jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jonas Brown | Maedeh Taheri | Fariborz Kargar | Ruben Salgado | Tekwam Geremew | Sergey Rumyantsev | Roger K. Lake | Alexander A. Balandin
Source: Applied Physics Reviews (article, 2023-10-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Current fluctuations and domain depinning in quasi-two-dimensional charge-density-wave 1<i>T</i>-TaS2 thin films We investigated the temperature dependence of the current fluctuations in thin films of the quasi-two-dimensional 1T-TaS2 van der Waals material. The current fluctuations, determined from the derivative current–voltage characteristics of two-terminal 1T-TaS2 devices, appear prominently at the electric fields that correspond to the transitions between various charge-density-wave macroscopic quantum condensate phases and at the onset of the depinning of the charge density wave domains. The depinning threshold field, ED, monotonically increases with decreasing temperature within the nearly commensurate charge-density-wave phase. The ED value increases with the decreasing 1T-TaS2 film thickness, revealing the surface pinning of the charge density waves. Our analysis suggests that the domain depinning is pronounced in the nearly commensurate phase. It is induced by the electric field but facilitated by local heating. The measured trends for ED of the domain depinning are important for understanding the physics of charge density waves in quasi-two-dimensional crystals and for developing electronic devices based on this type of quantum materials. Charge density wave ; Condensed matter physics ; Electric field ; van der Waals force ; Current density ; Charge density ; Charge (physics) ; Physics ; Density wave theory ; Phase (matter) ; Field (mathematics) ; Materials science ; Quantum mechanics ; Superconductivity Organic and Molecular Conductors Research ; 2D Materials and Applications ; Perovskite Materials and Applications
Authors: Natalia Starodubtseva | Alisa Tokareva | В. В. Родионов | Alexander Brzhozovskiy | Anna E. Bugrova | Vitaliy Chagovets | В. В. Кометова | Evgenii Kukaev | Nelson C. Soares | Kovalev Gi | А. С. Кононихин | Vladimir Frankevich | Е. Н. Николаев | Г. Т. Сухих
Source: Biomedicines (article, 2023-06-22)
Field: Chemistry — Spectroscopy

Integrating Proteomics and Lipidomics for Evaluating the Risk of Breast Cancer Progression: A Pilot Study Metastasis is a serious and often life-threatening condition, representing the leading cause of death among women with breast cancer (BC). Although the current clinical classification of BC is well-established, the addition of minimally invasive laboratory tests based on peripheral blood biomarkers that reflect pathological changes in the body is of utmost importance. In the current study, the serum proteome and lipidome profiles for 50 BC patients with (25) and without (25) metastasis were studied. Targeted proteomic analysis for concertation measurements of 125 proteins in the serum was performed via liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM MS) using the BAK 125 kit (MRM Proteomics Inc., Victoria, BC, Canada). Untargeted label-free lipidomic analysis was performed using liquid chromatography coupled to tandem mass-spectrometry (LC-MS/MS), in both positive and negative ion modes. Finally, 87 serum proteins and 295 lipids were quantified and showed a moderate correlation with tumor grade, histological and biological subtypes, and the number of lymph node metastases. Two highly accurate classifiers that enabled distinguishing between metastatic and non-metastatic BC were developed based on proteomic (accuracy 90%) and lipidomic (accuracy 80%) features. The best classifier (91% sensitivity, 89% specificity, AUC = 0.92) for BC metastasis diagnostics was based on logistic regression and the serum levels of 11 proteins: alpha-2-macroglobulin, coagulation factor XII, adiponectin, leucine-rich alpha-2-glycoprotein, alpha-2-HS-glycoprotein, Ig mu chain C region, apolipoprotein C-IV, carbonic anhydrase 1, apolipoprotein A-II, apolipoprotein C-II and alpha-1-acid glycoprotein 1. Proteomics ; Lipidomics ; Proteome ; Apolipoprotein A1 ; Breast cancer ; Blood proteins ; Apolipoprotein B ; Selected reaction monitoring ; Tandem mass spectrometry ; Cancer ; Chemistry ; Medicine ; Mass spectrometry ; Internal medicine ; Biochemistry ; Chromatography ; Cholesterol Advanced Proteomics Techniques and Applications ; Cancer, Lipids, and Metabolism ; Metabolomics and Mass Spectrometry Studies
Authors: Yuze Lu | Mali Gong | Jing Li | Jianshe Ma
Source: Agronomy (article, 2023-08-24)
Field: Chemistry — Analytical Chemistry

Strawberry Defect Identification Using Deep Learning Infrared–Visible Image Fusion Feature detection of strawberry multi-type defects and the ripeness stage faces huge challenges because of color diversity and visual similarity. Images from hyperspectral near-infrared (NIR) information sources are also limited by their low spatial resolution. In this study, an accurate RGB image (with a spatial resolution of 2048×1536 pixels) and NIR image (ranging from 700–1100 nm in wavelength, covering 146 bands, and with a spatial resolution of 696×700 pixels) fusion method was proposed to improve the detection of defects and features in strawberries. This fusion method was based on a pretrained VGG-19 model. The high-frequency parts of original RGB and NIR image pairs were filtered and fed into the pretrained VGG-19 simultaneously. The high-frequency features were extracted and output into ReLU layers; the l1-norm was used to fuse multiple feature maps into one feature map, and area pixel averaging was introduced to avoid the effect of extreme pixels. The high- and low-frequency parts of RGB and NIR were summed into one image according to the information weights at the end. In the validation section, the detection dataset included expanded 4000 RGB images and 4000 NIR images (training and testing set ratio was 4:1) from 240 strawberry samples labeled as mud contaminated, bruised, both defects, defect-free, ripe, half-ripe, and unripe. The detection neural network YOLOv3-tiny operated on RGB-only, NIR-only, and fused image input modes, achieving the highest mean average precision of 87.18% for the proposed method. Finally, the effects of different RGB and NIR weights on the detection results were also studied. This research demonstrated that the proposed fusion method can greatly improve the defect and feature detection of strawberry samples. Artificial intelligence ; RGB color model ; Pixel ; Computer vision ; Computer science ; Pattern recognition (psychology) ; Feature (linguistics) ; Image resolution ; Ranging ; Remote sensing ; Geography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Image Fusion Techniques
Authors: Xin Li | Qiushi Li | Liang Wu | Zongcheng Xu | Jianquan Yao
Source: Materials (article, 2023-03-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Focusing on the Development and Current Status of Metamaterial Absorber by Bibliometric Analysis In this paper, a total of 4770 effective documents about metamaterial absorbers were retrieved from the Web of Science Core Collection database. We scientifically analyzed the co-occurrence network of co-citation analysis by author, country/region, institutional, document, keywords co-occurrence, and the timeline of the clusters in the field of metamaterial absorber. Landy N. I.'s, with his cooperator et al., first experiment demonstrated a perfect metamaterial absorber microwave to absorb all incidents of radiation. From then on, a single-band absorber, dual-band absorber, triple-band absorber, multi-band absorber and broad-band absorber have been proposed and investigated widely. By integrating graphene and vanadium dioxide to the metamaterial absorber, the frequency-agile functionality can be realized. Tunable absorption will be very important in the future, especially metamaterial absorbers based on all-silicon. This paper provides a new research method to study and evaluate the performance of metamaterial absorbers. It can also help new researchers in the field of metamaterial absorbers to achieve the development of research content and to understand the recent progress. Current (fluid) ; Metamaterial ; Materials science ; Engineering physics ; Optics ; Engineering ; Physics ; Electrical engineering Metamaterials and Metasurfaces Applications ; Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies
Authors: Puneet Mishra | J.A. Verschoor | Mariska Nijenhuis‐de Vries | G. Polder | Martin P. Boer
Source: Infrared Physics & Technology (article, 2023-04-07)
Field: Chemistry — Analytical Chemistry

Portable near-infrared spectral imaging combining deep learning and chemometrics for dry matter and soluble solids prediction in intact kiwifruit A novel case of developing a portable spectral imaging device for kiwifruit analysis is presented. Furthermore, a new complementary spectral image processing strategy combining deep learning and advanced chemometric is proposed for processing the spectral images. The deep learning was used for detection and localisation of harvested fruit in the spectral image while the chemometric modelling was used to predict multiple fruit quality related properties i.e., dry matter and soluble solids content. The developed models were independently validated on fruit harvested from a different orchard as well as on a different variety. The one touch spectral imaging presented in this paper can allow widespread usage of spectral imaging for fresh fruit analysis, particularly benefitting non-experts in spectral imaging and chemometrics to routinely use the spectral imaging for fresh fruit analysis. Chemometrics ; Spectral imaging ; Imaging spectroscopy ; Hyperspectral imaging ; Computer science ; Spectral analysis ; Spectral signature ; Artificial intelligence ; Near-infrared spectroscopy ; Spectral properties ; Biological system ; Remote sensing ; Pattern recognition (psychology) ; Materials science ; Chemistry ; Optics ; Spectroscopy ; Machine learning ; Physics ; Geology ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Michael Horf | Robin Gebbers | Hans‐Werner Olfs | Sebastian Vogel
Source: The Science of The Total Environment (article, 2023-11-01)
Field: Chemistry — Analytical Chemistry

Determining nutrients, dry matter, and pH of liquid organic manures using visual and near-infrared spectrometry In agriculture, overfertilization with liquid organic manures (LOM) is causing environmental issues including eutrophication of non-agricultural ecosystems and nitrate pollution of groundwater. To avoid such problems, a precise and demand-oriented fertilization with LOM is needed. This can only be achieved if the nutrient composition of the LOM is known. However, traditional chemical analysis is cost- and time-intensive and furthermore dependent on a representative sample. Optical spectrometry in the visible and near-infrared range could provide an efficient alternative, if a chemometric calibration assures sufficient accuracy. To improve chemometric calibration, this study investigated several spectral preprocessing and regression algorithms, and compared predictions based either on dry or wet weight concentration. In addition, the capability of low-cost spectrometers was evaluated by simulating low-resolution spectra with smaller wavelength ranges. The reflectance spectra of 391 pig manure, 155 cattle manure, and 89 biogas digestate samples were used to predict plant macronutrients (N, P, K, Mg, Ca, S), micronutrients (Mn, Fe, Cu, Zn, B), dry matter (DM) and pH. The experiments demonstrate the general aptness of optical spectrometry to accurately predict DM, pH and all nutrients except boron in pig, cattle, and digestate LOM, even with simulated low-cost spectrometers. Best results show r<sup>2</sup> between 0.80 and 0.97, ratios of performance to interquartile distance (RPIQ) between 2.1 and 7.8, and mean absolute errors normalized by the median (nMAE) between 5 and 36 %. The regression methods PLSR, LASSO, and least angle regression predominantly performed best. The innovative preprocessing methods named simple ratios (SR) and normalized differences (ND) proved to be very useful algorithms, especially for N and P predictions, outperforming the accuracy of classical techniques in several cases. Concentrations on dry weight basis improved predictions of K, Mn, and pH. Digestate ; Manure ; Partial least squares regression ; Calibration ; Chemistry ; Nutrient ; Environmental science ; Mathematics ; Agronomy ; Anaerobic digestion ; Statistics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Soil Geostatistics and Mapping
Authors: Long Duong Ha | Ki Jun Kim | Seong Jung Kwon | Byoung‐Yong Chang | Seongpil Hwang
Source: Small (article, 2023-05-10)
Field: Chemistry — Electrochemistry

Time‐Resolved Electrochemical Impedance Spectroscopy of Stochastic Nanoparticle Collision: Short Time Fourier Transform versus Continuous Wavelet Transform This work demonstrates the utilization of short-time Fourier transform (STFT), and continuous wavelet transform (CWT) electrochemical impedance spectroscopy (EIS) for time-resolved analysis of stochastic collision events of platinum nanoparticles (NPs) onto gold ultramicroelectrode (UME). The enhanced electrocatalytic activity is observed in both chronoamperometry (CA) and EIS. CA provides the impact moment and rough estimation of the size of NPs. The quantitative information such as charge transfer resistance (R<sub>ct</sub> ) relevant to the exchange current density of a single Pt NP is estimated from EIS. The CWT analysis of the phase angle parameter is better for NP collision detection in terms of time resolution compared to the STFT method. Short-time Fourier transform ; Ultramicroelectrode ; Dielectric spectroscopy ; Materials science ; Fourier transform ; Continuous wavelet transform ; Analytical Chemistry (journal) ; Wavelet transform ; Fourier transform infrared spectroscopy ; Chronoamperometry ; Wavelet ; Cyclic voltammetry ; Fourier analysis ; Chemistry ; Electrode ; Discrete wavelet transform ; Electrochemistry ; Mathematics ; Computer science ; Physics ; Artificial intelligence ; Mathematical analysis ; Optics ; Physical chemistry Electrochemical Analysis and Applications ; Conducting polymers and applications ; Analytical Chemistry and Sensors
Authors: Jaka Kragelj | Rania Dumarieh | Yiling Xiao | Kendra K. Frederick
Source: Protein Science (article, 2023-03-17)
Field: Chemistry — Spectroscopy

Conformational ensembles explain <scp>NMR</scp> spectra of frozen intrinsically disordered proteins Protein regions which are intrinsically disordered, exist as an ensemble of rapidly interconverting structures. Cooling proteins to cryogenic temperatures for dynamic nuclear polarization (DNP) magic angle spinning (MAS) NMR studies suspends most of the motions, resulting in peaks that are broad but not featureless. To demonstrate that detailed conformational restraints can be retrieved from the peak shapes of frozen proteins alone, we developed and used a simulation framework to assign peak features to conformers in the ensemble. We validated our simulations by comparing them to spectra of α-synuclein acquired under different experimental conditions. Our assignments of peaks to discrete dihedral angle populations suggest that structural constraints are attainable under cryogenic conditions. The ability to infer ensemble populations from peak shapes has important implications for DNP MAS NMR studies of proteins with regions of disorder in living cells because chemical shifts are the most accessible measured parameter. Dihedral angle ; Conformational isomerism ; Magic angle spinning ; NMR spectra database ; Chemistry ; Intrinsically disordered proteins ; Nuclear magnetic resonance spectroscopy ; Molecular dynamics ; Spectral line ; Crystallography ; Physics ; Computational chemistry ; Stereochemistry ; Molecule ; Biochemistry Advanced NMR Techniques and Applications ; Protein Structure and Dynamics ; Enzyme Structure and Function
Authors: Dan Peng | Rui‐Hua Xu | Qi Zhou | Jinxia Yue | Min Su | Shaoshuai Zheng | Jun Li
Source: Molecules (article, 2023-07-28)
Field: Chemistry — Analytical Chemistry

Discrimination of Milk Freshness Based on Synchronous Two-Dimensional Visible/Near-Infrared Correlation Spectroscopy Coupled with Chemometrics Milk is one of the preferred beverages in modern healthy diets, and its freshness is of great significance for product sales and applications. By combining the two-dimensional (2D) correlation spectroscopy technique and chemometrics, a new method based on visible/near-infrared (Vis/NIR) spectroscopy was proposed to discriminate the freshness of milk. To clarify the relationship be-tween the freshness of milk and the spectra, the changes in the physicochemical indicators of milk during storage were analyzed as well as the Vis/NIR spectra and the 2D-Vis/NIR correlation spectra. The threshold-value method, linear discriminant analysis (LDA) method, and support vector machine (SVM) method were used to construct the discriminant models of milk freshness, and the parameters of the SVM-based models were optimized by the grid search method and particle swarm optimization algorithm. The results showed that with the prolongation of storage time, the absorbance of the Vis/NIR spectra of milk gradually increased, and the intensity of autocorrelation peaks and cross peaks in synchronous 2D-Vis/NIR spectra also increased significantly. Compared with the SVM-based models using Vis/NIR spectra, the SVM-based model using 2D-Vis/NIR spectra had a >15% higher prediction accuracy. Under the same conditions, the prediction performances of the SVM-based models were better than those of the threshold-value-based or LDA-based models. In addition, the accuracy rate of the SVM-based model using the synchronous 2D-Vis/NIR autocorrelation spectra was >97%. This work indicates that the 2D-Vis/NIR correlation spectra coupled with chemometrics is a great pattern to rapidly discriminate the freshness of milk, which provides technical support for improving the evaluation system of milk quality and maintaining the safety of milk product quality. Chemometrics ; Linear discriminant analysis ; Absorbance ; Support vector machine ; Near-infrared spectroscopy ; Spectroscopy ; Autocorrelation ; Chemistry ; Spectral line ; Analytical Chemistry (journal) ; Pattern recognition (psychology) ; Biological system ; Mathematics ; Artificial intelligence ; Statistics ; Computer science ; Chromatography ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Jian Chen | Wei Ding | Yuan‐Cheng Shi | Zhen‐Xu Yao | Rui‐Xin Wu
Source: Advanced Optical Materials (article, 2023-12-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Achieving Functional Meta‐Devices by Generalized Meta‐Atom Model for Metasurfaces and Genetic Algorithm Abstract Built by machine‐learning, the surrogate model of metasurfaces reduces the need for a huge number of simulations in the design process, enhancing the efficiency and performance of the designed meta‐devices. However, the surrogate model of metasurfaces is often constructed‐based on specific physical perspectives or experiences, which limits its versatility. In this study, a generalized surrogate meta‐atom model for metasurfaces is introduced. This model can simulate arbitrary meta‐atoms and their corresponding electromagnetic responses at any polarization within the full space of pixelated unit cells. Utilizing a genetic algorithm, the model is employed to design various types of meta‐devices, automatically generating configurations of meta‐atoms with optimal performance for specific application scenarios. Three typical meta‐devices, including the reflective linear‐circular polarization converter, the metasurface‐based absorber, and the asymmetrical transmission meta‐slab, are designed and validated through full‐wave simulations and/or experiments. This work presents an efficient and flexible approach to model arbitrary metasurfaces, opening new possibilities for metasurface design and applications. Surrogate model ; Computer science ; Metamodeling ; Polarization (electrochemistry) ; Genetic algorithm ; Design process ; Engineering design process ; Electronic engineering ; Materials science ; Work in process ; Mechanical engineering ; Machine learning Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Cheng Guo | Jin Zhang | Wensheng Cai | Xueguang Shao
Source: Applied Sciences (article, 2023-04-26)
Field: Chemistry — Analytical Chemistry

Enhancing Transferability of Near-Infrared Spectral Models for Soluble Solids Content Prediction across Different Fruits Near-infrared (NIR) spectroscopy is widely used for non-destructive detection of fruit quality, but the transferability of NIR models between different fruits is still a challenge. This study investigates the transferability of NIR models from strawberry to grape and apple using two case studies. A total of 94 strawberry, 80 grape, and 125 apple samples were measured for their soluble solids content (SSC) and NIR spectra. Partial least squares (PLS) regression was used to establish a model for predicting strawberry SSC, with an acceptable root mean square error of prediction (RMSEP) and correlation coefficient (R) of 0.53 °Brix and 0.91, respectively. Directly applying the strawberry model to grape and apple spectra significantly degrades the performance, increasing the RMSEP up to 3.47 and 16.40, respectively. Spectral preprocessing can improve the predictions for all three fruits, but the bias cannot be eliminated. Global modeling produces a generalized model, but the prediction for strawberry degrades. Calibration transfer with SS-PFCE and PLS correction, which are calibration methods without standard samples, was found to be an effective way to improve the prediction of grape and apple spectra using the strawberry model. Therefore, calibration transfer may be a feasible way for improving the transferability of NIR models for multiple fruits. Partial least squares regression ; Transferability ; Calibration ; Near-infrared spectroscopy ; Coefficient of determination ; Brix ; Mean squared error ; Mathematics ; Correlation coefficient ; Chemistry ; Food science ; Statistics ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research