; Conducting polymers and applications ; Advanced Battery Materials and Technologies
Authors: T. Sathish Kumar | S. Muthuvelammai | N. Jayachandran
Source: International Journal for Research in Applied Science and Engineering Technology (review, 2024-09-30)
Field: Materials Science — Polymers and Plastics

AI in Textiles: A Review of Emerging Trends and Applications Abstract: The integration of Artificial Intelligence (AI) in the textile industry is driving significant advancements across various domains, including manufacturing, design, quality control, and sustainability. This review explores how AI technologies, such as machine learning, computer vision, and predictive analytics, are transforming traditional processes to enhance efficiency, reduce waste, and offer innovative solutions for smart textiles and wearable technology. AI-driven automation is optimizing production workflows, while AI-powered quality control ensures higher accuracy in defect detection and fabric inspection. Additionally, AI is enabling predictive analytics in supply chains, helping manufacturers anticipate demand and manage resources more effectively. The review also highlights AI's role in sustainable textiles, where it supports eco-friendly practices by minimizing waste, promoting resource efficiency, and enabling circular fashion. Despite its transformative potential, AI implementation presents challenges, such as ethical concerns, data quality, and integration with traditional systems. This article discusses the current trends, applications, and future directions of AI in textiles, underscoring its pivotal role in reshaping the industry's future. Data science ; Computer science Textile materials and evaluations
Authors: Nicolae‐Viorel Buchete | Iwona Cicha | Sutapa Dutta | Panagiotis Neofytou
Source: Frontiers in Drug Delivery (review, 2024-03-04)
Field: Materials Science — Biomaterials

Multiscale physics-based in silico modelling of nanocarrier-assisted intravascular drug delivery A rational design of drug nanocarriers supported by <i>in silico</i> modelling tools can improve the efficacy of nanosystem-based intravascular drug delivery (IVDD). Computational model development stems from the vision of replacing conventional (pre)clinical trials with advanced simulations and applies to the development of more efficient nanocarriers for intravascular therapies. To establish a standardized framework for <i>in silico</i> preclinical trials, it is necessary to include <i>in silico</i> tools that can model each experimental stage of a preclinical trial for a respective nanocarrier system and give accurate and verifiable results. This review paper highlights the status of intravascular drug delivery supported by nanocarriers and discusses the modelling stages of a physics-based multiscale modelling framework that should be developed, validated and exploited to address the need for an effective preclinical assessment of nanocarriers for IVDD. Nanocarriers ; In silico ; Drug delivery ; Computer science ; Clinical trial ; Verifiable secret sharing ; Nanotechnology ; Biochemical engineering ; Computational biology ; Bioinformatics ; Engineering ; Materials science ; Biology Nanoparticle-Based Drug Delivery ; Field-Flow Fractionation Techniques ; Microfluidic and Bio-sensing Technologies
Authors: Vikram S. Ingole | Ujwala Kshirsagar | Vikash Singh | Manish Varun Yadav | Bipin Krishna | Roshan Kumar
Source: Computation (article, 2024-12-27)
Field: Chemistry — Analytical Chemistry

A Hybrid Model for Soybean Yield Prediction Integrating Convolutional Neural Networks, Recurrent Neural Networks, and Graph Convolutional Networks Soybean yield prediction is one of the most critical activities for increasing agricultural productivity and ensuring food security. Traditional models often underestimate yields because of limitations associated with single data sources and simplistic model architectures. These prevent complex, multifaceted factors influencing crop growth and yield from being captured. In this line, this work fuses multi-source data—satellite imagery, weather data, and soil properties—through the approach of multi-modal fusion using Convolutional Neural Networks and Recurrent Neural Networks. While satellite imagery provides information on spatial data regarding crop health, weather data provides temporal insights, and the soil properties provide important fertility information. Fusing these heterogeneous data sources embeds an overall understanding of yield-determining factors in the model, decreasing the RMSE by 15% and improving R2 by 20% over single-source models. We further push the frontier of feature engineering by using Temporal Convolutional Networks (TCNs) and Graph Convolutional Networks (GCNs) to capture time series trends, geographic and topological information, and pest/disease incidence. TCNs can capture long-range temporal dependencies well, while the GCN model has complex spatial relationships and enhanced the features for making yield predictions. This increases the prediction accuracy by 10% and boosts the F1 score for low-yield area identification by 5%. Additionally, we introduce other improved model architectures: a custom UNet with attention mechanisms, Heterogeneous Graph Neural Networks (HGNNs), and Variational Auto-encoders. The attention mechanism enables more effective spatial feature encoding by focusing on critical image regions, while the HGNN captures interaction patterns that are complex between diverse data types. Finally, VAEs can generate robust feature representation. Such state-of-the-art architectures could then achieve an MAE improvement of 12%, while R2 for yield prediction improves by 25%. In this paper, the state of the art in yield prediction has been advanced due to the employment of multi-source data fusion, sophisticated feature engineering, and advanced neural network architectures. This provides a more accurate and reliable soybean yield forecast. Thus, the fusion of Convolutional Neural Networks with Recurrent Neural Networks and Graph Networks enhances the efficiency of the detection process. Convolutional neural network ; Computer science ; Graph ; Recurrent neural network ; Artificial intelligence ; Yield (engineering) ; Artificial neural network ; Machine learning ; Theoretical computer science Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Soybean genetics and cultivation
Authors: Lucy C. Schofield | Jordan S. Dialpuri | Garib N. Murshudov | Jon Agirre
Source: Acta Crystallographica Section D Structural Biology (review, 2024-08-29)
Field: Chemistry — Spectroscopy

Post-translational modifications in the Protein Data Bank Proteins frequently undergo covalent modification at the post-translational level, which involves the covalent attachment of chemical groups onto amino acids. This can entail the singular or multiple addition of small groups, such as phosphorylation; long-chain modifications, such as glycosylation; small proteins, such as ubiquitination; as well as the interconversion of chemical groups, such as the formation of pyroglutamic acid. These post-translational modifications (PTMs) are essential for the normal functioning of cells, as they can alter the physicochemical properties of amino acids and therefore influence enzymatic activity, protein localization, protein-protein interactions and protein stability. Despite their inherent importance, accurately depicting PTMs in experimental studies of protein structures often poses a challenge. This review highlights the role of PTMs in protein structures, as well as the prevalence of PTMs in the Protein Data Bank, directing the reader to accurately built examples suitable for use as a modelling reference. Posttranslational modification ; Computational biology ; Chemistry ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Peptidase Inhibition and Analysis ; Ubiquitin and proteasome pathways
Authors: Ding Luo | Zerui Liu | Jin Cao | Yuying Yan | Bing Cao
Source: Energy (article, 2024-08-08)
Field: Materials Science — Materials Chemistry

Performance investigation and optimization of an L-type thermoelectric generator Thermoelectric generator ; Thermoelectric materials ; Thermoelectric effect ; Seebeck coefficient ; Thermal conductivity ; Materials science ; Electrical resistivity and conductivity ; Semiconductor ; Type (biology) ; Generator (circuit theory) ; Optoelectronics ; Mechanical engineering ; Power (physics) ; Composite material ; Electrical engineering ; Thermodynamics ; Physics ; Engineering Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Radiation and Cooling Technologies