jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mengting Chen | Jiahui Song | Haiyan He | Yue Yu | Ruoni Wang | Yue Huang | Zhanming Li
Source: Foods (article, 2024-10-11)
Field: Chemistry — Analytical Chemistry

Quantitative Analysis of High-Price Rice Adulteration Based on Near-Infrared Spectroscopy Combined with Chemometrics Near-infrared spectroscopy (NIRS) holds significant promise in detecting food adulteration due to its non-destructive, simple, and user-friendly properties. This study employed NIRS in conjunction with chemometrics to estimate the content of low-price rice flours (Nanjing, Songjing, Jiangxi silk, Yunhui) blended with high-price rice (Wuchang and Thai fragrant). Partial least squares regression (PLSR), support vector regression (SVR), and back-propagation neural network (BPNN) models were deployed to analyze the spectral data of adulterated samples and assess the degree of contamination. Various preprocessing techniques, parameter optimization strategies, and wavelength selection methods were employed to enhance model accuracy. With correlation coefficients exceeding 87%, the BPNN models exhibited high accuracy in estimating adulteration levels in high-price rice. The SPXY-SG-BPNN, SPXY-MMN-BPNN, KS-SNV-BPNN, and SPXY-SG-BPNN models showcased exceptional performance in discerning mixed Wuchang japonica, Thai fragrant indica, and Thai fragrant Yunhui rice. As shown above, NIRS demonstrated its potential as a rapid, non-destructive method for detecting low-price rice in premium rice blends. Future studies should be performed to concentrate on enhancing the models' versatility and practical applicability. Chemometrics ; Spectroscopy ; Infrared spectroscopy ; Near-infrared spectroscopy ; Analytical Chemistry (journal) ; Chemistry ; Environmental chemistry ; Chromatography ; Physics ; Optics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; GABA and Rice Research
Authors: Alessandra Milloch | Michele Fabrizio | Claudio Giannetti
Source: npj Spintronics (article, 2024-10-01)
Field: Materials Science — Polymers and Plastics

Mott materials: unsuccessful metals with a bright future Achieving the full understanding and control of the insulator-to-metal transition in Mott materials is key for the next generation of electronics devices, with applications ranging from ultrafast transistors, volatile and non-volatile memories and artificial neurons for neuromorphic computing. In this work, we will review the state-of-the-art knowledge of the Mott transition, with specific focus on materials of relevance for actual devices, such as vanadium and other transition metal oxides and chalcogenides. We will emphasize the current attempts in controlling the Mott switching dynamics via the application of external voltage and electromagnetic pulses and we will discuss how the recent advances in time- and space-resolved techniques are boosting the comprehension of the firing process. The nature of the voltage/light-induced Mott switching is inherently different from what is attainable by the slower variation of thermodynamic parameters, thus offering promising routes to achieving the reversible and ultrafast control of conductivity and magnetism in Mott nanodevices. Materials science Transition Metal Oxide Nanomaterials ; Advanced Memory and Neural Computing ; Advanced Condensed Matter Physics
Authors: Hongwei Sheng | Ping Wang | Yuan Yang | Chenglong Tang
Source: Materials (article, 2024-05-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Stress and Microstructures Characterization Based on Magnetic Incremental Permeability and Magnetic Barkhausen Noise Techniques Both microstructure and stress affect the structure and kinematic properties of magnetic domains. In fact, microstructural and stress variations often coexist. However, the coupling of microstructure and stress on magnetic domains is seldom considered in the evaluation of microstructural characteristics. In this investigation, Magnetic incremental permeability (MIP) and magnetic Barkhausen noise (MBN) techniques are used to study the coupling effect of characteristic microstructure and stress on the reversible and irreversible motions of magnetic domains, and the quantitative relationship between microstructure and magnetic domain characteristics is established. Considering the coupling effect of microstructure and stress on magnetic domains, a patterned characterization method of microstructure and stress is innovatively proposed. Pattern recognition based on the Multi-layer Perceptron (MLP) model is realized for microstructure and stress with an accuracy rate higher than 97%. The results show that the pattern recognition accuracy of magnetic domain features and micro-magnetic features simultaneously as input parameters is higher than that of micro-magnetic features alone as input parameters. Microstructure ; Materials science ; Stress (linguistics) ; Permeability (electromagnetism) ; Magnetic domain ; Barkhausen effect ; Nuclear magnetic resonance ; Composite material ; Magnetic field ; Magnetization ; Physics ; Chemistry Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Magnetic properties of thin films
Authors: Abdullah Al Siam | Mushfikus Salehin | Md. Shahinur Alam | Sahabuddin Ahamed | Md Hamidul Islam | Anisur Rahman
Source: Heliyon (article, 2024-08-27)
Field: Chemistry — Analytical Chemistry

Paddy seed viability prediction based on feature fusion of color and hyperspectral image with multivariate analysis Seed viability is essential to have a homogeneous plant population. The seed industry cannot adopt traditional procedures for seed viability evaluation since they are destructive, time-consuming, and need chemicals. This study aimed to investigate the potential of combining hyperspectral and color image features to differentiate viable and non-viable paddy seeds. The hyperspectral and color image of the 355 paddy seeds was captured and later used to examine their viability. An image processing algorithm was developed to extract features from color images of paddy seeds and investigated significant differences in the retrieved feature data using variance analysis. The spectra were extracted from the selected region of interest (ROI) of the hyperspectral paddy seed image and averaged. In the next step, the partial least square discrimination analysis (PLS-DA) model was developed to distinguish viable and non-viable paddy seeds. Initially, the PLS-DA model was developed using spectral data with different preprocessing techniques, and the result obtained an accuracy of 88.9 % in the calibration set and 86.1 % in the prediction set using Savitzky-Golay 2<sup>nd</sup> derivative preprocessed spectra. With the fusion of spectral and significant color image features, the model's accuracy improved to 93.3 % and 90.9 % in the calibration and prediction sets, respectively. Results also showed that the fusion of selected color image features with Savitzky-Golay 2<sup>nd</sup> derivative preprocessed spectra could achieve higher F1-score, recall, and precision values. The visualization map for the viable and non-viable paddy seeds was also developed utilizing the most effective predictive model. The results demonstrate the possibility of using the fusion of the hyperspectral and color image features to sort seeds according to viability, which may be applied in developing an online seed sorting method. Hyperspectral imaging ; Artificial intelligence ; Pattern recognition (psychology) ; Mathematics ; Partial least squares regression ; Preprocessor ; Computer science ; Statistics Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Y. Wang | Cristóbal Rodríguez | Sasha E. Alden | Myung-Hoon Choi | Kristen Alanis | Rahul Srinivasan | Lane A. Baker
Source: Science Advances (article, 2024-12-11)
Field: Chemistry — Electrochemistry

Electrochemical imaging of neurotransmitter release with fast-scan voltammetric ion conductance microscopy Understanding the dynamic spatial and temporal release of neurotransmitters can help resolve long-standing questions related to chemical modulation of neurological circuits. Dopamine modulates function in a range of physiological processes and is key to transmission in addiction and neurological disorders. Studies at subcellular scales promise to help develop a broader understanding of dopamine release, diffusion, and receptor activation and how these processes lead to functional outcomes. Electrochemical measurements of dopamine release at individual cells have proven especially informative. We describe incorporation of fast-scan cyclic voltammetry for detection of dopamine release with subcellular spatial resolution and millisecond time resolution. The platform is benchmarked with standard redox probes and then applied to imaging stimulated release from subcellular locations of a coculture of dopaminergic neurons and astrocytes. Voltammetry reveals heterogeneity in release based on time, location, and neuron identity. We believe that this platform ultimately offers a window to understanding neurotransmission in pathophysiological models of disease where cell-cell communication is key. Dopamine ; Neuroscience ; Neurotransmitter ; Dopaminergic ; Neurotransmission ; Biophysics ; Chemical imaging ; Chemistry ; Nanotechnology ; Materials science ; Computer science ; Biology ; Biochemistry ; Receptor ; Central nervous system Electrochemical Analysis and Applications ; Conducting polymers and applications ; Neuroscience and Neural Engineering
Authors: Manar M. Obiedallah | Maxim A. Mironov | Danila V. Belyaev | Antoaneta Ene | Diana Vakhrusheva | Svetlana Yu. Krasnoborova | Sergey Y. Bershitsky | Daniil V. Shchepkin | Артем С. Минин | Р.И. Ишметова | N.K. Ignatenko | S.G. Tolshchina | О. В. Федорова | Gennady L. Rusinov
Source: Scientific Reports (article, 2024-01-04)
Field: Chemistry — Organic Chemistry

Optimization, characterization, and cytotoxicity studies of novel anti-tubercular agent-loaded liposomal vesicles The treatment of tuberculosis is still a challenging process due to the widespread of pathogen strains resistant to antibacterial drugs, as well as the undesirable effects of anti-tuberculosis therapy. Hence, the development of safe and effective new anti-antitubercular agents, in addition to suitable nanocarrier systems, has become of utmost importance and necessity. Our research aims to develop liposomal vesicles that contain newly synthesized compounds with antimycobacterial action. The compound being studied is a derivative of imidazo-tetrazine named 3-(3,5-dimethylpyrazole-1-yl)-6-(isopropylthio) imidazo [1,2-b] [1,2,4,5] tetrazine compound. Several factors that affect liposomal characteristics were studied. The maximum encapsulation efficiency was 53.62 ± 0.09. The selected liposomal formulation T8* possessed a mean particle size of about 205.3 ± 3.94 nm with PDI 0.282, and zeta potential was + 36.37 ± 0.49 mv. The results of the in vitro release study indicated that the solubility of compound I was increased by its incorporation in liposomes. The free compound and liposomal preparation showed antimycobacterial activity against Mycobacterium tuberculosis H<sub>37</sub>R<sub>v</sub> (ATCC 27294) at MIC value 0.94-1.88 μg/ml. We predict that the liposomes may be a good candidate for delivering new antitubercular drugs. Antimycobacterial ; Tetrazine ; Liposome ; Nanocarriers ; Mycobacterium tuberculosis ; Pharmacology ; Cytotoxicity ; Chemistry ; Zeta potential ; Tuberculosis ; Combinatorial chemistry ; In vitro ; Drug ; Medicine ; Nanotechnology ; Materials science ; Biochemistry ; Nanoparticle ; Organic chemistry Synthesis and Characterization of Heterocyclic Compounds ; Bacteriophages and microbial interactions ; Microwave-Assisted Synthesis and Applications
Authors: Lauren Fields | M Ma | Kellen DeLaney | Ashley Phetsanthad | Lingjun Li
Source: PROTEOMICS (article, 2024-01-03)
Field: Chemistry — Spectroscopy

A crustacean neuropeptide spectral library for data‐independent acquisition (DIA) mass spectrometry applications Neuropeptides have tremendous potential for application in modern medicine, including utility as biomarkers and therapeutics. To overcome the inherent challenges associated with neuropeptide identification and characterization, data-independent acquisition (DIA) is a fitting mass spectrometry (MS) method of choice to achieve sensitive and accurate analysis. It is advantageous for preliminary neuropeptidomic studies to occur in less complex organisms, with crustacean models serving as a popular choice due to their relatively simple nervous system. With spectral libraries serving as a means to interpret DIA-MS output spectra, and Cancer borealis as a model of choice for neuropeptide analysis, we performed the first spectral library mapping of crustacean neuropeptides. Leveraging pre-existing data-dependent acquisition (DDA) spectra, a spectral library was built using PEAKS Online. The library is comprised of 333 unique neuropeptides. The identification results obtained through the use of this spectral library were compared with those achieved through library-free analysis of crustacean brain, pericardial organs (PO), and thoracic ganglia (TG) tissues. A statistically significant increase (Student's t-test, P value < 0.05) in the number of identifications achieved from the TG data was observed in the spectral library results. Furthermore, in each of the tissues, a distinctly different set of identifications was found in the library search compared to the library-free search. This work highlights the necessity for the use of spectral libraries in neuropeptide analysis, illustrating the advantage of spectral libraries for interpreting DIA spectra in a reproducible manner with greater neuropeptidomic depth. Neuropeptide ; Computational biology ; Identification (biology) ; Computer science ; Spectral analysis ; Pattern recognition (psychology) ; Biology ; Biological system ; Chemistry ; Artificial intelligence ; Bioinformatics ; Physics ; Spectroscopy ; Biochemistry ; Ecology ; Receptor Mass Spectrometry Techniques and Applications ; Neurobiology and Insect Physiology Research ; Advanced Proteomics Techniques and Applications
Authors: Rudi Setiawan | Hayatou Oumarou
Source: Indonesian Journal of Data and Science (article, 2024-03-31)
Field: Chemistry — Analytical Chemistry

Classification of Rice Grain Varieties Using Ensemble Learning and Image Analysis Techniques This research explored the efficacy of machine learning techniques, specifically the Bagging meta-estimator, in the classification of rice grain images. Utilizing a dataset composed of 45,000 images of Arborio, Basmati, and Jasmine rice varieties, a 5-fold cross-validation was employed to evaluate the model's performance. The results were highly promising, with the model consistently achieving over 96% in accuracy, precision, recall, and F1-score across all folds, indicating its robustness and reliability. The study confirmed that ensemble learning techniques could significantly improve the classification accuracy over single classifier systems in agricultural applications. The findings offer a significant contribution to automated agricultural processes, suggesting that machine learning can greatly enhance the efficiency and precision of rice variety classification. These results pave the way for further research into the integration of such models into agricultural quality control and provide a foundation for the exploration of advanced image processing and deep learning techniques for improved performance. Future research directions include expanding the model to encompass a wider variety of crops and integrating additional data modalities to refine classification accuracy further. Practical applications should explore the incorporation of this technology into existing agricultural systems to maximize the benefits of automation in quality control. Ensemble learning ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Alexander S. Ospennikov | Yu. M. Chesnokov | Andrey V. Shibaev | Б. В. Локшин | Olga E. Philippova
Source: Gels (article, 2024-11-28)
Field: Materials Science — Biomaterials

Nanostructured Hydrogels of Carboxylated Cellulose Nanocrystals Crosslinked by Calcium Ions Bio-based eco-friendly cellulose nanocrystals (CNCs) gain an increasing interest for diverse applications. We report the results of an investigation of hydrogels spontaneously formed by the self-assembly of carboxylated CNCs in the presence of CaCl<sub>2</sub> using several complementary techniques: rheometry, isothermal titration calorimetry, FTIR-spectroscopy, cryo-electron microscopy, cryo-electron tomography, and polarized optical microscopy. Increasing CaCl<sub>2</sub> concentration was shown to induce a strong increase in the storage modulus of CNC hydrogels accompanied by the growth of CNC aggregates included in the network. Comparison of the rheological data at the same ionic strength provided by NaCl and CaCl<sub>2</sub> shows much higher dynamic moduli in the presence of CaCl<sub>2</sub>, which implies that calcium cations not only screen the repulsion between similarly charged nanocrystals favoring their self-assembly, but also crosslink the polyanionic nanocrystals. Crosslinking is endothermic and driven by increasing entropy, which is most likely due to the release of water molecules surrounding the interacting COO<sup>-</sup> and Ca<sup>2+</sup> ions. The hydrogels can be easily destroyed by increasing the shear rate because of the alignment of rodlike nanocrystals along the direction of flow and then quickly recover up to 90% of their viscosity in 15 s, when the shear rate is decreased. Self-healing hydrogels ; Rheometry ; Chemical engineering ; Materials science ; Nanocrystal ; Dynamic mechanical analysis ; Rheology ; Isothermal titration calorimetry ; Ionic strength ; Nanotechnology ; Polymer chemistry ; Chemistry ; Polymer ; Composite material ; Aqueous solution ; Organic chemistry ; Physical chemistry Advanced Cellulose Research Studies ; Polysaccharides and Plant Cell Walls ; Lignin and Wood Chemistry
Authors: Tan Peng | Yong‐Chen Xiong | Chun-Bo Hua | Zheng-Rong Liu | Xiaolu Zhu | Wei Cao | Fang Lv | Yue Hou | Bin Zhou | Ziyu Wang | Rui Xiong
Source: Physical review. B./Physical review. B (preprint, 2024-05-14)
Field: Materials Science — Materials Chemistry

Structural disorder-induced topological phase transitions in quasicrystals Recently, structural disorder-induced topological phase transitions in periodic systems have attracted much attention. However, in aperiodic systems such as quasicrystalline systems, the interplay between structural disorder and band topology is still unclear. In this work, we investigate the effects of structural disorder on a quantum spin Hall insulator phase and a higher-order topological phase in a two-dimensional Amman-Beenker tiling quasicrystalline lattice. We demonstrate that the structural disorder can induce a topological phase transition from a quasicrystalline normal insulator phase to an amorphous quantum spin Hall insulator phase, which is confirmed by bulk gap closing and reopening, robust edge states, a quantized spin Bott index, and conductance. Furthermore, the structural disorder-induced higher-order topological phase transition from a quasicrystalline normal insulator phase to an amorphous higher-order topological phase characterized by a quantized quadrupole moment and topological corner states is also found. More strikingly, the disorder-induced higher-order topological insulator with eight corner states represents a distinctive topological state that eludes realization in conventional crystalline systems. Our work extends the study of the interplay between disorder effects and topologies to quasicrystalline and amorphous systems. Quasicrystal ; Phase (matter) ; Condensed matter physics ; Topology (electrical circuits) ; Phase transition ; Physics ; Materials science ; Theoretical physics ; Mathematics ; Quantum mechanics ; Combinatorics Quasicrystal Structures and Properties
Authors: Sena Dayioglugil | Nuri Solak
Source: Materials (article, 2024-05-04)
Field: Materials Science — Materials Chemistry

Effect of ZnO on Luminescence Performance of Terbium-Activated Zinc Borosilicate Glasses In this study, terbium-doped ZnO-SiO<sub>2</sub>-B<sub>2</sub>O<sub>3</sub>-Na<sub>2</sub>O glasses were fabricated with the conventional melt-quenching method. The effect of altering the concentration of the host matrix on luminescence performance was investigated in terms of different ZnO/B<sub>2</sub>O<sub>3</sub> and ZnO/SiO<sub>2</sub> ratios. FT-IR results indicate that bridging oxygens (Bos) were converted to non-bridging oxygens (NBOs) with increments of ZnO. Furthermore, the emission intensity and luminescence lifetime of samples were influenced by the amount of ZnO; this was proven with photoluminescence spectra results. The maximum emission intensity was observed at a 1.1 ZnO/B<sub>2</sub>O<sub>3</sub> ratio and a 0.8 ZnO/SiO<sub>2</sub> ratio; however, the highest luminescence lifetime was observed at a 1.1 ZnO/SiO<sub>2</sub> ratio. The emission intensity and luminescence lifetime of glass samples were improved by heat treatment as a result of the formation of willemite and zinc oxide phases. An increase in the ZnO/SiO<sub>2</sub> ratio facilitated the formation of willemite and zinc oxide phases; therefore, crystallinity was directly related to the luminescence behavior of glass samples. Terbium ; Luminescence ; Materials science ; Zinc ; Photoluminescence ; Crystallinity ; Quenching (fluorescence) ; Oxide ; Borosilicate glass ; Doping ; Analytical Chemistry (journal) ; Mineralogy ; Inorganic chemistry ; Optoelectronics ; Metallurgy ; Composite material ; Fluorescence ; Optics ; Chemistry Luminescence Properties of Advanced Materials ; Glass properties and applications ; Lanthanide and Transition Metal Complexes