jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daniele Vivona | Kiarash Gordiz | Randall J. Meyer | Sumathy Raman | Yang Shao‐Horn
Source: Journal of Materials Chemistry A (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Mechanistic insights into the origin of the oxygen migration barrier Electronic structure signatures of oxygen ion migration highlight new fundamental descriptors of the oxygen migration barrier based on the electronic structure of the local chemical environment. Oxygen ; Chemical physics ; Electronic structure ; Activation barrier ; Materials science ; Chemistry ; Nanotechnology ; Computational chemistry ; Density functional theory Mass Spectrometry Techniques and Applications ; Electrochemical Analysis and Applications ; Advanced Memory and Neural Computing
Authors: Štěpán Sršeň | O. Anatole von Lilienfeld | Petr Slavı́ček
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Fast and accurate excited states predictions: machine learning and diabatization The efficiency of machine learning algorithms for electronically excited states is far behind ground-state applications. One of the underlying problems is the insufficient smoothness of the fitted potential energy surfaces and other properties in the vicinity of state crossings and conical intersections, which is a prerequisite for an efficient regression. Smooth surfaces can be obtained by switching to the diabatic basis. However, diabatization itself is still an outstanding problem. We overcome these limitations by solving both problems at once. We use a machine learning approach combining clustering and regression techniques to correct for the deficiencies of property-based diabatization which, in return, provides us with smooth surfaces that can be easily fitted. Our approach extends the applicability of property-based diabatization to multidimensional systems. We utilize the proposed diabatization scheme to achieve higher prediction accuracy for adiabatic states and we show its performance by reconstructing global potential energy surfaces of excited states of nitrosyl fluoride and formaldehyde. While the proposed methodology is independent of the specific property-based diabatization and regression algorithm, we show its performance for kernel ridge regression and a very simple diabatization based on transition multipoles. Compared to most other algorithms based on machine learning, our approach needs only a small amount of training data. Computer science ; Diabatic ; Smoothness ; Conical surface ; Algorithm ; Adiabatic process ; Mathematical optimization ; Statistical physics ; Machine learning ; Mathematics ; Physics ; Mathematical analysis ; Geometry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Srestha Basu | Adi Hendler‐Neumark | Gili Bisker
Source: The Journal of Physical Chemistry Letters (article, 2024-10-10)
Field: Materials Science — Materials Chemistry

Ratiometric Normalization of Near-Infrared Fluorescence in Defect-Engineered Single-Walled Carbon Nanotubes for Cholesterol Detection Ratiometric probing of analytes presents a substantial advancement in molecular recognition, offering self-calibrating signals that enhance the measurement accuracy and reliability. We present a dual-emitting probe based on (6,5) chirality-enriched single-walled carbon nanotubes (SWCNTs) with oxygen defects for cholesterol (Chol) detection using ratiometric fluorescence readouts. The interaction with Chol induced significant intensity variations in the <i>E</i><sub>11</sub> and <i>E</i><sub>11</sub><sup>*</sup> emission peaks of oxygen defect-induced SWCNTs, giving rise to ratiometric fluorescence changes. The sensitivity of these probes toward Chol in water and serum was 0.28 ± 0.01 and 0.72 ± 0.05 μM, respectively, which is comparable to that of common gold standards for cholesterol detection used in clinical samples. By utilizing ratiometric readouts, our approach enhanced selectivity over numerous competing analytes, including amino acids, sugars, cations, anions, proteins, steroid hormones, surfactants, and phospholipids. Mechanistic investigations revealed that Chol detection by defect-integrated SWCNTs was facilitated by Chol incorporation within micelles formed by sodium cholate, the surfactant dispersant used for the SWCNT suspension. Oxygen defects played a crucial role by directly interacting with Chol. This strategy employing defect-integrated dual-peak NIR-emitting SWCNTs as sensors for Chol in aqueous and serum environments not only enables background-free detection of biologically relevant analytes but also advances biosensing using SWCNTs through tailored surface functionalization and advanced read-out concepts. Carbon nanotube ; Fluorescence ; Normalization (sociology) ; Analyte ; Materials science ; Nanotechnology ; Infrared ; Optoelectronics ; Chemistry ; Optics ; Chromatography Carbon Nanotubes in Composites ; Electrochemical sensors and biosensors ; Analytical Chemistry and Sensors
Authors: Abdulaziz Alshalawi | Fahad Saleh Al–Ismail
Source: IEEE Access (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Partial Discharge Detection Based on Ultrasound Using Optimized Deep Learning Approach Electrical equipment is prone to different types of Partial Discharge (PD) failures that are varying between minor and severe level. In this paper, Three developed models for Convolution Neural Network (CNN) are proposed to detect and classify four different partial discharge types which are arcing, corona discharge, tracking, looseness as well as healthy equipment situation. Notably, the resulting models exhibited an impressive overall accuracy of more than 94%, which is particularly significant considering the inherent presence of noise in the real-world samples obtained as representative field failures. These findings underscore the robustness and effectiveness of the CNN models in accurately identifying PDs, despite the intricate challenges associated with real-world data. Partial discharge ; Robustness (evolution) ; Computer science ; Convolutional neural network ; Artificial intelligence ; Electric arc ; Convolution (computer science) ; Deep learning ; Corona discharge ; Noise (video) ; Artificial neural network ; Machine learning ; Pattern recognition (psychology) ; Engineering ; Voltage ; Electrical engineering ; Electrode High voltage insulation and dielectric phenomena ; Advanced Fiber Optic Sensors ; Electrical Fault Detection and Protection
Authors: Kishore K. Mahalingan | Danielle A. Grotjahn | Yan Li | Gabriel C. Lander | Elena A. Zehr | Antonina Roll‐Mecak
Source: Nature Chemical Biology (article, 2024-04-24)
Field: Materials Science — Materials Chemistry

Structural basis for α-tubulin-specific and modification state-dependent glutamylation Posttranslational modification ; Tubulin ; Basis (linear algebra) ; State (computer science) ; Computational biology ; Cell biology ; Chemistry ; Biology ; Microtubule ; Computer science ; Programming language ; Biochemistry ; Mathematics Enzyme Structure and Function ; Biochemical and Molecular Research ; Cancer, Hypoxia, and Metabolism
Authors: Chloé Van Leene | Laura Van Moortel | Karolien De Bosscher | Kris Gevaert
Source: Trends in Biochemical Sciences (review, 2024-12-19)
Field: Chemistry — Spectroscopy

Exploring protein conformations with limited proteolysis coupled to mass spectrometry Limited proteolysis coupled to mass spectrometry (LiP-MS) has emerged as a powerful proteomic tool for studying protein conformations. Since its introduction in 2014, LiP-MS has expanded its scope to explore complex biological systems and shed light on disease mechanisms, and has been used for protein drug research. This review discusses the evolution of the technique, recent technical advances, including enhanced protocols and integration of machine learning, and diverse applications across various experimental models. Despite its achievements, challenges in protein extraction and conformotypic peptide identification remain. Ongoing methodological refinements will be crucial to overcome these challenges and enhance the capabilities of the technique. However, LiP-MS offers significant potential for future discoveries in structural proteomics and medical research. Proteolysis ; Mass spectrometry ; Chemistry ; Computational biology ; Biochemistry ; Chromatography ; Biology ; Enzyme Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Zishen Zhang | Hong Cheng | Meiyu Chen | Lixin Zhang | Yudou Cheng | Wenjuan Geng | Junfeng Guan
Source: Foods (article, 2024-12-08)
Field: Chemistry — Analytical Chemistry

Detection of Pear Quality Using Hyperspectral Imaging Technology and Machine Learning Analysis The non-destructive detection of fruit quality is indispensable in the agricultural and food industries. This study aimed to explore the application of hyperspectral imaging (HSI) technology, combined with machine learning, for a quality assessment of pears, so as to provide an efficient technical method. Six varieties of pears were used for inspection, including 'Sucui No.1', 'Zaojinxiang', 'Huangguan', 'Akizuki', 'Yali', and 'Hongli No.1'. Spectral data within the 398~1004 nm wavelength range were analyzed to compare the predictive performance of the Least Squares Support Vector Machine (LS-SVM) models on various quality parameters, using different preprocessing methods and the selected feature wavelengths. The results indicated that the combination of Fast Detrend-Standard Normal Variate (FD-SNV) preprocessing and Competitive Adaptive Reweighted Sampling (CARS)-selected feature wavelengths yielded the best improvement in model predictive ability for forecasting key quality parameters such as firmness, soluble solids content (SSC), pH, color, and maturity degree. They could enhance the predictive capability and reduce computational complexity. Furthermore, in order to construct a quality prediction model, integrating hyperspectral data from six pear varieties resulted in an <i>RPD</i> (Ratio of Performance to Deviation) exceeding 2.0 for all the quality parameters, indicating that increasing the fruit sample size and variety number further strengthened the robustness of the model. The Backpropagation Neural Network (BPNN) model could accurately distinguish six distinct pear varieties, achieving prediction accuracies of above 99% for both the calibration and test sets. In summary, the combination of HSI and machine learning models enabled an efficient, rapid, and non-destructive detection of pear quality and provided a practical value for quality control and the commercial processing of pears. Hyperspectral imaging ; PEAR ; Quality (philosophy) ; Artificial intelligence ; Computer science ; Machine learning ; Pattern recognition (psychology) ; Remote sensing ; Geography ; World Wide Web Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Yu Xia | Wenxi Liu | Jingwu Meng | Jinghao Hu | Wenbo Liu | Jie Kang | Bin Luo | Han Zhang | Wei Tang
Source: Frontiers in Plant Science (review, 2024-01-10)
Field: Chemistry — Analytical Chemistry

Principles, developments, and applications of spatially resolved spectroscopy in agriculture: a review Agriculture is the primary source of human survival, which provides the most basic living and survival conditions for human beings. As living standards continue to improve, people are also paying more attention to the quality and safety of agricultural products. Therefore, the detection of agricultural product quality is very necessary. In the past decades, the spectroscopy technique has been widely used because of its excellent results in agricultural quality detection. However, traditional spectral inspection methods cannot accurately describe the internal information of agricultural products. With the continuous research and development of optical properties, it has been found that the internal quality of an object can be better reflected by separating the properties of light, such as its absorption and scattering properties. In recent years, spatially resolved spectroscopy has been increasingly used in the field of agricultural product inspection due to its simple compositional structure, low-value cost, ease of operation, efficient detection speed, and outstanding ability to obtain information about agricultural products at different depths. It can also separate optical properties based on the transmission equation of optics, which allows for more accurate detection of the internal quality of agricultural products. This review focuses on the principles of spatially resolved spectroscopy, detection equipment, analytical methods, and specific applications in agricultural quality detection. Additionally, the optical properties methods and direct analysis methods of spatially resolved spectroscopy analysis methods are also reported in this paper. Agriculture ; Quality (philosophy) ; Product (mathematics) ; Spectroscopy ; Computer science ; Field (mathematics) ; Biochemical engineering ; Environmental science ; Process engineering ; Agricultural engineering ; Risk analysis (engineering) ; Engineering ; Business ; Physics ; Mathematics ; Geography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Kıyas Kayaalp
Source: Information Technology And Control (article, 2024-03-22)
Field: Chemistry — Analytical Chemistry

Classification of Medicinal Plant Leaves for Types and Diseases with Hybrid Deep Learning Methods Leaf images are often used to detect plant diseases because most disease symptoms appear on the leaves. Analyzes performed by experts in the laboratory environment are expensive and time consuming. Therefore, there is a need for automated plant disease detection systems that are both economical and can help diagnose early symptoms more accurately. In this study, a deep learning-based methodology is presented for the classification of leaf diseases of plants, which are very similar in color, texture, vein and shape and cannot be noticed by non-experts, which are important for traditional medicine and pharmaceutical industry. In the model development process, 7 pre-learning deep learning algorithms and an image data set created from plant leaves in 10 categories were preferred. The proposed model classifies the plant type and diseased condition in the dataset. In the first step of training the model, different learning rates were tested with optimum hyperparameters. In the second part, a test accuracy rate of 98.69% was achieved with the DenseNet121 model, with increased data. At the last stage, after the edge detection processes, the test accuracy value of 67.92% was reached with the DenseNet 121 model. Artificial intelligence ; Computer science ; Traditional medicine ; Machine learning ; Medicine Spectroscopy and Chemometric Analyses
Authors: Jikai Che | Qing Liang | Yifan Xia | Yang Liu | Hongshan Li | Ning Hu | Cheng Wei-bo | Hong Zhang | Haipeng Lan
Source: Foods (article, 2024-11-04)
Field: Chemistry — Analytical Chemistry

The Study on Nondestructive Detection Methods for Internal Quality of Korla Fragrant Pears Based on Near-Infrared Spectroscopy and Machine Learning Quality control and grading of Korla fragrant pears significantly impact their commercial value. Rapid and non-destructive detection of soluble solids content (SSC) and firmness is crucial to improving this. This study proposes a method combining near-infrared spectroscopy (NIRS) with machine learning for the rapid, non-destructive detection of SSC and firmness in Korla pears. By analyzing absorbance in the 900-1800 nm range, six preprocessing methods-Savitzky-Golay derivative (SGD), standard normal variate (SNV), multiplicative scatter correction (MSC), Savitzky-Golay smoothing (SGS), vector normalization (VN), and min-max normalization (MMN)-were applied to the raw spectral data. uninformative variable elimination (UVE) and successive projections algorithm (SPA) were then used to extract effective wavelengths. Partial least squares regression (PLSR) models were developed for SSC and firmness based on the extracted data. The results showed that all preprocessing and wavelength-extraction methods improved model accuracy. The optimal SSC prediction model was MSC-SPA-PLSR (R = 0.93, RMSE = 0.195), and the best hardness prediction model was MSC-UVE-PLSR (R = 0.83, RMSE = 0.249). This research aids in establishing a non-destructive testing system, offering producers a rapid and accurate quality assessment tool, and provides the food industry with better production control measures to enhance standardization and market competitiveness of Korla pears. Partial least squares regression ; Normalization (sociology) ; Mean squared error ; Near-infrared spectroscopy ; Mathematics ; Preprocessor ; Artificial intelligence ; Computer science ; Algorithm ; Pattern recognition (psychology) ; Statistics ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Phytochemicals and Antioxidant Activities
Authors: Meysam Latifi Amoghin | Yousef Abbaspour‐Gilandeh | Mohammad Tahmasebi | Juan Ignacio Arribas
Source: Chemometrics and Intelligent Laboratory Systems (article, 2024-05-09)
Field: Chemistry — Analytical Chemistry

Automatic non-destructive estimation of polyphenol oxidase and peroxidase enzyme activity levels in three bell pepper varieties by Vis/NIR spectroscopy imaging data based on machine learning methods The browning process of food products if often formed upon cutting and damage during their processing, transport, and storage, amongst other potential sources and reasons. Enzymic browning can be mainly due to polyphenol peroxidase (PPO) and peroxidase (POD) enzymes. Visible/near-infrared (Vis/NIR) imaging spectroscopy in the range of 350-1150 nm was used in this study for automatic and non-destructive evaluation of PPO and POD activity levels in three bell pepper varieties (red, yellow, orange; N=30), with a total of 30 inputs samples in each variety. The spectral data were then modeled by the partial least square regression (PLSR) throughout the whole spectral range, without using any subset of the most effective wavelength (EW) values. Regression determination coefficient (R2) values for the estimation (prediction) of POD enzyme activity levels were 0.794, 0.772, and 0.726 for red, yellow, and orange bell peppers, respectively, all over the validation set. At the same time, the activity levels of PPO enzyme over bell peppers showed R2 values of 0.901, 0.810, and 0.859, for red, yellow, and orange bell peppers, respectively, all over the validation set. In addition, a combination of Support vector machine (SVM) with either genetic algorithms (GA), particle swarm optimization (PSO), ant colony optimization (ACO), or imperialistic competitive algorithms (ICA) hybrid machine learning (ML) techniques were used to select the optimal (discriminant) spectral EW wavelength values, and regression performance was consistently improved, to judge from higher regression fit R2 values. Either 14 or 15 EWs were computed and selected in order of their discriminative power using previously mentioned ML techniques. The SVM-PSO method resulted the best one in the process of selecting the most effective wavelength values (nm). On the other hand, three regression methods comprising PLSR, multiple least regression (MLR), and neural network (NN), were employed to model the SVM-PSO selected EWs. The ratio of performance to deviation (RPD), the R2 and the root mean square error (RMSE), over the test set, for the non-linear NN regression method exhibited better results as compared to the other two regression methods, being closely followed by PLSR, and therefore NN regression method was selected as the best approach for modeling the most effective spectral wavelength values in this study. Polyphenol oxidase ; Peroxidase ; Pepper ; Chemistry ; Computer science ; Polyphenol ; Enzyme ; Spectroscopy ; Catechol oxidase ; Artificial intelligence ; Biochemistry ; Food science ; Physics ; Antioxidant Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Sezer Dümen | Esra Kavalcı Yılmaz | Kemal Adem | Erdinç Avaroğlu
Source: European Food Research and Technology (article, 2024-04-15)
Field: Chemistry — Analytical Chemistry

Performance of vision transformer and swin transformer models for lemon quality classification in fruit juice factories Abstract Assessing the quality of agricultural products holds vital significance in enhancing production efficiency and market viability. The adoption of artificial intelligence (AI) has notably surged for this purpose, employing deep learning and machine learning techniques to process and classify agricultural product images, adhering to defined standards. This study focuses on the lemon dataset, encompassing ‘good’ and ‘bad’ quality classes, initiate by augmenting data through rescaling, random zoom, flip, and rotation methods. Subsequently, employing eight diverse deep learning approaches and two transformer methods for classification, the study culminated in the ViT method achieving an unprecedented 99.84% accuracy, 99.95% recall, and 99.66% precision, marking the highest accuracy documented. These findings strongly advocate for the efficacy of the ViT method in successfully classifying lemon quality, spotlighting its potential impact on agricultural quality assessment. Artificial intelligence ; Machine learning ; Transformer ; Computer science ; Random forest ; Quality (philosophy) ; Agricultural engineering ; Mathematics ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Food Supply Chain Traceability
Authors: Rahuljeet Chadha | Jason A. Guerrero | Lu Wei | Laura M. Sanchez
Source: ACS Central Science (review, 2024-03-21)
Field: Chemistry — Spectroscopy

Seeing is Believing: Developing Multimodal Metabolic Insights at the Molecular Level This outlook explores how two different molecular imaging approaches might be combined to gain insight into dynamic, subcellular metabolic processes. Specifically, we discuss how matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and stimulated Raman scattering (SRS) microscopy, which have significantly pushed the boundaries of imaging metabolic and metabolomic analyses in their own right, could be combined to create comprehensive molecular images. We first briefly summarize the recent advances for each technique. We then explore how one might overcome the inherent limitations of each individual method, by envisioning orthogonal and interchangeable workflows. Additionally, we delve into the potential benefits of adopting a complementary approach that combines both MSI and SRS spectro-microscopy for informing on specific chemical structures through functional-group-specific targets. Ultimately, by integrating the strengths of both imaging modalities, researchers can achieve a more comprehensive understanding of biological and chemical systems, enabling precise metabolic investigations. This synergistic approach holds substantial promise to expand our toolkit for studying metabolites in complex environments. Metabolomics ; Computer science ; Workflow ; Mass spectrometry imaging ; Modalities ; Data science ; Computational biology ; Molecular imaging ; Nanotechnology ; Artificial intelligence ; Chemistry ; Mass spectrometry ; Bioinformatics ; Materials science ; Biology Mass Spectrometry Techniques and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Metabolomics and Mass Spectrometry Studies