jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Abhranil Banerjee | Kanishka Jha | Michal Petrů | Rajeev Kumar | Shubham Sharma | Mandeep Saini | Kahtan A. Mohammed | Abhinav Kumar | Mohamed Abbas | Sayed M. Eldin
Source: Journal of Materials Research and Technology (article, 2023-09-27)
Field: Materials Science — Polymers and Plastics

Fabrication and characterization of weld attributes in hot gas welding of alkali treated hybrid flax fiber and pine cone fibers reinforced poly-lactic acid (PLA) based biodegradable polymer composites: studies on mechanical and morphological properties The contemporary times have witnessed a lot of interest in fabricating green composites because of its biodegradability, durability, affordability, ease of manufacturing and its potential to replace existing commercial goods in the field of engineering applications. The green composites are envisaged to replace synthetic polymer-based composites since the green composites are fabricated using the biodegradable polymers reinforced with natural fibers. In this research work, Flax Fiber and Pine Cone Fibers are reinforced in Poly-lactic Acid (PLA) matrix. The green composites are fabricated using compression moulding technique in different nominations as per the weight fraction such as (90:10), (80:20) and (70:30). It was noticed that a higher tensile strength of 27 MPa is observed in case of group 2 (80:20) specifically for the composite having 80% of matrix and 14% flax and 6% pine cone fibers. An IoT based automatic hot gas welding method was used to join the fibers by considering various input parameters like stand of distance, weld rate, specimen groove geometry, and composition. The mechanical testing of the weld specimens was carried. It was observed that the weld joints having higher stand of distance were observed to have less tensile strength as compared to others and the fracture points of such specimens were observed to be at the weld bead. SEM analysis of the fracture point validated the observations from the mechanical characterization. Materials science ; Composite material ; Ultimate tensile strength ; Welding ; Composite number ; Fiber ; Polymer ; Compression molding ; Mold Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Mechanical Engineering and Vibrations Research
Authors: Dapeng Ye | Libin Wu | Xiaobin Li | Tolulope Opeyemi Atoba | Wenhao Wu | Haiyong Weng
Source: Plants (review, 2023-04-18)
Field: Chemistry — Analytical Chemistry

A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping Non-destructive plant stress phenotyping begins with traditional one-dimensional (1D) spectroscopy, followed by two-dimensional (2D) imaging, three-dimensional (3D) or even temporal-three-dimensional (T-3D), spectral-three-dimensional (S-3D), and temporal-spectral-three-dimensional (TS-3D) phenotyping, all of which are aimed at observing subtle changes in plants under stress. However, a comprehensive review that covers all these dimensional types of phenotyping, ordered in a spatial arrangement from 1D to 3D, as well as temporal and spectral dimensions, is lacking. In this review, we look back to the development of data-acquiring techniques for various dimensions of plant stress phenotyping (1D spectroscopy, 2D imaging, 3D phenotyping), as well as their corresponding data-analyzing pipelines (mathematical analysis, machine learning, or deep learning), and look forward to the trends and challenges of high-performance multi-dimension (integrated spatial, temporal, and spectral) phenotyping demands. We hope this article can serve as a reference for implementing various dimensions of non-destructive plant stress phenotyping. Dimension (graph theory) ; Stress (linguistics) ; Computer science ; Artificial intelligence ; Spectral analysis ; Biological system ; Pattern recognition (psychology) ; Mathematics ; Spectroscopy ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Badiâa Essghaier | Hédia Hannachi | Rihem Nouir | Filomena Mottola | Lucia Rocco
Source: Nanomaterials (article, 2023-06-28)
Field: Materials Science — Materials Chemistry

Green Synthesis and Characterization of Novel Silver Nanoparticles Using Achillea maritima subsp. maritima Aqueous Extract: Antioxidant and Antidiabetic Potential and Effect on Virulence Mechanisms of Bacterial and Fungal Pathogens Novel silver nanoparticles were synthesized based on a simple and non-toxic method by applying the green synthesis technique, using, for the first time, the aqueous extract of an extremophile plant belonging to the <i>Achillea maritima</i> subsp. <i>maritima</i> species. AgNP characterization was performed via UV-Visible, front-face fluorescence spectroscopy, and FTIR and XRD analyses. AgNP formation was immediately confirmed by a color change from yellow to brown and by a surface plasmon resonance peak using UV-Vis spectroscopy at 420 nm. The biosynthesized AgNPs were spherical in shape with a size ranging from approximatively 14.13 to 21.26 nm. The presented silver nanoparticles exhibited strong antioxidant activity following a DPPH assay compared to ascorbic acid, with IC50 values of about 0.089 µg/mL and 22.54 µg/mL, respectively. The AgNPs showed higher antidiabetic capacities than acarbose, by inhibiting both alpha amylase and alpha glucosidase. The silver nanoparticles could affect various bacterial mechanisms of virulence, such as EPS production, biofilm formation and DNA damage. The silver nanoparticles showed no lysozyme activity on the cell walls of Gram-positive bacteria. The AgNPs also had a strong inhibitory effect on the <i>Candida albicans</i> virulence factor (extracellular enzymes, biofilm formation). The microscopic observation showed abnormal morphogenesis and agglomeration of <i>Candida albicans</i> exposed to AgNPs. The AgNPs showed no cytotoxic effect on human cells in an MTT assay. The use of novel silver nanoparticles is encouraged in the formulation of natural antimicrobial and antidiabetic agents. Silver nanoparticle ; DPPH ; Chemistry ; Biofilm ; Surface plasmon resonance ; Nuclear chemistry ; Antioxidant ; Biochemistry ; Biology ; Nanoparticle ; Bacteria ; Materials science ; Nanotechnology Nanoparticles: synthesis and applications ; Essential Oils and Antimicrobial Activity ; Medicinal Plants and Neuroprotection
Authors: Sami Malola | María Francisca Matus | Hannu Häkkinen
Source: The Journal of Physical Chemistry C (article, 2023-08-09)
Field: Materials Science — Materials Chemistry

Theoretical Analysis of the Electronic Structure and Optical Properties of DNA-Stabilized Silver Cluster Ag <sub>16</sub> Cl <sub>2</sub> in Aqueous Solvent DNA-stabilized silver nanoclusters with 10–30 silver atoms are by construction ideal candidates for biocompatible bright fluorescent emitters, but their electronic structure is not well understood. Here, using density functional theory (DFT), we analyze the ground-state electronic structure and optical absorption of a bright NIR-emitting cluster Ag16Cl2, which is stabilized by two DNA strands of 9-base sequence 5′-CACCTAGCG-3′ and whose atomic structure was very recently confirmed to have two chlorides bound to the silver core. We are able to (i) unambiguously assign the charge of this cluster in aqueous solvent, (ii) analyze the details of silver–DNA interactions and their effect on the cluster charge, (iii) analyze the character of low-energy optical absorption peaks and the involved electron orbitals and make a first assessment on circular dichroism, and (iv) evaluate the suitability of various DFT exchange–correlation functionals via benchmarking to experimental optical data. This work lays out a baseline for all future theoretical work to understand the electronic, chiroptical, and fluorescence properties of these fascinating biocompatible nanostructures. Nanoclusters ; Cluster (spacecraft) ; Electronic structure ; Density functional theory ; Superatom ; Absorption (acoustics) ; Chemistry ; Circular dichroism ; Absorption spectroscopy ; Aqueous solution ; Materials science ; Chemical physics ; Crystallography ; Nanotechnology ; Physical chemistry ; Computational chemistry ; Physics Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Pharmacological Effects and Toxicity Studies
Authors: Rui Jiao | Jiaqi Han | Wenbing Huang | Yu Rong | Yang Liu
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2023-06-26)
Field: Materials Science — Materials Chemistry

Energy-Motivated Equivariant Pretraining for 3D Molecular Graphs Pretraining molecular representation models without labels is fundamental to various applications. Conventional methods mainly process 2D molecular graphs and focus solely on 2D tasks, making their pretrained models incapable of characterizing 3D geometry and thus defective for downstream 3D tasks. In this work, we tackle 3D molecular pretraining in a complete and novel sense. In particular, we first propose to adopt an equivariant energy-based model as the backbone for pretraining, which enjoys the merits of fulfilling the symmetry of 3D space. Then we develop a node-level pretraining loss for force prediction, where we further exploit the Riemann-Gaussian distribution to ensure the loss to be E(3)-invariant, enabling more robustness. Moreover, a graph-level noise scale prediction task is also leveraged to further promote the eventual performance. We evaluate our model pretrained from a large-scale 3D dataset GEOM-QM9 on two challenging 3D benchmarks: MD17 and QM9. Experimental results demonstrate the efficacy of our method against current state-of-the-art pretraining approaches, and verify the validity of our design for each proposed component. Code is available at https://github.com/jiaor17/3D-EMGP. Equivariant map ; Computer science ; Robustness (evolution) ; Exploit ; Gaussian ; Graph ; Theoretical computer science ; Component (thermodynamics) ; Artificial intelligence ; Machine learning ; Mathematics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Computational Drug Discovery Methods
Authors: Salvador Castillo‐Gironés | Remi Van Belleghem | Niels Wouters | Sandra Munera | J. Blasco | Wouter Saeys
Source: Postharvest Biology and Technology (article, 2023-10-19)
Field: Chemistry — Analytical Chemistry

Detection of subsurface bruises in plums using spectral imaging and deep learning with wavelength selection Bruise ; Hyperspectral imaging ; Artificial intelligence ; Computer science ; Environmental science ; Horticulture ; Remote sensing ; Biology ; Geology ; Medicine Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Listeria monocytogenes in Food Safety
Authors: Nicole Balasco | Carlo Diaferia | Elisabetta Rosa | Alessandra Monti | Menotti Ruvo | Nunzianna Doti | Luigi Vitagliano
Source: International Journal of Molecular Sciences (review, 2023-05-06)
Field: Materials Science — Biomaterials

A Comprehensive Analysis of the Intrinsic Visible Fluorescence Emitted by Peptide/Protein Amyloid-like Assemblies Amyloid aggregation is a widespread process that involves proteins and peptides with different molecular complexity and amino acid composition. The structural motif (cross-β) underlying this supramolecular organization generates aggregates endowed with special mechanical and spectroscopic properties with huge implications in biomedical and technological fields, including emerging precision medicine. The puzzling ability of these assemblies to emit intrinsic and label-free fluorescence in regions of the electromagnetic spectrum, such as visible and even infrared, usually considered to be forbidden in the polypeptide chain, has attracted interest for its many implications in both basic and applied science. Despite the interest in this phenomenon, the physical basis of its origin is still poorly understood. To gain a global view of the available information on this phenomenon, we here provide an exhaustive survey of the current literature in which original data on this fluorescence have been reported. The emitting systems have been classified in terms of their molecular complexity, amino acid composition, and physical state. Information about the wavelength of the radiation used for the excitation as well as the emission range/peak has also been retrieved. The data collected here provide a picture of the complexity of this multifaceted phenomenon that could be helpful for future studies aimed at defining its structural and electronic basis and/or stimulating new applications. Fluorescence ; Phenomenon ; Peptide ; Chemistry ; Infrared ; Protein aggregation ; Nanotechnology ; Computer science ; Biophysics ; Biological system ; Materials science ; Physics ; Biology ; Biochemistry Supramolecular Self-Assembly in Materials ; Nanocluster Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: Md Nurul Raihen | Sultana Akter
Source: International Journal of Mathematics and Computer in Engineering (article, 2023-10-31)
Field: Chemistry — Analytical Chemistry

Prediction modeling using deep learning for the classification of grape-type dried fruits Abstract Dried grapes (or Raisins) are among the most frequently grown and consumed cereal crops worldwide. They are also an important source of nutrition and nourishment in a variety of countries including Türkiye, the United States, Greece, etc. In addition to that, raisins consist of 15% water, 79% carbs (including 4% fiber), 3% protein, and very little fat. In our study, there were a total of 900 raisin grains used, with 450 pieces from each type: Kecimen and Besni raisin. Seven morphological features were taken from these images after going through several steps of pre-processing. Since machine learning algorithms can analyze large datasets quickly, automatic classification is made possible. With enough training and testing, machine learning models can attain a high degree of precision in classifying raisin grains. They are able to detect variations in size, shape, color, and texture that would be difficult for humans to detect consistently. Eleven machine learning and five different types of artificial intelligence have been used to classify these features. As part of this study, we look into different machine learning and deep learning methods: GaussianNB, Decision Tree, K-Nearest Neighbor, Random Forest, Support vector machine (SVM), XGBoost, LightGBM, and AdaBoost, Logistic Regression, Artificial Neural Network and Deep Learning Network. Study efficacy is evaluated using standard metrics as F1 score and ROC area under the curve (AUC). Using the caret, H 2 O, neuralnet, and keras packages, AdaBoost and LightGBM, two of the fourteen models, achieve an accuracy of 90.30% and 98.40%, respectively, and a ROC curve score of around 90%. Artificial intelligence ; Type (biology) ; Deep learning ; Computer science ; Biology ; Ecology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Tanja Lužaić | Snežana Kravić | Zorica Stojanović | Nada Grahovac | Siniša Jocić | Sandra Cvejić | Lato Pezo | Ranko Romanić
Source: Heliyon (article, 2023-07-01)
Field: Chemistry — Organic Chemistry

Investigation of oxidative characteristics, fatty acid composition and bioactive compounds content in cold pressed oils of sunflower grown in Serbia and Argentina PCA indicated that phenols, chlorophyll, linoleic and oleic acid were the most effective variables for the differentiation of sunflower hybrids grown in Serbia and Argentina. Furthermore, based on the fatty acid composition and bioactive compounds content in the oils, a new Artificial Neural Network (ANN) model was developed to predict the oxidative stability parameters of cold pressed sunflower oil. Food science ; Composition (language) ; Chemical composition ; Oxidative phosphorylation ; Sunflower ; Chemistry ; Sunflower oil ; Biology ; Biochemistry ; Horticulture ; Organic chemistry Edible Oils Quality and Analysis ; Sunflower and Safflower Cultivation ; Free Radicals and Antioxidants
Authors: Edward Linscott | Nicola Colonna | Riccardo De Gennaro | Ngoc Linh Nguyen | Giovanni Borghi | Andrea Ferretti | Ismaïla Dabo | Nicola Marzari
Source: Journal of Chemical Theory and Computation (article, 2023-08-23)
Field: Materials Science — Materials Chemistry

<tt>koopmans</tt>: An Open-Source Package for Accurately and Efficiently Predicting Spectral Properties with Koopmans Functionals Over the past decade we have developed Koopmans functionals, a computationally efficient approach for predicting spectral properties with an orbital-density-dependent functional framework. These functionals impose a generalized piecewise linearity condition to the entire electronic manifold, ensuring that orbital energies match the corresponding electron removal/addition energy differences (in contrast to semilocal DFT, where a mismatch between the two lies at the heart of the band gap problem and, more generally, the unreliability of Kohn-Sham orbital energies). This strategy has proven to be very powerful, yielding molecular orbital energies and solid-state band structures with comparable accuracy to many-body perturbation theory but at greatly reduced computational cost while preserving a functional formulation. This paper reviews the theory of Koopmans functionals, discusses the algorithms necessary for their implementation, and introduces koopmans, an open-source package that contains all of the code and workflows needed to perform Koopmans functional calculations and obtain reliable spectral properties of molecules and materials. Density functional theory ; Workflow ; Hybrid functional ; Computer science ; Perturbation theory (quantum mechanics) ; Band gap ; Statistical physics ; Physics ; Algorithm ; Computational physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; CO2 Reduction Techniques and Catalysts
Authors: Penghui Sun | Jiajia Wang | Zhilin Dong
Source: Sensors (article, 2023-05-17)
Field: Chemistry — Analytical Chemistry

CNN–LSTM Neural Network for Identification of Pre-Cooked Pasta Products in Different Physical States Using Infrared Spectroscopy Infrared (IR) spectroscopy is nondestructive, fast, and straightforward. Recently, a growing number of pasta companies have been using IR spectroscopy combined with chemometrics to quickly determine sample parameters. However, fewer models have used deep learning models to classify cooked wheat food products and even fewer have used deep learning models to classify Italian pasta. To solve these problems, an improved CNN-LSTM neural network is proposed to identify pasta in different physical states (frozen vs. thawed) using IR spectroscopy. A one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) were constructed to extract the local abstraction and sequence position information from the spectra, respectively. The results showed that the accuracy of the CNN-LSTM model reached 100% after using principal component analysis (PCA) on the Italian pasta spectral data in the thawed state and 99.44% after using PCA on the Italian pasta spectral data in the frozen form, verifying that the method has high analytical accuracy and generalization. Therefore, the CNN-LSTM neural network combined with IR spectroscopy helps to identify different pasta products. Principal component analysis ; Chemometrics ; Artificial intelligence ; Convolutional neural network ; Spectroscopy ; Artificial neural network ; Pattern recognition (psychology) ; Infrared spectroscopy ; Computer science ; Deep learning ; Abstraction ; Machine learning ; Chemistry ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Cailing Zhou | Yu Zhou | Yaqian Zheng | Yue Yu | Kailing Yang | Zhiyong Chen | Xianhui Chen | Kang Wen | Yajie Chen | Silei Bai | Junfeng Song | Tong Wu | E Lei | Muyang Wan | Qingyun Cai | Z. Luyan | Wing‐Leung Wong | Yugang Bai | Chunhui Zhang | Xinxin Feng
Source: ACS Applied Materials & Interfaces (article, 2023-04-11)
Field: Chemistry — Organic Chemistry

Amphiphilic Nano-Swords for Direct Penetration and Eradication of Pathogenic Bacterial Biofilms Bacterial biofilms are major causes of persistent and recurrent infections and implant failures. Biofilms are formable by most clinically important pathogens worldwide, such as <i>Staphylococcus aureus</i>, <i>Pseudomonas aeruginosa</i>, and <i>Escherichia coli</i>, causing recalcitrance to standard antibiotic therapy or anti-biofilm strategies due to amphiphilic impermeable extracellular polymeric substances (EPS) and the presence of resistant and persistent bacteria within the biofilm matrix. Herein, we report our design of an oligoamidine-based amphiphilic "nano-sword" with high structural compacity and rigidity. Its rigid, amphiphilic structure ensures effective penetration into EPS, and the membrane-DNA dual-targeting mechanism exerts strong bactericidal effect on the dormant bacterial persisters within biofilms. The potency of this oligoamidine is shown in two distinct modes of application: it may be used as a coating agent for polycaprolactone to fully inhibit surface biofilm growth in an implant-site mimicking micro-environment; meanwhile, it cures model mice of biofilm infections in various ex vivo and in vivo studies. Biofilm ; Microbiology ; Pseudomonas aeruginosa ; Amphiphile ; Staphylococcus aureus ; In vivo ; Bacteria ; Extracellular polymeric substance ; Escherichia coli ; Materials science ; Antibiotics ; Nanotechnology ; Chemistry ; Biology ; Biochemistry Antimicrobial agents and applications ; Bacterial biofilms and quorum sensing ; Antimicrobial Peptides and Activities