jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: James Flannigan | D. S. MacIver | Hikaru G. Jolliffe | Mark Haw | Ján Šefčı́k
Source: Crystals (article, 2023-09-18)
Field: Materials Science — Materials Chemistry

Nucleation and Growth Kinetics of Sodium Chloride Crystallization from Water and Deuterium Oxide Despite the ubiquity of the crystallization of sodium chloride (NaCl) throughout history, few detailed, well-controlled quantitative studies of the kinetics of NaCl crystallization have been published. Taking advantage of recent advances in technology such as image analysis for crystallite counting and ‘high-throughput’ techniques for characterizing the highly stochastic nucleation process, we report on a detailed examination of the primary and secondary nucleation kinetics of NaCl, crystallized from solution, in water (H2O) and in the isotopologue D2O. We show that crystallization conditions, especially sample agitation, have a very significant effect on crystallization kinetics. We also critically evaluate the workflow employed and the associated nucleation/growth models used to interpret its results, comparing outcomes from NaCl with those from organic crystal systems with which the workflow was originally developed and demonstrated. For primary nucleation, some key assumptions of the workflow and data interpretation are called into question for the NaCl system. Even so, it can still provide direct measurements of secondary nucleation and crystal growth from crystal counting and sizing, providing valuable characterization under consistent controlled conditions to enhance and ‘bring up to date’ the literature on the crystallization of this ubiquitous system. Nucleation ; Crystallization ; Crystallite ; Crystal growth ; Kinetics ; Materials science ; Chemical engineering ; Chemistry ; Crystallography ; Physics ; Organic chemistry Crystallization and Solubility Studies ; Spectroscopy and Quantum Chemical Studies ; Enzyme Structure and Function
Authors: Yihang Qiu | Sixue Chen | Zheyu Hou | Jingjing Wang | Jian Shen | Chaoyang Li
Source: Micromachines (article, 2023-03-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Chiral Metasurface for Near-Field Imaging and Far-Field Holography Based on Deep Learning Chiral metasurfaces have great influence on the development of holography. Nonetheless, it is still challenging to design chiral metasurface structures on demand. As a machine learning method, deep learning has been applied to design metasurface in recent years. This work uses a deep neural network with a mean absolute error (MAE) of 0.03 to inverse design chiral metasurface. With the help of this approach, a chiral metasurface with circular dichroism (CD) values higher than 0.4 is designed. The static chirality of the metasurface and the hologram with an image distance of 3000 μm are characterized. The imaging results are clearly visible and demonstrate the feasibility of our inverse design approach. Holography ; Inverse ; Chirality (physics) ; Circular dichroism ; Optics ; Inverse problem ; Deep learning ; Field (mathematics) ; Computer science ; Physics ; Artificial intelligence ; Mathematics ; Geometry ; Chemistry ; Mathematical analysis ; Pure mathematics Metamaterials and Metasurfaces Applications ; Electromagnetic Scattering and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Xiao Zhang | Chenxin Zhou | Qi Sun | Ying Liu | Yutu Yang | Zilong Zhuang
Source: Agriculture (article, 2023-05-25)
Field: Chemistry — Analytical Chemistry

Prediction of Solid Soluble Content of Green Plum Based on Improved CatBoost Most green plums need to be processed before consumption, and due to personal subjective factors, manual harvesting and sorting are difficult to achieve using standardized processing. Soluble solid content (SSC) of green plum was taken as the research object in this paper. Visible near-infrared (VIS-NIR) and shortwave near-infrared (SW-NIR) full-spectrum spectral information of green plums were collected, and the spectral data were corrected and pre-processed. Random forest algorithm based on induced random selection (IRS-RF) was proposed to screen four sets of characteristic wavebands. Bayesian optimization CatBoost model (BO-CatBoost) was constructed to predict SSC value of green plums. The experimental results showed that the preprocessing method of multiplicative scatter corrections (MSC) was obviously superior to Savitzky–Golay (S–G), the prediction effect of SSC based on VIS-NIR spectral waveband by partial least squares regression model (PLSR) was obviously superior to SW-NIR spectral waveband, MSC + IRS-RF was obviously superior to corresponding combination of correlation coefficient method (CCM), successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and random forest (RF). With the lowest dimensional selected feature waveband, the lowest VIS-NIR band group was only 53, and the SW-NIR band group was only 100. The model proposed in this paper based on MSC + IRS-RF + BO-CatBoost was superior to PLSR, XGBoost, and CatBoost in predicting SSC, with R2P of 0.957, which was 3.1% higher than the traditional PLSR. Partial least squares regression ; Random forest ; Near-infrared spectroscopy ; Feature selection ; Computer science ; Artificial intelligence ; Remote sensing ; Mathematics ; Preprocessor ; Pattern recognition (psychology) ; Biological system ; Statistics ; Optics ; Physics ; Geography Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use ; Remote-Sensing Image Classification
Authors: Zhiyu Zhou | Yan‐Jun Hu | Zefei Zhu | Yaming Wang
Source: Journal of Natural Fibers (article, 2023-01-19)
Field: Materials Science — Polymers and Plastics

Fabric Wrinkle Objective Evaluation Model with Random Vector Function Link Based on Optimized Artificial Hummingbird Algorithm With the more and more amelioration of our quality of life, our needs for clothing have altered from having clothes to wearing good-looking, among which the wrinkle resistance of clothing fabric owns a giant effect on the beauty of clothing. Nowadays, artificial subjective evaluation is mainly used to evaluate the wrinkle grade of garment fabrics in the textile industry. This evaluation method owns the shortcoming of poor accuracy, being time-consuming and poor objectivity. For solving this problem, it is very important to put forward an objective evaluation model of fabric wrinkle grade. In this paper, we proposed a fabric wrinkle objective evaluation model with the optimized random vector functional link. The model applies DarkNet19 deep neural network to abstract the high-order visual features of the wrinkled surface image of the fabric, uses the improved artificial hummingbird optimization algorithm to ameliorate the import bias and weight of the random vector function link’s hidden layer, and uses L2,1 norm regularization computes output weights for random vector function links. The relative tests reveal that the objective evaluation model of fabric wrinkles put forward in this paper has excellent performance. Hummingbird ; Wrinkle ; Algorithm ; Function (biology) ; Link (geometry) ; Computer science ; Artificial intelligence ; Materials science ; Composite material ; Biology Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Color perception and design
Authors: Chun‐Jen Shih | Chao‐Yang Lin | Kai Chen | Nurul Ridho Al Amin | Dian Luo | I‐Sheng Hsu | Abdul Khalik Akbar | Sajal Biring | Chih‐Hsuan Lu | Bo‐Han Chen | Shang‐Da Yang | Jiun‐Haw Lee | Shun‐Wei Liu
Source: Advanced Science (article, 2023-09-22)
Field: Materials Science — Polymers and Plastics

Semi‐Transparent, Pixel‐Free Upconversion Goggles with Dual Audio‐Visual Communication The intractable brittleness and opacity of the crystalline semiconductor restrict the prospect of developing low-cost imaging systems. Here, infrared visualization technologies are established with large-area, semi-transparent organic upconversion devices that bring high-resolution invisible images into sight without photolithography. To exploit all photoinduced charge carriers, a monolithic device structure is proposed built on the infrared-selective, single-component charge generation layer of chloroaluminum phthalocyanine (ClAlPc) coupled to two visible light-emitting layers manipulated with unipolar charges. Transient pump-probe spectroscopy reveals that the ClAlPc-based device exhibits an efficient charge dissociation process under forward bias. This process is indicated by the prompt and strong features of electroabsorption screening. Furthermore, by imposing the electric field, the ultrafast excited state dynamic suggests a prolonged charge carrier lifetime from the ClAlPc, which facilitates the charge utilization for upconversion luminance. For the first time, >30% of the infrared photons are utilized without photomultiplication strategies owing to the trivial spectrum overlap between ClAlPc and the emitter. In addition, the device can broadcast the acoustic signal by synchronizing the device frequency with the light source, which enables to operate it in dual audio-visual mode. The work demonstrates the potential of upconversion devices for affordable infrared imaging in wearable electronics. Optoelectronics ; Materials science ; Photon upconversion ; Infrared ; Optics ; Computer science ; Physics ; Luminescence Transition Metal Oxide Nanomaterials ; Thin-Film Transistor Technologies ; Luminescence and Fluorescent Materials
Authors: Srinivasu Kunuku | Bo-Rong Lin | C.H. Chen | Chun‐Hsiang Chang | Tzung-Yuang Chen | Tung‐Yuan Hsiao | Hung-Kai Yu | Yu-Jen Chang | Li-Chuan Liao | George T.Y. Chen | Robert Bogdanowicz | Huan Niu
Source: ACS Omega (article, 2023-01-22)
Field: Materials Science — Materials Chemistry

Nanodiamonds Doped with Manganese for Applications in Magnetic Resonance Imaging Nanodiamonds (NDs) are emerging with great potential in biomedical applications like biomarking through fluorescence and magnetic resonance imaging (MRI), targeted drug delivery, and cancer therapy. The magnetic and optical properties of NDs could be tuned by selective doping. Therefore, we report multifunctional manganese-incorporated NDs (Mn-NDs) fabricated by Mn ion implantation. The fluorescent properties of Mn-NDs were tuned by inducing the defects by ion implantation and enhancing the residual nitrogen vacancy density achieved by a two-step annealing process. The cytotoxicity of Mn-NDs was investigated using NCTC clone 929 cells, and the results revealed no cytotoxicity effect. Mn-NDs have demonstrated dual mode contrast enhancement for both <i>T</i> <sub>1</sub>- and <i>T</i> <sub>2</sub>-weighted in vitro MR imaging. Furthermore, Mn-NDs have illustrated a significant increase in longitudinal relaxivity (fivefold) and transversal relaxivity (17-fold) compared to the as-received NDs. Mn-NDs are employed to investigate their ability for in vivo MR imaging by intraperitoneal (ip) injection of Mn-NDs into mice with liver tumors. After 2.5 h of ip injection, the enhancement of contrast in <i>T</i> <sub>1</sub>- and <i>T</i> <sub>2</sub>-weighted images has been observed via the accumulation of Mn-NDs in liver tumors of mice. Therefore, Mn-NDs have great potential for in vivo imaging by MR imaging in cancer therapy. In vivo ; Magnetic resonance imaging ; Materials science ; Nuclear magnetic resonance ; Manganese ; MRI contrast agent ; Cytotoxicity ; Fluorescence-lifetime imaging microscopy ; Fluorescence ; In vitro ; Biophysics ; Chemistry ; Nanotechnology ; Nanoparticle ; Medicine ; Biology ; Radiology ; Biochemistry ; Optics Diamond and Carbon-based Materials Research ; Graphene research and applications ; Carbon Nanotubes in Composites
Authors: Yihan Ma | Jonas Florentin Kolb | Achintha Ihalage | Andre Sarker Andy | Yang Hao
Source: Advanced Photonics Research (article, 2023-03-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Incorporating Meta‐Atom Interactions in Rapid Optimization of Large‐Scale Disordered Metasurfaces Based on Deep Interactive Learning Surface symmetry breaking and disorder have been recently explored to overcome operation bandwidth, unwanted diffraction, and polarization dependence issues in the conventional metasurface designs thanks to their increasing degrees of design freedom. However, efficient full‐wave simulation and optimization of electrically large electromagnetic structures have been a longstanding problem. Herein, an interactive learning approach is developed to build new meta‐atom datasets which include the effect of mutual coupling. A deep learning‐based model is developed to extract features of incident/reflection waves and their neighboring interaction responses from a limited number of known meta‐atoms. Finally, the deep neural network is incorporated with optimization algorithms to design, as an example, large‐scale metasurfaces for beam manipulation and wideband scattering reduction. The results demonstrate that the proposed architecture can be successfully applied to rapidly design aperture‐efficient metasurfaces or metalenses at large scales of over tens of thousands of meta‐atoms. Computer science ; Bandwidth (computing) ; Deep learning ; Diffraction ; Scattering ; Wideband ; Polarization (electrochemistry) ; Physics ; Optics ; Artificial intelligence ; Telecommunications ; Chemistry Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Sam Van Haute | Amin Nikkhah | Derick Malavi | Sajad Kiani
Source: Scientific Reports (article, 2023-03-14)
Field: Chemistry — Analytical Chemistry

Prediction of essential oil content in spearmint (Mentha spicata) via near-infrared hyperspectral imaging and chemometrics Spearmint (Mentha spicata L.) is grown for its essential oil (EO), which find use in food, beverage, fragrance and other industries. The current study explores the ability of near infrared hyperspectral imaging (HSI) (935 to 1720 nm) to predict, in a rapid, nondestructive manner, the essential oil content of dried spearmint (0.2 to 2.6% EO). Spectral values of spearmint samples varied considerably with spatial coordinates, and so the use of averaging the spectral values of a surface scan was warranted. Data preprocessing was done with Multiplicative Scatter Correction (MSC) or Standard Normal Variate (SNV). Selection of spectral input variables was done with Least Absolute Shrinkage and Selection Operator (LASSO), Principal Component Analysis (PCA) or Partial Least Squares (PLS). Regression was executed with linear regression (LASSO, PLS regression, PCA regression), Support Vector Machine (SVM) regression, and Multilayer Perceptron (MLP). The best prediction of EO concentration was achieved with the combination of MSC or SNV preprocessing, PLS dimension reduction, and MLP regression (1 hidden layer with 6 nodes), achieving a good prediction with a ratio of performance to deviation (RPD) of 2.84 ± 0.07, an R<sup>2</sup> of prediction of 0.863 ± 0.008, and a RMSE of prediction of 0.219 ± 0.005% EO. These results show that NIR-HSI is a viable method for rapid, nondestructive analysis of EO concentration. Future work should explore the use of NIR in the visible spectrum, the use of HSI for determining EO in other plant materials and the potential of HSI to determine individual compounds in these solid plant/food matrices. Partial least squares regression ; Hyperspectral imaging ; Mentha spicata ; Principal component analysis ; Chemometrics ; Lasso (programming language) ; Pattern recognition (psychology) ; Dimensionality reduction ; Mathematics ; Artificial intelligence ; Linear regression ; Feature selection ; Near-infrared spectroscopy ; Regression ; Computer science ; Statistics ; Essential oil ; Chemistry ; Food science ; Machine learning ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Essential Oils and Antimicrobial Activity
Authors: Martin Wortmann | Michael Westphal | Bernhard Peter Kaltschmidt | Michaela Klöcker | Ashley Stephen Layland | Bennet Brockhagen | Andreas Hütten | Natalie Frese | Andrea Ehrmann
Source: Nanoscale Advances (article, 2023-01-01)
Field: Materials Science — Biomaterials

Nanofibers are a matter of perspective: effects of methodology and subjectivity on diameter measurements Nanofibers are currently among the most researched nanomaterials in materials science. Various high-resolution microscopy techniques are used for morphological investigations, with the diameter as primary characteristic. Since methodological factors influencing the diameter distribution are usually ignored, numerical values can hardly be compared across different or even within single studies. Here, we investigate influencing factors such as microscopy technique, degree of magnification, eventual coatings, and the analysts' bias in the image selection and evaluation. We imaged a single nanofiber sample using scanning electron microscopy (SEM), helium ion microscopy (HIM), atomic force microscopy (AFM), and transmission electron microscopy (TEM). These techniques yield significant methodological variations between the diameter distributions. We further observed a strong influence of analysts' subjectivity, with a consistent average deviation between 4 different analysts of up to 31%. The average deviation between micrographs within each category was 14%, revealing a considerable influence of micrograph selection and strong potential for cherry picking. The mean values were mostly comparable with the results using automated image analysis software, which was more reproducible, much faster, and more accurate for images with lower magnification. The results demonstrate that one of the most frequently measured characteristics of nanofibers is subject to strong systematic fluctuations that are rarely if ever addressed. Magnification ; Microscopy ; Nanofiber ; Materials science ; Subjectivity ; Resolution (logic) ; Scanning electron microscope ; Transmission electron microscopy ; Perspective (graphical) ; Nanotechnology ; Standard deviation ; Statistics ; Optics ; Composite material ; Artificial intelligence ; Computer science ; Physics ; Mathematics Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Surface Modification and Superhydrophobicity
Authors: Jinhui Yang | Bangchu Gong | Xibing Jiang
Source: Frontiers in Forests and Global Change (article, 2023-06-29)
Field: Chemistry — Analytical Chemistry

Hyperspectral imaging-based prediction of soluble sugar content in Chinese chestnuts Soluble sugars are critical determinants of fruit quality and play a significant role in human nutrition. Chestnuts, rich in soluble sugars, derive their sweetness from them. However, their content varies with cultivar, location, and environmental conditions. Traditional methods for determining soluble sugar content in chestnuts are time-consuming, laborious, and destructive. Therefore, there is a pressing need for rapid, non-destructive, and straightforward methods for determining soluble sugars in chestnuts to expedite genetic selection. This study aimed to develop a hyperspectral imaging-based prediction model for soluble sugar content in Chinese chestnuts. Firstly, abnormal samples were eliminated using ensemble partial least squares for outlier detection. We then compared the average original and block scale (BS) spectra, with the latter demonstrating significant differences. The BS pretreatment exhibited two small absorption peaks in the 403.7 ∼ 429.1 nm band and 454.7 ∼ 500 nm band, less fluctuation in the spectral curves from 503.2 to 687.2 nm, and a substantial increase in spectral absorption between 690.6 and 927.8 nm. Subsequently, we developed a partial least squares (PLS) model using BS pretreatment and regularized elimination (rep) variable selection, which showed better accuracy in predicting chestnut soluble sugar content than other variable selection methods. The model fitting accuracy after the spectra treatment was marginally better than that of the original spectra, with a calibration set correlation coefficient (R2) of 0.59 and root mean square error (RMSE) of 1.02, and a validation set R2 of 0.66 and RMSE of 0.94. The wavelengths at 464.3, 503.2, 539.3, 579, and 711.3 nm were identified as critical for developing the soluble sugar content prediction model. The study demonstrated the potential of Near-Infrared Spectroscopy (NIS) as a rapid and non-destructive method for predicting chestnut soluble sugar content, which could be beneficial for quality control and sorting in the food industry. Partial least squares regression ; Hyperspectral imaging ; Sugar ; Mean squared error ; Content (measure theory) ; Near-infrared spectroscopy ; Chemistry ; Sweetness ; Residual ; Mathematics ; Outlier ; Correlation coefficient ; Analytical Chemistry (journal) ; Food science ; Statistics ; Artificial intelligence ; Computer science ; Chromatography ; Algorithm ; Biology Spectroscopy and Chemometric Analyses ; Nuts composition and effects ; Essential Oils and Antimicrobial Activity
Authors: Kusumiyati Kusumiyati | Yonathan Asikin
Source: Open Agriculture (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Machine learning-based prediction of total phenolic and flavonoid in horticultural products Abstract The purpose of this study was to predict the total phenolic content (TPC) and total flavonoid content (TFC) in several horticultural commodities using near-infrared spectroscopy (NIRS) combined with machine learning. Although models are typically developed for a single product, expanding the coverage of the model can improve efficiency. In this study, 700 samples were used, including varieties of shallot, cayenne pepper, and red chili. The results showed that the TPC model developed yielded R 2 cal, root mean squares error in the calibration set, R 2 pred, root mean squares error in prediction set, and ratio of performance to deviation values of 0.79, 123.33, 0.78, 124.20, and 2.13, respectively. Meanwhile, the TFC model produced values of 0.71, 44.52, 0.72, 42.10, and 1.87, respectively. The wavelengths 912, 939, and 942 nm are closely related to phenolic compounds and flavonoids. The accuracy of the model in this study produced satisfactory results. Therefore, the application of NIRS and machine learning to horticultural products has a high potential of replacing conventional laboratory analysis TPC and TFC. Flavonoid ; Mathematics ; Calibration ; Partial least squares regression ; Chemistry ; Mean squared prediction error ; Training set ; Statistics ; Horticulture ; Computer science ; Artificial intelligence ; Biology ; Organic chemistry Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Spectroscopy Techniques in Biomedical and Chemical Research