jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shazia Dilbar | Hassan Sher | Hina Ali | Riaz Ullah | Ahmad Ali | Zahid Ullah
Source: ACS Omega (article, 2023-08-17)
Field: Materials Science — Materials Chemistry

Antibacterial Efficacy of Green Synthesized Silver Nanoparticles Using <i>Salvia nubicola</i> Extract against <i>Ralstonia solanacearum,</i> the Causal Agent of Vascular Wilt of Tomato Ralstonia solanacearum is a phytopathogen causing bacterial wilt diseases of tomato and affecting its productivity, which leads to prominent economic losses annually. As an alternative to conventional pesticides, green synthesized nanoparticles are believed to possess strong antibacterial activities besides being cheap and ecofriendly. Here, we present the synthesis of silver nanoparticles (Sn-AgNPs) from medicinally important aqueous plant extracts of Salvia nubicola. Characterization of biologically synthesized nanoparticles was performed through UV–vis spectrophotometry, Fourier transform infrared spectroscopy (FTIR), energy-dispersive X-ray spectroscopy, X-ray diffraction, scanning electron microscopy, transmission electron microscopy (TEM), and thermogravimetric analysis. The antibacterial activity of the biosynthesized silver nanoparticles was tested against the phytopathogen R. solanacearum through in vitro experiments. Preliminary phytochemical analysis of the plant extracts revealed the presence of substantial amounts of flavonoids (57.08 mg GAE/g), phenolics (42.30 mg GAE/g), tannins, and terpenoids. The HPLC phenolic profile indicated the presence of 25 possible bioactive compounds. Results regarding green synthesized silver nanoparticles revealed the conformation of different functional groups through FTIR analysis, which could be responsible for the bioreduction and capping of Ag ions into silver NPs. TEM results revealed the spherical, crystalline shape of nanoparticles with the size in the range of 23–63 nm, which validates SEM results. Different concentrations of Sn-AgNPs (T1 (500 μg/mL) to T7 (78.1 μg/mL)) with a combination of plant extracts (PE-Sn-AgNPs) and plant extracts alone exhibited an efficient inhibition of R. solanacearum. These findings could be used as an effective alternative preparation against the bacterial wilt of tomato. Ralstonia solanacearum ; Bacterial wilt ; Silver nanoparticle ; Ralstonia ; Traditional medicine ; Biology ; Microbiology ; Nanoparticle ; Nanotechnology ; Medicine ; Materials science ; Bacteria ; Pathogen Nanoparticles: synthesis and applications ; Essential Oils and Antimicrobial Activity ; Medicinal Plant Research
Authors: Wenqiang Gao | Zhiyun Xiao | Tengfei Bao
Source: Applied Sciences (article, 2023-04-17)
Field: Chemistry — Analytical Chemistry

Detection and Identification of Potato-Typical Diseases Based on Multidimensional Fusion Atrous-CNN and Hyperspectral Data As one of the world’s most crucial crops, the potato is an essential source of nutrition for human activities. However, several diseases pose a severe threat to the yield and quality of potatoes. Timely and accurate detection and identification of potato diseases are of great importance. Hyperspectral imaging has emerged as an essential tool that provides rich spectral and spatial distribution information and has been widely used in potato disease detection and identification. Nevertheless, the accuracy of prediction is often low when processing hyperspectral data using a one-dimensional convolutional neural network (1D-CNN). Additionally, conventional three-dimensional convolutional neural networks (3D-CNN) often require high hardware consumption while processing hyperspectral data. In this paper, we propose an Atrous-CNN network structure that fuses multiple dimensions to address these problems. The proposed structure combines the spectral information extracted by 1D-CNN, the spatial information extracted by 2D-CNN, and the spatial spectrum information extracted by 3D-CNN. To enhance the perceptual field of the convolution kernel and reduce the loss of hyperspectral data, null convolution is utilized in 1D-CNN and 2D-CNN to extract data features. We tested the proposed structure on three real-world potato diseases and achieved recognition accuracy of up to 0.9987. The algorithm presented in this paper effectively extracts hyperspectral data feature information using three different dimensional CNNs, leading to higher recognition accuracy and reduced hardware consumption. Therefore, it is feasible to use the 1D-CNN network and hyperspectral image technology for potato plant disease identification. Hyperspectral imaging ; Convolutional neural network ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Identification (biology) ; Kernel (algebra) ; Mathematics ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Bahman Daneshian | Daniel Höche | Ole Øystein Knudsen | Anders W. B. Skilbred
Source: npj Materials Degradation (article, 2023-02-15)
Field: Materials Science — Materials Chemistry

Effect of climatic parameters on marine atmospheric corrosion: correlation analysis of on-site sensors data Abstract A good understanding of influencing parameters is required to predict corrosivity in marine and coastal environments. This study investigated the influences of real-time data of (i) air temperature, (ii) sensor surface temperature, (iii) relative humidity, (iv) precipitation, and (v) wind on steel corrosion via data analysis. The results revealed that the time when the sensor surface temperature is below the dewpoint temperature reveals the best correlation with corrosion. Wind speed above 5 m s −1 also correlated with corrosion. At the test site, most of the corrosion occurred during autumn and winter, due to more water condensation and more wind. During spring and summer, there was little corrosion, due to little condensation and dry surfaces. Corrosion ; Relative humidity ; Wind speed ; Environmental science ; Humidity ; Precipitation ; Condensation ; Dew point ; Wind direction ; Air temperature ; Atmospheric sciences ; Materials science ; Meteorology ; Metallurgy ; Geology ; Geography Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Carlos Hernández | Nuria Aleixos | Juan Gómez‐Sanchís | Sergio Cubero | Flavio Prieto | J. Blasco
Source: Postharvest Biology and Technology (article, 2023-12-21)
Field: Chemistry — Analytical Chemistry

Enhancing anthracnose detection in mango at early stages using hyperspectral imaging and machine learning Hyperspectral imaging ; Colletotrichum ; Horticulture ; Colletotrichum gloeosporioides ; Spectral signature ; Artificial intelligence ; Computer science ; Biology ; Remote sensing ; Geography Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Plant Pathogens and Fungal Diseases
Authors: Sajad Ebrahimi | Razieh Pourdarbani | Sajad Sabzi | Mohammad Hossein Rohban | Juan Ignacio Arribas
Source: Horticulturae (article, 2023-08-17)
Field: Chemistry — Analytical Chemistry

From Harvest to Market: Non-Destructive Bruise Detection in Kiwifruit Using Convolutional Neural Networks and Hyperspectral Imaging Fruit is often bruised during picking, transportation, and packaging, which is an important post-harvest issue especially when dealing with fresh fruit. This paper is aimed at the early, automatic, and non-destructive ternary (three-class) detection and classification of bruises in kiwifruit based on local spatio-spectral near-infrared (NIR) hyperspectral (HSI) imaging. For this purpose, kiwifruit samples were hand-picked under two ripening stages, either one week (7 days) before optimal ripening (unripe) or at the optimal ripening time instant (ripe). A total of 408 kiwi fruit, i.e., 204 kiwifruits for the ripe stage and 204 kiwifruit for the unripe stage, were harvested. For each stage, three classes were considered (68 samples per class). First, 136 HSI images of all undamaged (healthy) fruit samples, under the two different ripening categories (either unripe or ripe) were acquired. Next, bruising was artificially induced on the 272 fruits under the impact of a metal ball to generate the corresponding bruised fruit HSI image samples. Then, the HSI images of all bruised fruit samples were captured either 8 (Bruised-1) or 16 h (Bruised-2) after the damage was produced, generating a grand total of 408 HSI kiwifruit imaging samples. Automatic 3D-convolutional neural network (3D-CNN) and 2D-CNN classifiers based on PreActResNet and GoogLeNet models were used to analyze the HSI input data. The results showed that the detection of bruising conditions in the case of the unripe fruit is a bit easier than that for its ripe counterpart. The correct classification rate (CCR) of 3D-CNN-PreActResNet and 3D-CNN-GoogLeNet for unripe fruit was 98% and 96%, respectively, over the test set. At the same time, the CCRs of 3D-CNN-PreActResNet and 3D-CNN-GoogLeNet for ripe fruit were both 86%, computed over the test set. On the other hand, the CCRs of 2D-CNN-PreActResNet and 2D-CNN-GoogLeNet for unripe fruit were 96 and 95%, while for ripe fruit, the CCRs were 91% and 98%, respectively, computed over the test set, implying that early detection of the bruising area on HSI imaging was consistently more accurate in the unripe fruit case as compared to its ripe counterpart, with an exception made for the 2D-CNN GoogLeNet classifier which showed opposite behavior. Ripening ; Hyperspectral imaging ; Convolutional neural network ; Actinidia chinensis ; Artificial intelligence ; Bruise ; Horticulture ; Pattern recognition (psychology) ; Stage (stratigraphy) ; Computer science ; Mathematics ; Biology Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Essential Oils and Antimicrobial Activity
Authors: Hiu Yung Wong
Source: ECS Journal of Solid State Science and Technology (article, 2023-04-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

TCAD Simulation Models, Parameters, and Methodologies for β-Ga<sub>2</sub>O<sub>3</sub> Power Devices β -Ga 2 O 3 is an emerging material and has the potential to revolutionize power electronics due to its ultra-wide-bandgap (UWBG) and lower native substrate cost compared to Silicon Carbide and Gallium Nitride. Since β -Ga 2 O 3 technology is still not mature, experimental study of β -Ga 2 O 3 is difficult and expensive. Technology-Computer-Aided Design (TCAD) is thus a cost-effective way to study the potentials and limitations of β -Ga 2 O 3 devices. In this paper, TCAD parameters calibrated to experiments are presented. They are used to perform the simulations in heterojunction p-NiO/n-Ga 2 O 3 diode, Schottky diode, and normally-off Ga 2 O 3 vertical FinFET. Besides the current-voltage (I-V) simulations, breakdown, capacitance-voltage (C-V), and short-circuit ruggedness simulations with robust setups are discussed. TCAD Sentaurus is used in the simulations but the methodologies can be applied in other simulators easily. This paves the road to performing a holistic study of β -Ga 2 O 3 devices using TCAD. Materials science ; Schottky diode ; Gallium nitride ; Silicon carbide ; Optoelectronics ; Technology CAD ; Breakdown voltage ; Diode ; Power semiconductor device ; Substrate (aquarium) ; Voltage ; Electronic engineering ; Gallium ; Engineering physics ; Electrical engineering ; Nanotechnology ; Engineering ; CAD ; Layer (electronics) Ga2O3 and related materials ; Advanced Photocatalysis Techniques ; ZnO doping and properties
Authors: Faris M. AL‐Oqla | Hossam Faris | Maria Habib | Pedro Á. Castillo
Source: Emerging Science Journal (article, 2023-12-01)
Field: Materials Science — Polymers and Plastics

Evolving Genetic Programming Tree Models for Predicting the Mechanical Properties of Green Fibers Advanced modern technology and the industrial sustainability theme have contributed to the implementation of composite materials for various industrial applications. Bio-composites are among the desired alternatives for green products. However, to properly control the performance of bio-composites, predicting their constituent properties is of paramount importance. This work introduces an innovative, evolving genetic programming tree model for predicting the mechanical properties of natural fibers for the first time based upon several inherent chemical and physical properties. Cellulose, hemicellulose, lignin, and moisture contents, as well as the Microfibrillar angle of various natural fibers, were considered to establish the prediction models. A one-hold-out methodology was applied for the training/testing phases. Robust models were developed utilizing evolving genetic programming tree models to predict the tensile strength, Young’s modulus, and the elongation at break properties of the natural fibers. It was revealed that the Microfibrillar angle was dominant and capable of determining the ultimate tensile strength of the natural fibers by 44.7%, comparable to other considered properties, while the impact of cellulose content in the model was only 35.6%. This would facilitate utilizing artificial intelligence to predict the overall mechanical properties of natural fibers without exhausting experimental efforts and cost to enhance the development of better green composite materials for various industrial applications. Doi: 10.28991/ESJ-2023-07-06-02 Full Text: PDF Ultimate tensile strength ; Hemicellulose ; Genetic programming ; Natural fiber ; Composite material ; Materials science ; Lignin ; Composite number ; Cellulose ; Fiber ; Computer science ; Modulus ; Biochemical engineering ; Process engineering ; Biological system ; Artificial intelligence ; Engineering ; Chemical engineering Natural Fiber Reinforced Composites ; Agricultural Engineering and Mechanization ; Soil Mechanics and Vehicle Dynamics
Authors: Zilu Ye | Pierre Sabatier | Leander van der Hoeven | Teeradon Phlairaharn | David Hartlmayr | Fabiana Izaguirre | Anjali Seth | Hiren J. Joshi | Dorte B. Bekker‐Jensen | Nicolai Bache | Jesper V. Olsen
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-11-28)
Field: Chemistry — Spectroscopy

High-throughput and scalable single cell proteomics identifies over 5000 proteins per cell Abstract The emergence of mass spectrometry (MS)-based single-cell proteomics (SCP) promise to revolutionize the study of cellular biology and biomedicine by providing an unparalleled view of the proteome in individual cells. Despite its groundbreaking potential, SCP is nascent and faces challenges including limited sequence depth, throughput, and reproducibility, which have constrained its broader utility. This study introduces key methodological advances, which considerably improve the sensitivity, coverage and dependability of protein identification from single cells. We developed an almost lossless SCP workflow encompassing sample preparation to MS analysis, doubling the number of identified proteins from roughly 2000 to over 5000 in individual HeLa cells. A comprehensive evaluation of analytical software tools, alongside strict false discovery rate (FDR) controls solidified the reliability of our results. These enhancements also facilitated the direct detection of post-translational modifications (PTMs) in single cells, negating the need for enrichment and thereby simplifying the analytical process. Although throughput in MS remains a challenge, our study demonstrates the feasibility of processing up to 80 label-free SCP samples per day. Moreover, an optimized tissue dissociation buffer enabled effective single cell disaggregation of drug-treated cancer cell spheroids, refining the overall proteomic analysis. Our workflow sets a new benchmark in SCP for sensitivity and throughput, with broad applications ranging from the study of cellular development to disease progression and the identification of cell type-specific markers and therapeutic targets. Proteomics ; Proteome ; Workflow ; Computer science ; Computational biology ; Throughput ; Biomedicine ; Drug discovery ; Chemistry ; Bioinformatics ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Single-cell and spatial transcriptomics
Authors: Sanyou Chen | Ziting Sun | Wan-He Li | Pei Yu | Qian Shi | Fei Kong | Qi Zhang | Pengfei Wang | Ya Wang | Fazhan Shi | Jiangfeng Du
Source: Nano Letters (article, 2023-03-27)
Field: Materials Science — Materials Chemistry

Digital Magnetic Detection of Biomolecular Interactions with Single Nanoparticles Biomolecular interactions compose a fundamental element of all life forms and are the biological basis of many biomedical assays. However, current methods for detecting biomolecular interactions have limitations in sensitivity and specificity. Here, using nitrogen-vacancy centers in diamond as quantum sensors, we demonstrate digital magnetic detection of biomolecular interactions with single magnetic nanoparticles (MNPs). We first developed a single-particle magnetic imaging (SiPMI) method on 100 nm-sized MNPs with negligible magnetic background, high signal stability, and accurate quantification. The single-particle method was performed on biotin-streptavidin interactions and DNA-DNA interactions in which a single-base mismatch was specifically differentiated. Subsequently, SARS-CoV-2-related antibodies and nucleic acids were examined by a digital immunomagnetic assay derived from SiPMI. In addition, a magnetic separation process improved the detection sensitivity and dynamic range by more than 3 orders of magnitude and also the specificity. This digital magnetic platform is applicable to extensive biomolecular interaction studies and ultrasensitive biomedical assays. Magnetic nanoparticles ; Streptavidin ; Nanotechnology ; Magnetic particle inspection ; Nanoparticle ; Chemistry ; Materials science ; Biotin ; Biochemistry Diamond and Carbon-based Materials Research ; Force Microscopy Techniques and Applications ; Characterization and Applications of Magnetic Nanoparticles
Authors: Tingting Zhang | Lu Long | Ni Yang | Ian D. Fisk | Wensong Wei | Li Wang | Jing Li | Qun Sun | Rensen Zeng
Source: Food Control (article, 2023-06-11)
Field: Chemistry — Analytical Chemistry

Integration of hyperspectral imaging, non-targeted metabolomics and machine learning for vigour prediction of naturally and accelerated aged sweetcorn seeds Hyperspectral imaging ; Metabolomics ; Metabolome ; Chemistry ; Artificial intelligence ; Computer science ; Biological system ; Chromatography ; Biology Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Food composition and properties
Authors: Mingchih Fang | Peng-Shih-Yun Chin | Wen‐Chieh Sung | Tai‐Yuan Chen
Source: Molecules (article, 2023-05-26)
Field: Chemistry — Organic Chemistry

Physicochemical and Volatile Flavor Properties of Fish Skin under Conventional Frying, Air Frying and Vacuum Frying The aim of this study was to investigate the physicochemical characteristics and volatile flavor of fried tilapia skins under three frying methods. Conventional deep-fat frying usually increases the oil content of the fried fish skin and leads to lipid oxidation, which reduces the product quality. Alternative frying methods, such as air frying for 6 and 12 min under 180 °C (AF6, and AF12) and vacuum frying at 0.085 MPa for 8 and 24 min under 120 °C (VF8, and VF24) were compared to conventional frying for 2 and 8 min under 180 °C (CF2, and CF8) for tilapia skin. Physical properties of fried skin, such as the moisture content, water activity, L* values and breaking force decreased under all frying methods, while the lipid oxidation and a*, b* values increased with the increase in frying time. In general, VF offered higher hardness of product compared to AF which had a lower breaking force. Especially AF12 and CF8 had the lowest breaking force, which indicated higher crispness. For the oil quality inside the product, AF and VF reduced conjugated dienes formation and retarded oxidation compared to CF. The results of the flavor compositions of fish skin measured using gas chromatography mass spectrometry (GC/MS) with solid phase microextraction (SPME) showed that CF obtained higher unpleasant oily odor (nonanal, 2,4-decadienal, etc.), while AF presented greater grilling flavor (pyrazine derivatives). Because fish skin fried by AF only relied on hot air, Maillard reaction derived compounds, such as methylpyrazine, 2,5-dimethylpyrazine, and benzaldehyde were the leading flavors. This made the aroma profiles of AF very different from VF and CF. Among all the approaches, AF and VF developed lower oil content, mild fat oxidation and better flavor attributes, which proves their practical applications for frying tilapia fish skin. Flavor ; Nonanal ; Chemistry ; Maillard reaction ; Food science ; Solid-phase microextraction ; Tilapia ; Lipid oxidation ; Fish <Actinopterygii> ; Gas chromatography–mass spectrometry ; Mass spectrometry ; Chromatography ; Organic chemistry ; Antioxidant Edible Oils Quality and Analysis ; Meat and Animal Product Quality ; Phytochemicals and Antioxidant Activities
Authors: Lili Xu | Kenji Kikushima | Shumpei Sato | Ariful Islam | Tomohito Sato | Shuhei Aramaki | Chi Zhang | Takumi Sakamoto | Fumihiro Eto | Yutaka Takahashi | Ikuko Yao | Manabu Machida | Tomoaki Kahyo | Mitsutoshi Setou
Source: PLoS ONE (article, 2023-04-06)
Field: Chemistry — Physical and Theoretical Chemistry

Spatial distribution of the Shannon entropy for mass spectrometry imaging Mass spectrometry imaging (MSI) allows us to visualize the spatial distribution of molecular components in a sample. A large amount of mass spectrometry data comprehensively provides molecular distributions. In this study, we focus on the information in the obtained data and use the Shannon entropy as a quantity to analyze MSI data. By calculating the Shannon entropy at each pixel on a sample, the spatial distribution of the Shannon entropy is obtained from MSI data. We found that low-entropy pixels in entropy heat maps for kidneys of mice had different structures between two ages (3 months and 31 months). Such changes cannot be visualized by conventional imaging techniques. We further propose a method to find informative molecules. As a demonstration of the proposed scheme, we identified two molecules by setting a region of interest which contained low-entropy pixels and by exploring changes of peaks in the region. Pixel ; Entropy (arrow of time) ; Spatial analysis ; Spatial distribution ; Rényi entropy ; Mass spectrometry imaging ; Mass spectrometry ; Statistical physics ; Principle of maximum entropy ; Physics ; Pattern recognition (psychology) ; Computer science ; Biological system ; Mathematics ; Chemistry ; Artificial intelligence ; Statistics ; Biology ; Chromatography ; Thermodynamics thermodynamics and calorimetric analyses ; Evolution and Genetic Dynamics ; Metabolomics and Mass Spectrometry Studies