jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohammed S. Alam | Laura McGregor | Roy M. Harrison
Source: TrAC Trends in Analytical Chemistry (review, 2024-04-16)
Field: Chemistry — Spectroscopy

A review of organic aerosol speciation by comprehensive two-dimensional gas chromatography Comprehensive two-dimensional gas chromatography (GC×GC) is a mature analytical technique with a far greater separation capability than conventional one-dimensional methods. Atmospheric organic aerosols are comprised of a diverse range of substances which challenge one-dimensional analysis, and for which the two-dimensional method has proved very suitable. The review outlines the basic principles of the two-dimensional separation, and the instrumentation, including detectors. Time-of-flight mass spectrometry offers many advantages, especially with unknown mixtures, and has included use of low and high energy electron impact ionisation. The available methods of data reduction and analysis are outlined. GC×GC may be used in either targeted or untargeted modes, and studies have covered a very wide range of analytes, including many compound groups. Most common are studies of hydrocarbons, including polycyclic aromatic hydrocarbons (PAH), but other work has included oxygenates, chlorinated and nitrogen compounds, and diverse industrial chemicals and consumer products sampled across a range of environments. Gas chromatography ; Mass spectrometry ; Oxygenate ; Chemistry ; Analyte ; Environmental chemistry ; Range (aeronautics) ; Instrumentation (computer programming) ; Aerosol ; Chromatography ; Environmental science ; Materials science ; Organic chemistry ; Computer science Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Mass Spectrometry Techniques and Applications
Authors: Yu Zhao | Huijiao Wang | Tian Huang | Zhiqiang Guan | Zile Li | Yu Lei | Shaohua Yu | Guoxing Zheng
Source: Nanophotonics (article, 2024-09-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural network‐assisted meta‐router for fiber mode and polarization demultiplexing Advancements in computer science have propelled society into an era of data explosion, marked by a critical need for enhanced data transmission capacity, particularly in the realm of space-division multiplexing and demultiplexing devices for fiber communications. However, recently developed mode demultiplexers primarily focus on mode divisions within one dimension rather than multiple dimensions (i.e., intensity distributions and polarization states), which significantly limits their applicability in space-division multiplexing communications. In this context, we introduce a neural network-assisted meta-router to recognize intensity distributions and polarization states of optical fiber modes, achieved through a single layer of metasurface optimized via neural network techniques. Specifically, a four-mode meta-router is theoretically designed and experimentally characterized, which enables four modes, comprising two spatial modes with two polarization states, independently divided into distinct spatial regions, and successfully recognized by positions of corresponding spatial regions. Our framework provides a paradigm for fiber mode demultiplexing apparatus characterized by application compatibility, transmission capacity, and function scalability with ultra-simple design and ultra-compact device. Merging metasurfaces, neural network and mode routing, this proposed framework paves a practical pathway towards intelligent metasurface-aided optical interconnection, including applications such as fiber communication, object recognition and classification, as well as information display, processing, and encryption. Multiplexing ; Computer science ; Router ; Scalability ; Electronic engineering ; Computer network ; Telecommunications ; Engineering Metamaterials and Metasurfaces Applications ; Neural Networks and Reservoir Computing ; Photonic and Optical Devices
Authors: Yi Ren | Yongji Wang | Ziyu Xiong | Yunping Bai | Huashun Wen | Kunpeng Zhai | Sha Zhu | Ning Hua Zhu
Source: Advanced Functional Materials (article, 2024-12-06)
Field: Materials Science — Materials Chemistry

Reconfigurable Artificial Perception System and Logic Gates Based on Large‐Scale Antiambipolar 2D Heterostructure Array Abstract Van der Waals (vdWs) heterostructures enable the fabrication of various optoelectronic devices with unprecedented functionalities, particularly antiambipolar transistors. Different methods have been employed to construct such devices; however, their integration and versatility remain significantly constrained. Here, large‐scale antiambipolar vdWs heterostructure arrays capable of implementing self‐adaptive artificial perception systems and reconfigurable logic gates are verified adopting 2D Te/WS 2 heterojunctions for the first time. The resulting transistors allow the modulation of band slope and depletion regions in individual constituents through electrostatic gating, delivering a high rectification ratio of 1.8 × 10 4 . Benefiting from the gate‐tunable antiambipolar characteristic and charge capture, such a device can realize reconfigurable modulation of synaptic plasticity between excitatory and inhibitory modes, enabling the emulation of artificial perception system for the adaptive and subjective perception adjustment in various external environments and also a recognition accuracy of 90.5% for image classification through training and inference simulations. In addition, five fundamental logic gate operations with good stability and reliability, as well as low energy consumption, are achieved in the dual‐gate antiambipolar transistor by tuning the input signals. Furthermore, similar antiambipolar characteristics are also observed from other 2D Te‐based heterostructures, defining a simple, effective, and universal method to create multifunctional antiambipolar transistors. Heterojunction ; Materials science ; Transistor ; Emulation ; Modulation (music) ; Computer science ; Logic gate ; Optoelectronics ; Electronic engineering ; Voltage ; Electrical engineering ; Physics ; Algorithm ; Engineering 2D Materials and Applications ; Advanced Memory and Neural Computing ; Perovskite Materials and Applications
Authors: Syahril Siregar | Asep Nurhikmat | Rima Zuriah Amdani | Retno Utami Hatmi | Mahargono Kobarsih | Annisa Kusumaningrum | M. Karim | Amarilia Harsanti Dameswari | Nugroho Siswanto | Siswoprayogi Siswoprayogi | Ponco Yuliyanto
Source: ACS Omega (article, 2024-07-16)
Field: Chemistry — Analytical Chemistry

Estimation of Proximate Composition in Rice Using ATR-FTIR Spectroscopy and Chemometrics This study presents an innovative approach for estimating the proximate composition of diverse rice varieties using attenuated total reflectance fourier transform infrared (ATR-FTIR) spectroscopy and chemometric techniques. Principal component analysis (PCA) reveals distinct separations among the seven rice varieties based on their FTIR spectra. Robust partial least squares (PLS) regression models, developed with high calibration (<i>R</i> <sup>2</sup>) values from 0.778 for protein up to 0.941 for moisture, demonstrate high accuracy in predicting proximate composition. The root mean squared error (RMSE) in percentage values, indicative of prediction accuracy, were low across all proximate components. To ensure the response variable of regression, proximate composition measurements were taken five times, while FTIR spectra were scanned tens of times, employing random numbers around the average with the same standard deviation as the measurement. Notably, the study emphasizes the pivotal role of the amide-III band in protein determination, alongside specific wavenumber regions associated with molecular changes in proximate components. This research underscores the potential of ATR-FTIR spectroscopy and chemometrics for rapid and accurate proximate assessment in food science and agriculture. Chemometrics ; Partial least squares regression ; Fourier transform infrared spectroscopy ; Attenuated total reflection ; Principal component analysis ; Principal component regression ; Analytical Chemistry (journal) ; Fourier transform ; Spectroscopy ; Fourier transform spectroscopy ; Chemistry ; Mathematics ; Biological system ; Chromatography ; Statistics ; Optics ; Biology ; Physics Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Food composition and properties
Authors: Milad Razbin | Mortaza Salehian | Ali Akbar Gharehaghaji
Source: Scientific Reports (article, 2024-10-11)
Field: Materials Science — Polymers and Plastics

A viscoelastic-plastic model for the core of various close-packings of multifilament polyamide-6 yarns Different forms of close-packed yarns can be produced by varying the number of monofilaments in the core region, ranging from one to five. Numerous efforts have been made to model or simulate the mechanical response of close-packed yarns; however, previous studies have predominantly focused on one or two monofilaments in the core. In this study, we propose an analytical approach that combines a geometrical model with an artificial neural network (ANN) to predict the tensile behavior of close-packed yarns containing 2 to 5 monofilaments in the core region. The novelty of this hybrid model lies not only in accounting for more than two monofilaments in the core but also in extending the prediction range from elastic to viscoelastic-plastic behavior. Validation of the proposed method showed excellent agreement between experimental and theoretical results. Numerical simulations further confirmed that the results align with theoretical predictions, demonstrating the model's accuracy in predicting the tensile behavior of close-packed yarns. This modeling approach has the potential to significantly improve the understanding and modeling of textile structures. Viscoelasticity ; Core (optical fiber) ; Ultimate tensile strength ; Materials science ; Polyamide ; Composite material ; Computer science Textile materials and evaluations ; Mechanical Behavior of Composites ; Dyeing and Modifying Textile Fibers
Authors: Nicholas R. Ellin | Yingchan Guo | Ramón Alain Miranda‐Quintana | Boone M. Prentice
Source: Digital Discovery (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Extended similarity methods for efficient data mining in imaging mass spectrometry Imaging mass spectrometry is a label-free imaging modality that allows for the spatial mapping of many compounds directly in tissues. In an imaging mass spectrometry experiment, a raster of the tissue surface produces a mass spectrum at each sampled <i>x</i>, <i>y</i> position, resulting in thousands of individual mass spectra, each comprising a pixel in the resulting ion images. However, efficient analysis of imaging mass spectrometry datasets can be challenging due to the hyperspectral characteristics of the data. Each spectrum contains several thousand unique compounds at discrete <i>m</i>/<i>z</i> values that result in unique ion images, which demands robust and efficient algorithms for searching, statistical analysis, and visualization. Some traditional post-processing techniques are fundamentally ill-equipped to dissect these types of data. For example, while principal component analysis (PCA) has long served as a useful tool for mining imaging mass spectrometry datasets to identify correlated analytes and biological regions of interest, the interpretation of the PCA scores and loadings can be non-trivial. The loadings often contain negative peaks in the PCA-derived pseudo-spectra, which are difficult to ascribe to underlying tissue biology. Herein, we have utilized extended similarity indices to streamline the interpretation of imaging mass spectrometry data. This novel workflow uses PCA as a pixel-selection method to parse out the most and least correlated pixels, which are then compared using the extended similarity indices. The extended similarity indices complement PCA by removing all non-physical artifacts and streamlining the interpretation of large volumes of imaging mass spectrometry spectra simultaneously. The linear complexity, O(<i>N</i>), of these indices suggests that large imaging mass spectrometry datasets can be analyzed in a 1 : 1 scale of time and space with respect to the size of the input data. The extended similarity indices algorithmic workflow is exemplified here by identifying discrete biological regions of mouse brain tissue. Mass spectrometry imaging ; Similarity (geometry) ; Mass spectrometry ; Data mining ; Computer science ; Information retrieval ; Artificial intelligence ; Chemistry ; Chromatography ; Image (mathematics) Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Weiyan Zhou | Tingting Wu | Matthew D. Lew
Source: The Journal of Physical Chemistry A (article, 2024-07-09)
Field: Chemistry — Spectroscopy

Fundamental Limits in Measuring the Anisotropic Rotational Diffusion of Single Molecules Many biophysical techniques, such as single-molecule fluorescence correlation spectroscopy, Förster resonance energy transfer, and fluorescence anisotropy, measure the translation and rotation of biomolecules to quantify molecular processes at the nanoscale. These methods often simplify data analysis by assuming isotropic rotational diffusion, e.g., that molecules wobble within a circular cone. This simplification ignores the anisotropy present in many biological contexts that may cause molecules to exhibit different degrees of diffusion in different directions. Here, we loosen this assumption and establish a theoretical framework for describing and measuring anisotropic rotational diffusion using fluorescence imaging. We show that anisotropic wobble is directly quantified by the eigenvalues of a 3-by-3 positive-semidefinite Hermitian matrix <i>M</i> consisting of the second-order moments of a molecule's transition dipole <b>μ</b>. This formalism enables us to model the influence of unavoidable shot noise using a Hermitian perturbation matrix <i>E</i>; the eigenvalues of <i>E</i> directly bound errors in measurements of wobble via Weyl's inequality. Quantifying various perturbations <i>E</i> reveals that anisotropic wobble measurements are generally more sensitive to errors compared to quantifying isotropic wobble. Moreover, severe shot noise can induce negative eigenvalues in estimates of <i>M</i>, thereby causing the anisotropic wobble measurement to fail. Our analysis, using Fisher information, shows that techniques with worse orientation measurement sensitivity experience stronger perturbations <i>E</i> and require larger signal to background ratios to measure anisotropic rotational diffusion accurately. Our work provides deep insights for improving the state of the art in imaging the orientations and anisotropic rotational diffusion of single molecules. Rotational diffusion ; Anisotropy ; Diffusion ; Molecule ; Physics ; Materials science ; Optics ; Quantum mechanics Analytical Chemistry and Chromatography ; thermodynamics and calorimetric analyses ; Field-Flow Fractionation Techniques
Authors: Peder Lyngby | Kristian S. Thygesen
Source: 2D Materials (preprint, 2024-06-04)
Field: Materials Science — Materials Chemistry

Ab initio property characterisation of thousands of previously unexplored 2D materials Abstract We perform extensive density functional theory calculations to determine the stability and elementary properties of 4249 previously unexplored monolayer crystals. The monolayers comprise the most stable subset (energy within 0.1 eV/atom of the convex hull) of a larger portfolio of two-dimensional (2D) materials recently discovered using a deep generative model and systematic lattice decoration schemes. The relaxed 2D structures are run through the basic property workflow of the Computational 2D Materials Database (C2DB) to evaluate the dynamical stability and obtain the stiffness tensor, piezoelectric tensor, deformation potentials, Born and Bader charges, electronic band structure, effective masses, plasma frequency, Fermi surface, projected density of states, magnetic moments, magnetic exchange couplings, magnetic anisotropy, topological indices, optical- and infrared polarisability. We provide statistical overviews of the property data and highlight a few specific examples of interesting materials. Our work exposes previously unknown parts of the 2D chemical space and provides a basis for the discovery of 2D materials with specific properties. All data is available in the C2DB. Ab initio ; Density functional theory ; Tensor (intrinsic definition) ; Magnetic monopole ; Physics ; Condensed matter physics ; Statistical physics ; Materials science ; Computer science ; Quantum mechanics ; Mathematics ; Geometry 2D Materials and Applications ; Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research
Authors: Jiwen Ren | Yuming Xiong | Xinyu Chen | Yong Hao
Source: Sensors (article, 2024-08-22)
Field: Chemistry — Analytical Chemistry

Comparative Analysis of Machine Learning and Deep Learning Algorithms for Assessing Agricultural Product Quality Using NIRS The success of near-infrared spectroscopy (NIRS) analysis hinges on the precision and robustness of the calibration model. Shallow learning (SL) algorithms like partial least squares discriminant analysis (PLS-DA) often fall short in capturing the interrelationships between adjacent spectral variables, and the analysis results are easily affected by spectral noise, which dramatically limits the breadth and depth of applications of NIRS. Deep learning (DL) methods, with their capacity to discern intricate features from limited samples, have been progressively integrated into NIRS. In this paper, two discriminant analysis problems, including wheat kernels and Yali pears as examples, and several representative calibration models were used to research the robustness and effectiveness of the model. Additionally, this article proposed a near-infrared calibration model, which was based on the Gramian angular difference field method and coordinate attention convolutional neural networks (G-CACNNs). The research results show that, compared with SL, spectral preprocessing has a smaller impact on the analysis accuracy of consensus learning (CL) and DL, and the latter has the highest analysis accuracy in the modeling results using the original spectrum. The accuracy of G-CACNNs in two discrimination tasks was 98.48% and 99.39%. Finally, this research compared the performance of various models under noise to evaluate the robustness and noise resistance of the proposed method. Robustness (evolution) ; Artificial intelligence ; Linear discriminant analysis ; Partial least squares regression ; Preprocessor ; Computer science ; Machine learning ; Pattern recognition (psychology) ; Algorithm ; Chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Huiling Luo | Huanhuan Gao | Yanzhao Wang | Chaohui Wang | Fan Zhang | Yanzhang Shao | Tong Liu | Zhengjie Wang | He‐Xiu Xu
Source: Nanophotonics (article, 2024-08-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multichannel full‐space coding metasurface with linearly‐circularly‐polarized wavefront manipulation Achieving independent multitasked wavefront control by using an ultrathin plate is a challenge to increase information capacity in integration optics and radar applications. Transmission-reflection-integrated metasurface provides an efficient recipe primarily for multifunctional meta-device, however it is challenging to synergize both linear polarization (LP) and circular polarization (CP) using a single meta-plate. Here, a multichannel full-space coding metasurface composed of interleaved shared-aperture meta-atom is proposed to achieve large information capacity by capsulating judiciously engineered high efficiency triple sub-elements (modes) in four-layer scheme. By rotating dual-gap split ring resonator and varying size of "L" type structure insulating by a metallic ring with electrostatic-analogue shielding effect, both Pancharatnam-Berry (PB) and dynamic phases are independently realized under CP and LP waves, respectively. Such an extraordinary insulating strategy completely suppresses crosstalk among three modes and unprecedentedly increases the capability in yielding kaleidoscopic wavefront control. To verify the significance, a proof-of-concept metadevice is devised and experimentally demonstrated with tri-channel wavefront manipulations, exhibiting reflective dual-vortex beam and Bessel beam for forward and backward CP wave, respectively at high frequency, while transmissive polarization beam splitting for 45°-LP wave at low frequency. Our finding in polarization-direction multiplexing is expected to generate great interest in electromagnetic integration with emerging degree of freedoms. Wavefront ; Optics ; Physics ; Polarization (electrochemistry) ; Resonator ; Linear polarization ; Multiplexing ; Laser ; Computer science ; Telecommunications Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Ashma Begum
Source: International Journal of Biochemistry and Molecular Biology (review, 2024-01-01)
Field: Chemistry — Organic Chemistry

Adulteration in edible oil (mustard oil) and ghee; detection and their effects on human health Edible oils and ghee are vital parts of our daily culinary practices. In recent years, owing to heightened demand in the domestic and global markets, consistent reports regarding the adulteration of edible oils and ghee with substandard ingredients have been reported. Adulteration in edible oils is widespread, with distinctive contaminants, including cottonseed, mineral, and lower-cost oils like palm olein. In the case of ghee, it is repeatedly combined with animal fats, synthetic materials, or vanaspati. The consumption of contaminated oils and trans-fats within the human diet has resulted in adverse health effects, including cardiovascular diseases, digestive disorders, and even cancer. The review aims to summarize various adulterants found in edible oil (mustard oil) and ghee, their detection techniques, and harmful effects. This review provides an overview of the contemplation linked to the adulteration of edible oils and ghee, the compliance with relevant regulations, and the technological approaches available for detection. The detection technologies for identifying adulteration in edible oils are chromatography and spectroscopy biochemical methods and the use of high-precision analytical instruments. The presence of adulterants in edible oil and ghee undermines our societal integrity and our ethical standards. Awareness among consumers is essential for effectively combating these adulterations. Human health ; Medicine ; Mustard Plant ; Edible oil ; Food science ; Traditional medicine ; Environmental health ; Chemistry ; Biology ; Brassica ; Agronomy Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies