jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Reshmi Thekke Parayil | Santosh K. Gupta | Malini Abraham | Subrata Das | Shreyas S. Pitale | K. Sudarshan | M. Mohapatra
Source: Materials Advances (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Ultra-bright and thermally stable deep red emitting doped yttrium zirconate nanoparticles for tunable white LEDs and indoor plant growth Deep red emitting Y 2 Zr 2 O 7 :Eu 3+ (YZOE) pyrochlore nanocrystals with very high color purity, excellent thermal stability and a high photoluminescence quantum yield (∼88%) is synthesized for color tunable white LEDs and indoor plant growth. Materials science ; Photoluminescence ; Light-emitting diode ; Pyrochlore ; Yttrium ; Optoelectronics ; Quantum yield ; Thermal stability ; Nanocrystal ; Doping ; White light ; Nanoparticle ; Chemical engineering ; Nanotechnology ; Optics ; Metallurgy ; Chemistry ; Fluorescence ; Oxide Luminescence Properties of Advanced Materials ; Nuclear materials and radiation effects ; Glass properties and applications
Authors: Xinlei Gu | Chang Liu | Xinxin Gao | Kan Zhang | Weitao Zheng | Changfeng Chen
Source: Research (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Solving Strength–Toughness Dilemma in Superhard Transition-Metal Diborides via a Distinct Chemically Tuned Solid Solution Approach Solid solution strengthening enhances hardness of metals by introducing solute atoms to create local distortions in base crystal lattice, which impedes dislocation motion and plastic deformation, leading to increased strength but reduced ductility and toughness. In sharp contrast, superhard materials comprising covalent bonds exhibit high strength but low toughness via a distinct mechanism dictated by brittle bond deformation, showcasing another prominent scenario of classic strength-toughness tradeoff dilemma. Solving this less explored and understood problem presents a formidable challenge that requires a viable strategy of tuning main load-bearing bonds in these strong but brittle materials to achieve concurrent enhancement of the peak stress and related strain range. Here, we demonstrate a chemically tuned solid solution approach that simultaneously enhances hardness and toughness of superhard transition-metal diboride Ta<sub>1-<i>x</i></sub> Zr <i><sub>x</sub></i> B<sub>2</sub>. This striking phenomenon is achieved by introducing solute atom Zr that has lower electronegativity than solvent atom Ta to reduce the charge depletion on the main load-bearing B-B bonds during indentation, leading to prolonged deformation that gives rise to notably higher strain range and the corresponding peak stress. This finding highlights the crucial role of properly matched contrasting relative electronegativity of solute and solvent atoms in creating concurrent strengthening and toughening and opens a promising avenue for rational design of enhanced mechanical properties in a large class of transition-metal borides. This strategy of concurrent strength-toughness optimization via solute-atom-induced chemical tuning of the main load-bearing bonding charge is expected to work in broader classes of materials, such as nitrides and carbides. Materials science ; Toughness ; Brittleness ; Composite material ; Solid solution strengthening ; Solid solution ; Metallurgy MXene and MAX Phase Materials ; Boron and Carbon Nanomaterials Research ; Metal and Thin Film Mechanics
Authors: Farid Hajareh Haghighi | Roya Binaymotlagh | Ilaria Fratoddi | Laura Chronopoulou | Cleofe Palocci
Source: Gels (article, 2023-12-04)
Field: Materials Science — Biomaterials

Peptide-Hydrogel Nanocomposites for Anti-Cancer Drug Delivery Cancer is the second leading cause of death globally, but conventional anticancer drugs have side effects, mainly due to their non-specific distribution in the body in both cancerous and healthy cells. To address this relevant issue and improve the efficiency of anticancer drugs, increasing attention is being devoted to hydrogel drug-delivery systems for different kinds of cancer treatment due to their high biocompatibility and stability, low side effects, and ease of modifications. To improve the therapeutic efficiency and provide multi-functionality, different types of nanoparticles (NPs) can be incorporated within the hydrogels to form smart hydrogel nanocomposites, benefiting the advantages of both counterparts and suitable for advanced anticancer applications. Despite many papers on non-peptide hydrogel nanocomposites, there is limited knowledge about peptide-based nanocomposites, specifically in anti-cancer drug delivery. The aim of this short but comprehensive review is, therefore, to focus attention on the synergies resulting from the combination of NPs with peptide-based hydrogels. This review, which includes a survey of recent advances in this kind of material, does not aim to be an exhaustive review of hydrogel technology, but it instead highlights recent noteworthy publications and discusses novel perspectives to provide valuable insights into the promising synergic combination of peptide hydrogels and NPs for the design of novel anticancer drug delivery systems. Self-healing hydrogels ; Drug delivery ; Biocompatibility ; Nanotechnology ; Cancer therapy ; Drug ; Cancer treatment ; Targeted drug delivery ; Materials science ; Cancer ; Medicine ; Pharmacology Supramolecular Self-Assembly in Materials ; Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery
Authors: Baohua Zhang | Yunmeng Ou | Shuwan Yu | Yuchen Liu | Ying Liu | Wei Qiu
Source: Plant Methods (article, 2023-12-19)
Field: Chemistry — Analytical Chemistry

Gray mold and anthracnose disease detection on strawberry leaves using hyperspectral imaging This study provided an accurate, rapid, and nondestructive recognition of strawberry gray mold and anthracnose disease in early stage. Hyperspectral imaging ; Mold ; Horticulture ; Biology ; Botany ; Remote sensing ; Geography Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement
Authors: Rui Li | Cheng Chen | Ming Chen | Ruohan Wu | Yun Sun | Benwei Zhu | Zhong Yao
Source: Journal of Food Science (article, 2023-04-02)
Field: Materials Science — Biomaterials

Fabrication of carboxymethyl chitosan/oxidized carboxymethyl cellulose composite film and its assessment for coating preservation of strawberry In the current study, a novel chitosan-based composite, carboxymethyl chitosan (CMCHS)/oxidized carboxymethyl cellulose (OCMC) was fabricated and characterized. The composite film (CMCHS 1.5%w/v + OCMC 0.8%w/v) was more uniform and had better tensile properties, UV blocking, water vapor permeability, and antifungal properties than pure CMCHS film. Preservation experiments showed that the CMCHS/OCMC film was more efficient for retaining the quality decrease of strawberry during storage. By the end of 7 days' storage, the hardness, contents of organic acid, soluble solids, and reducing sugar of coated strawberries were increased by 35.1%, 38.5%, 14.1%, and 3.5%, respectively, compared to the control group; and the decay rate of strawberries in CMCHS/OCMC group also dropped to 36%, about 42% decrease than that in control, suggesting the promising application of CMCHS/OCMC composite in coating preservation. Carboxymethyl cellulose ; Composite number ; Chitosan ; Coating ; Fabrication ; Ultimate tensile strength ; Materials science ; Cellulose ; Chemical engineering ; Chemistry ; Composite material ; Nuclear chemistry ; Organic chemistry ; Sodium ; Metallurgy Nanocomposite Films for Food Packaging ; Postharvest Quality and Shelf Life Management ; Lignin and Wood Chemistry
Authors: Eunsoo Park | Yun‐Soo Kim | Mohammad Akbar Faqeerzada | Moon S. Kim | Insuck Baek | Byoung–Kwan Cho
Source: Frontiers in Plant Science (article, 2023-02-01)
Field: Chemistry — Analytical Chemistry

Hyperspectral reflectance imaging for nondestructive evaluation of root rot in Korean ginseng (Panax ginseng Meyer) Root rot of <i>Panax ginseng</i> caused by <i>Cylindrocarpon destructans</i>, a soil-borne fungus is typically diagnosed by frequently checking the ginseng plants or by evaluating soil pathogens in a farm, which is a time- and cost-intensive process. Because this disease causes huge economic losses to ginseng farmers, it is important to develop reliable and non-destructive techniques for early disease detection. In this study, we developed a non-destructive method for the early detection of root rot. For this, we used crop phenotyping and analyzed biochemical information collected using the HSI technique. Soil infected with root rot was divided into sterilized and infected groups and seeded with 1-year-old ginseng plants. HSI data were collected four times during weeks 7-10 after sowing. The spectral data were analyzed and the main wavelengths were extracted using partial least squares discriminant analysis. The average model accuracy was 84% in the visible/near-infrared region (29 main wavelengths) and 95% in the short-wave infrared (19 main wavelengths). These results indicated that root rot caused a decrease in nutrient absorption, leading to a decline in photosynthetic activity and the levels of carotenoids, starch, and sucrose. Wavelengths related to phenolic compounds can also be utilized for the early prediction of root rot. The technique presented in this study can be used for the early and timely detection of root rot in ginseng in a non-destructive manner. Ginseng ; Hyperspectral imaging ; Traditional medicine ; Botany ; Plant roots ; Biology ; Remote sensing ; Medicine ; Geography ; Pathology Spectroscopy and Chemometric Analyses ; Agriculture, Soil, Plant Science ; Ginseng Biological Effects and Applications
Authors: Sarah N. Elliott | Kevin B. Moore | Andreas V. Copan | Yuri Georgievskii | Murat Keçeli | Kieran P. Somers | Manik Kumer Ghosh | Henry J. Curran | Stephen J. Klippenstein
Source: Combustion and Flame (article, 2023-01-11)
Field: Chemistry — Organic Chemistry

Systematically derived thermodynamic properties for alkane oxidation Key combustion properties , such as ignition delay time, show strong sensitivity to the thermochemistry of the main species in the standard radical oxidation pathway at low temperatures (600–1000 K). Significant uncertainties persist in current estimates of thermodynamic properties, particularly for the larger species of relevance to common practical fuels. In this article, we use advanced computational schemes to evaluate thermodynamic properties for the fuel, fuel radical, peroxy, hydroperoxy-alkyl radical, and hydroperoxide species for a set of 17 fuels containing up to 9 carbon atoms, and with various degrees of branching in the alkane backbone. The procedure, termed STAR-1D , combines conformer sampling to find the minimum geometry, B2PLYP-D3/cc-pVTZ harmonic frequency evaluations, and ω B97X-D/cc-pVTZ one-dimensional torsional mapping. It includes two physically-based scaling routines: a frequency-dependent scaling to B2PLYP-D3/cc-pVTZ anharmonic frequencies and a scaling of one-dimensional ω B97X-D/cc-pVTZ torsional profiles to reproduce the product of the B2PLYP-D3/cc-pVTZ frequencies in the harmonic limit. Substantive comparisons with existing experimental databases, together with careful examinations of key theoretical assumptions, are used to explore the accuracy of the predictions. These computationally intensive explorations of 195 species were facilitated by automated thermochemistry software. High accuracy 0 K heats of formation from a separate study are used with the STAR-1D computations to generate NASA polynomial representations. In addition to their intrinsic value, the present results also provide a reliable database for the optimization of group additivity or machine learning schemes for scaling to larger combustion systems . Towards this end a complementary extensive conformational analysis is carried out for the medium sized species and the thermodynamic properties of the lowest energy hydrogen-bonded and non-hydrogen bonded conformers are contrasted for larger hydroperoxy-alkyl radical species. Thermochemistry ; Chemistry ; Scaling ; Thermodynamics ; Anharmonicity ; Alkane ; Combustion ; Statistical physics ; Computational chemistry ; Physical chemistry ; Organic chemistry ; Physics ; Quantum mechanics ; Mathematics ; Catalysis Chemical Thermodynamics and Molecular Structure ; Advanced Combustion Engine Technologies ; Advanced Chemical Physics Studies
Authors: Jixiang Yang | LI Xin-guo | Xiaofei Ma
Source: Remote Sensing (article, 2023-11-09)
Field: Chemistry — Analytical Chemistry

Improving the Accuracy of Soil Organic Carbon Estimation: CWT-Random Frog-XGBoost as a Prerequisite Technique for In Situ Hyperspectral Analysis Rapid and accurate measurement of the soil organic carbon (SOC) content is a pre-condition for sustainable grain production and land development, and contributes to carbon neutrality in the agricultural industry. To provide technical support for the development and utilization of land resources, the SOC content can be estimated using Vis-NIR diffuse reflectance spectroscopy. However, the spectral redundancy and co-linearity issues of Vis-NIR spectra pose extreme challenges for spectral analysis and model construction. This study compared the effects of different pre-processing methods and feature variable algorithms on the estimation of the SOC content. To this end, in situ hyperspectral data and soil samples were collected from the lakeside oasis of Bosten Lake in Xinjiang, China. The results showed that the combination of continuous wavelet transform (CWT)-random frog could rapidly estimate the SOC content with excellent estimation accuracy (R2 of 0.65–0.86). The feature variable selection algorithm effectively improved the estimation accuracy (average improvement of (0.30–0.48); based on their ability to improve model estimation on average, the algorithms can be ranked as follows: particle swarm optimization (PSO) &gt; ant colony optimization (ACO) &gt; random frog &gt; Boruta &gt; simulated annealing (SA) &gt; successive projections algorithm (SPA). The CWT-XGBoost model based on random frog showed the best results, with R2 = 0.86, RMSE = 2.44, and RPD = 2.78. The feature bands accounted for only 0.57% of the Vis-NIR bands, and the most important sensitive bands were distributed at 755–1195 nm, 1602 nm, 1673 nm, and 2213 nm. These findings are of significance for the extraction of precise information on lakeside oases in arid areas, which would aid in achieving human–land sustainability. Hyperspectral imaging ; Ant colony optimization algorithms ; Feature selection ; Simulated annealing ; Particle swarm optimization ; Random forest ; Environmental science ; Soil carbon ; Partial least squares regression ; Mean squared error ; Pattern recognition (psychology) ; Remote sensing ; Computer science ; Biological system ; Soil science ; Algorithm ; Mathematics ; Artificial intelligence ; Statistics ; Geology ; Soil water ; Biology Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Remote Sensing in Agriculture
Authors: Deseree Reid | Stephanie Thibert | Mowei Zhou
Source: Protein Science (review, 2023-03-01)
Field: Chemistry — Spectroscopy

Dissecting the structural heterogeneity of proteins by native mass spectrometry A single gene yields many forms of proteins via combinations of posttranscriptional/posttranslational modifications. Proteins also fold into higher-order structures and interact with other molecules. The combined molecular diversity leads to the heterogeneity of proteins that manifests as distinct phenotypes. Structural biology has generated vast amounts of data, effectively enabling accurate structural prediction by computational methods. However, structures are often obtained heterologously under homogeneous states in vitro. The lack of native heterogeneity under cellular context creates challenges in precisely connecting the structural data to phenotypes. Mass spectrometry (MS) based proteomics methods can profile proteome composition of complex biological samples. Most MS methods follow the "bottom-up" approach, which denatures and digests proteins into short peptide fragments for ease of detection. Coupled with chemical biology approaches, higher-order structures can be probed via incorporation of covalent labels on native proteins that are maintained at the peptide level. Alternatively, native MS follows the "top-down" approach and directly analyzes intact proteins under nondenaturing conditions. Various tandem MS activation methods can dissect the intact proteins for in-depth structural elucidation. Herein, we review recent native MS applications for characterizing heterogeneous samples, including proteins binding to mixtures of ligands, homo/hetero-complexes with varying stoichiometry, intrinsically disordered proteins with dynamic conformations, glycoprotein complexes with mixed modification states, and active membrane protein complexes in near-native membrane environments. We summarize the benefits, challenges, and ongoing developments in native MS, with the hope to demonstrate an emerging technology that complements other tools by filling the knowledge gaps in understanding the molecular heterogeneity of proteins. Proteome ; Computational biology ; Context (archaeology) ; Structural biology ; Chemistry ; Human proteome project ; Proteomics ; Peptide ; Biophysics ; Membrane protein ; Intrinsically disordered proteins ; Mass spectrometry ; Protein–protein interaction ; Tandem mass spectrometry ; Biology ; Small molecule ; Biochemistry ; Native state ; Membrane ; Gene ; Chromatography Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Enzyme Structure and Function
Authors: Menghong Wu | Sen Dou | Nan Lin | Ranzhe Jiang | Bingxue Zhu
Source: Remote Sensing (article, 2023-09-26)
Field: Chemistry — Analytical Chemistry

Estimation and Mapping of Soil Organic Matter Content Using a Stacking Ensemble Learning Model Based on Hyperspectral Images Fast and accurate SOM estimation and spatial mapping are significant for cultivated land planning and management, crop growth monitoring, and soil carbon pool estimation. It is a key problem to construct a fast and efficient estimation model based on hyperspectral remote sensing image data to realize the inversion mapping of SOM in large areas. In order to solve the problem that the estimation accuracy is not high due to the influence of hyperspectral image quality and soil sample quantity during the estimation model construction, this study explored a method for constructing an estimation model of SOM contents based on a new stacking ensemble learning algorithm and hyperspectral images. Surface soil samples in Huangzhong County of Qinghai Province were collected, and their ZY1-02D hyperspectral remote sensing images were investigated. As input data, a feature band dataset was constructed using the Pearson correlation coefficient and successive projections algorithm. Based on the dataset, a new SOM estimation model under the stacking ensemble learning framework combined with heterogeneous models was developed by optimizing the combination of base and meta-learners. Finally, the spatial distribution map of SOM was plotted based on the result of the model over the study area. The result suggested that the input data quality of the estimation model is improved by constructing a feature band dataset. The multi-class ensemble learning estimation model with the combination strategy of the base and meta-learners has better predictive effects and stability than the single-algorithm and single-level ensemble models with homogeneous learners. The coefficient of determination is 0.829, the residual prediction deviation is 2.85, and the predictive set root mean square error is 1.953. The results can provide new ideas for estimating SOM content using hyperspectral images and ensemble learning algorithms, and serve as a reference for mapping large-scale SOM spatial distribution using space-borne hyperspectral images. Hyperspectral imaging ; Computer science ; Remote sensing ; Pattern recognition (psychology) ; Artificial intelligence ; Ensemble learning ; Data mining ; Geography Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Remote-Sensing Image Classification
Authors: Yubiao Huang | Jiaqing Zhang | Rui Liu | Shuangyao Zhao
Source: Processes (article, 2023-11-16)
Field: Chemistry — Analytical Chemistry

Improving Accuracy and Interpretability of CNN-Based Fault Diagnosis through an Attention Mechanism This study aims to enhance the accuracy and interpretability of fault diagnosis. To address this objective, we present a novel attention-based CNN method that leverages image-like data generated from multivariate time series using a sliding window processing technique. By representing time series data in an image-like format, the spatiotemporal dependencies inherent in the raw data are effectively captured, which allows CNNs to extract more comprehensive fault features, consequently enhancing the accuracy of fault diagnosis. Moreover, the proposed method incorporates a form of prior knowledge concerning category-attribute correlations into CNNs through the utilization of an attention mechanism. Under the guidance of thisprior knowledge, the proposed method enables the extraction of accurate and predictive features. Importantly, these extracted features are anticipated to retain the interpretability of the prior knowledge. The effectiveness of the proposed method is verified on the Tennessee Eastman chemical process dataset. The results show that proposed method achieved a fault diagnosis accuracy of 98.46%, which is significantly higher than similar existing methods. Furthermore, the robustness of the proposed method is analyzed by sensitivity analysis on hyperparameters, and the interpretability is revealed by visually analyzing its feature extraction process. Interpretability ; Computer science ; Robustness (evolution) ; Artificial intelligence ; Feature extraction ; Data mining ; Pattern recognition (psychology) ; Machine learning ; Hyperparameter Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Anomaly Detection Techniques and Applications