jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kapil Nichani | Steffen Uhlig | Bertrand Colson | Karina Hettwer | Kirsten Simon | Josephine Bönick | Carsten Uhlig | Sabine Kemmlein | M. Stoyke | Petra Gowik | Gerd Huschek | Harshadrai M. Rawel
Source: Foods (article, 2022-12-27)
Field: Chemistry — Analytical Chemistry

Development of Non-Targeted Mass Spectrometry Method for Distinguishing Spelt and Wheat Food fraud, even when not in the news, is ubiquitous and demands the development of innovative strategies to combat it. A new non-targeted method (NTM) for distinguishing spelt and wheat is described, which aids in food fraud detection and authenticity testing. A highly resolved fingerprint in the form of spectra is obtained for several cultivars of spelt and wheat using liquid chromatography coupled high-resolution mass spectrometry (LC-HRMS). Convolutional neural network (CNN) models are built using a nested cross validation (NCV) approach by appropriately training them using a calibration set comprising duplicate measurements of eleven cultivars of wheat and spelt, each. The results reveal that the CNNs automatically learn patterns and representations to best discriminate tested samples into spelt or wheat. This is further investigated using an external validation set comprising artificially mixed spectra, samples for processed goods (spelt bread and flour), eleven untypical spelt, and six old wheat cultivars. These cultivars were not part of model building. We introduce a metric called the D score to quantitatively evaluate and compare the classification decisions. Our results demonstrate that NTMs based on NCV and CNNs trained using appropriately chosen spectral data can be reliable enough to be used on a wider range of cultivars and their mixes. Cultivar ; Pattern recognition (psychology) ; Convolutional neural network ; Wheat flour ; Artificial intelligence ; Mathematics ; Calibration ; Metric (unit) ; Training set ; Common wheat ; Computer science ; Agronomy ; Food science ; Chemistry ; Biology ; Statistics ; Engineering Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Lídia Kunz Lazzari | Roberta Motta Neves | Andrielen Braz Vanzetto | Ademir J. Zattera | Ruth Marlene Campomanes Santana
Source: Materials Research (article, 2022-01-01)
Field: Materials Science — Biomaterials

Thermal Degradation Kinetics and Lifetime Prediction of Cellulose Biomass Cryogels Reinforced by its Pyrolysis Waste Degradation kinetics is an important tool in order to understand and improve energy conversion and the final application of a material. Cellulose cryogels (CC) are a new class of materials that can be reinforced by several types of particle, including biochar. Apart from it, degradation kinetics and lifetime prediction of biomass cellulose cryogels reinforced by cellulose pyrolysis waste (BC) has been investigated using TG techniques and iso-conversional model free methods. Additionally, the same study was applied to cellulose cryogels reinforced by graphene nanoplatelets (NPG) to compare the behavior of a filler from waste (BC) and a noble filler (NPG). Furthermore, the influence of the addition of the fillers into the cellulose biomass were evaluated in terms of thermal stability and crystallinity. BC and GNP led to higher values of activation energies (Ea) calculated from model-free isoconversional methods and all samples degraded in two-steps. Finally, lifetime prediction was successfully applied and the CC cryogel became more stable over time, maintaining almost 80% of the mass for 1 year exposed at 180 °C. The results of this study shown that only cellulose biomass cryogels are more suitable to produce thermal insulators due to it higher thermal stability. Cellulose ; Pyrolysis ; Biomass (ecology) ; Materials science ; Thermal stability ; Kinetics ; Crystallinity ; Filler (materials) ; Composite material ; Chemical engineering ; Degradation (telecommunications) Advanced Cellulose Research Studies ; Polymer Nanocomposites and Properties ; Natural Fiber Reinforced Composites
Authors: Jordi‐Roger Riba | Rosa Cantero | Rita Puig
Source: Polymers (article, 2022-07-29)
Field: Chemistry — Analytical Chemistry

Classification of Textile Samples Using Data Fusion Combining Near- and Mid-Infrared Spectral Information There is an urgent need to reuse and recycle textile fibers, since today, low recycling rates are achieved. Accurate classification methods for post-consumer textile waste are needed in the short term for a higher circularity in the textile and fashion industries. This paper compares different spectroscopic data from textile samples in order to correctly classify the textile samples. The accurate classification of textile waste results in higher recycling rates and a better quality of the recycled materials. The data fusion of near- and mid-infrared spectra is compared with single-spectrum information. The classification results show that data fusion is a better option, providing more accurate classification results, especially for difficult classification problems where the classes are wide and close to one another. The experimental results presented in this paper prove that the data fusion of near- and mid-infrared spectra is a good option for accurate textile-waste classification, since this approach allows the classification results to be significantly improved. Textile ; Reuse ; Fusion ; Computer science ; Sensor fusion ; Infrared ; Artificial intelligence ; Pattern recognition (psychology) ; Process engineering ; Pulp and paper industry ; Materials science ; Waste management ; Engineering ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Industrial Vision Systems and Defect Detection
Authors: Jiaxing Gao | Zhibin Zhang
Source: IEEE Access (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Study on Deep Learning Model for Online Estimation of Chlorophyll Content Based on Near Ground Multispectral Feature Bands Chlorophyll content of plant leaves is an essential indicator of crop growth status. This study is focused on the nondestructive estimation of the chlorophyll content of maize using near ground multispectral data. We propose a one-dimensional convolutional neural network-gated recurrent unit (1-D-CNN-GRU). That is, it combines a 1D-CNN with strong feature expression capacity and strong memory capacity with the gated recurrent unit (GRU) neural network to estimate of the chlorophyll content of maize directly from multispectral images. Furthermore, the iteratively retaining informative variables-successive projections algorithm (IRIV-SPA) is first used to select the feature wavebands from the 11 available wavebands of two datasets in the experiment. The experimental results show that the selected feature wavebands have more accuracy than the raw wavebands when using the same model; based on these feature wavebands, the 1D-CNN-GRU model has smaller errors than the other conventional models such as support vector regression (SVR) and random forest (RF), with an MRE of 0.069, RMSE of 3.473 on Datasets I, and an MRE of 0.108, RMSE of 7.568 on Datasets II. Meanwhile, the real-time performance is also validated in the experiment. These investigations can provide a valuable guidance for online monitoring chlorophyll content of crops such as maize etc., based on near ground multispectral band data processing for developing smart agricultural system. Multispectral image ; Feature (linguistics) ; Convolutional neural network ; Mean squared error ; Computer science ; Artificial intelligence ; Random forest ; Support vector machine ; Remote sensing ; Pattern recognition (psychology) ; Deep learning ; Feature vector ; Mathematics ; Statistics ; Geography Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Hamed Taheri Gorji | Jo Ann S. Van Kessel | Bradd J. Haley | Kaylee Husarik | Jakeitha Sonnier | Seyed Mojtaba Shahabi | Hossein Kashani Zadeh | Diane E. Chan | Jianwei Qin | Insuck Baek | Moon S. Kim | Alireza Akhbardeh | Mona Sohrabi | Brick Kerge | Nicholas MacKinnon | Fartash Vasefi | Kouhyar Tavakolian
Source: Frontiers in Sensors (article, 2022-09-22)
Field: Chemistry — Analytical Chemistry

Deep learning and multiwavelength fluorescence imaging for cleanliness assessment and disinfection in Food Services Precise, reliable, and speedy contamination detection and disinfection is an ongoing challenge for the food-service industry. Contamination in food-related services can cause foodborne illness, endangering customers and jeopardizing provider reputations. Fluorescence imaging has been shown to be capable of identifying organic residues and biofilms that can host pathogens. We use new fluorescence imaging technology, applying Xception and DeepLabv3+ deep learning algorithms to identify and segment contaminated areas in images of equipment and surfaces. Deep learning models demonstrated a 98.78% accuracy for differentiation between clean and contaminated frames on various surfaces and resulted in an intersection over union (IoU) score of 95.13% for the segmentation of contamination. The portable imaging system’s intrinsic disinfection capability was evaluated on S. enterica, E. coli, and L. monocytogenes , resulting in up to 8-log reductions in under 5 s. Results showed that fluorescence imaging with deep learning algorithms could help assure safety and cleanliness in the food-service industry. Contamination ; Deep learning ; Computer science ; Food safety ; Artificial intelligence ; Fluorescence-lifetime imaging microscopy ; Fluorescence ; Environmental science ; Food science ; Chemistry ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Listeria monocytogenes in Food Safety
Authors: Mees Dieperink | Francesca Scalerandi | Wiebke Albrecht
Source: Nanoscale (review, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Correlating structure, morphology and properties of metal nanostructures by combining single-particle optical spectroscopy and electron microscopy The nanoscale morphology of metal nanostructures directly defines their optical, catalytic and electronic properties and even small morphological changes can cause significant property variations. On the one hand, this dependence allows for precisely tuning and exploring properties by shape engineering; next to advanced synthesis protocols, post-synthesis modification through tailored laser modification has become an emerging tool to do so. On the other hand, with this interconnection also comes the quest for detailed structure-property correlation and understanding of laser-induced reshaping processes on the individual nanostructure level beyond ensemble averages. With the development of single-particle (ultrafast) optical spectroscopy techniques and advanced electron microscopy such understanding can in principle be gained at the femtosecond temporal and atomic spatial scale, respectively. However, accessing <i>both</i> on the <i>same</i> individual nanostructure is far from straightforward as it requires the combination of optical spectroscopy and electron microscopy. In this Minireview, we highlight key studies from recent years that performed such correlative measurements on the same individual metal nanostructure either in a consecutive <i>ex situ</i> manner or <i>in situ</i> inside the electron microscope. We demonstrate that such a detailed correlation is critical for revealing the full picture of the structure-property relationship and the physics behind light-induced nanostructure modifications. We put emphasis on the advantages and disadvantages of each methodology as well as on the unique information that one can gain only by correlative studies performed on the same individual nanostructure and end with an outlook on possible further development of this field in the near future. Electron microscope ; Materials science ; Morphology (biology) ; Spectroscopy ; Particle (ecology) ; Nanostructure ; Metal ; Nanotechnology ; Single particle analysis ; Optical microscope ; Scanning electron microscope ; Microscopy ; Chemical engineering ; Chemical physics ; Chemistry ; Optics ; Physics ; Composite material ; Organic chemistry ; Metallurgy ; Biology Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties ; nanoparticles nucleation surface interactions
Authors: Adrian Ruckhofer | Marco Sacchi | Anthony J. R. Payne | A. P. Jardine | Wolfgang Ernst | Nadav Avidor | Anton Tamtögl
Source: Nanoscale Horizons (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Evolution of ordered nanoporous phases during h-BN growth: controlling the route from gas-phase precursor to 2D material by <i>in situ</i> monitoring Large-area single-crystal monolayers of two-dimensional (2D) materials such as graphene and hexagonal boron nitride (h-BN) can be grown by chemical vapour deposition (CVD). However, the high temperatures and fast timescales at which the conversion from a gas-phase precursor to the 2D material appears, make it extremely challenging to simultaneously follow the atomic arrangements. We utilise helium atom scattering to discover and control the growth of novel 2D h-BN nanoporous phases during the CVD process. We find that prior to the formation of h-BN from the gas-phase precursor, a metastable (3 × 3) structure is formed, and that excess deposition on the resulting 2D h-BN leads to the emergence of a (3 × 4) structure. We illustrate that these nanoporous structures are produced by partial dehydrogenation and polymerisation of the borazine precursor upon adsorption. These steps are largely unexplored during the synthesis of 2D materials and we unveil the rich phases during CVD growth. Our results provide significant foundations for 2D materials engineering in CVD, by adjusting or carefully controlling the growth conditions and thus exploiting these intermediate structures for the synthesis of covalent self-assembled 2D networks. Nanoporous ; Borazine ; Materials science ; Chemical vapor deposition ; Boron nitride ; Graphene ; Monolayer ; Chemical engineering ; Metastability ; Dehydrogenation ; Nanotechnology ; Phase (matter) ; Crystal growth ; Crystallography ; Catalysis ; Chemistry ; Organic chemistry Graphene research and applications ; 2D Materials and Applications ; Thermal properties of materials
Authors: Guowei Yu | Benxue Ma | Huihui Li | Yating Hu | Yujie Li
Source: Foods (article, 2022-12-01)
Field: Chemistry — Analytical Chemistry

Discrimination of Pesticide Residue Levels on the Hami Melon Surface Using Multiscale Convolution Pesticide residues directly or indirectly threaten the health of humans and animals. We need a rapid and nondestructive method for the safety evaluation of fruits. In this study, the feasibility of visible/near-infrared (Vis/NIR) spectroscopy technology was explored for the discrimination of pesticide residue levels on the Hami melon surface. The one-dimensional convolutional neural network (1D-CNN) model was proposed for spectral data discrimination. We compared the effect of different convolutional architectures on the model performance, including single-depth, symmetric, and asymmetric multiscale convolution. The results showed that the 1D-CNN model could discriminate the presence or absence of pesticide residues with a high accuracy above 99.00%. The multiscale convolution could significantly improve the model accuracy while reducing the modeling time. In particular, the asymmetric convolution had a better comprehensive performance. For two-level discrimination, the accuracy of lambda-cyhalothrin and beta-cypermethrin was 93.68% and 95.79%, respectively. For three-level discrimination, the accuracy of lambda-cyhalothrin and beta-cypermethrin was 86.32% and 89.47%, respectively. For four-level discrimination, the accuracy of lambda-cyhalothrin and beta-cypermethrin was 87.37% and 93.68%, respectively, and the average modeling time was 3.5 s. This finding will encourage more relevant research to use multiscale 1D-CNN as a spectral analysis strategy for the detection of pesticide residues in fruits. Convolutional neural network ; Convolution (computer science) ; Pesticide ; Artificial intelligence ; Residue (chemistry) ; Pattern recognition (psychology) ; Biological system ; Pesticide residue ; Mathematics ; Environmental science ; Computer science ; Chemistry ; Biology ; Artificial neural network ; Agronomy ; Biochemistry Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Smart Agriculture and AI
Authors: Xiaobo Meng | Gongmao Peng | Kang Niu | Xiaogang Wang | Hongwei Mei | Liming Wang
Source: Polymers (article, 2022-06-21)
Field: Materials Science — Materials Chemistry

Characteristics of Small-Molecule Migration of Silicone Rubber Insulator in Electrical Power Systems The migration of low-molecular-weight components of polysiloxane (small molecules) to the surface of high-temperature-vulcanizing silicone rubber (HTV-SR) ensures its hydrophobicity and tends to coat the surface of pollutants, which would otherwise lower hydrophobicity. The transferability of hydrophobicity will ensure the insulator maintains its higher hydrophobicity after being coated with surface pollutants, thus providing the insulator with higher pollution flashover voltage. This migration process takes a certain time, and in this paper, the time characteristics of hydrophobicity transfer from HTV-SR coated with ten different inert materials were investigated. Ten different inert materials have different migration times and static contact angles, possibly due to the influence of pollution material characteristics on the characteristics of small-molecule migration. Thermogravimetric analysis (TG), Fourier transform infrared spectroscopy (FTIR), and gas chromatography-mass spectrometry (GC-MS) were used to analyze the migration of small molecules to the polluted surface. The evidence of small molecules migrating to the surface of the polluted material over time was found. Furthermore, the influence of pollution material characteristics on small-molecule migration was analyzed via SEM, specific surface area, and porosity. On that basis, the hydrophobicity migration characteristics of mixtures of kaolin and kieselguhr were also studied and compared to determine how best to simulate the behavior of natural pollution using artificial pollutants and their mixtures. Silicone rubber ; Thermogravimetric analysis ; Vulcanization ; Materials science ; Chemical engineering ; Porosity ; Silicone ; Contact angle ; Fourier transform infrared spectroscopy ; Molecule ; Inert ; Composite material ; Insulator (electricity) ; Inert gas ; Pollutant ; Natural rubber ; Chemistry ; Organic chemistry High voltage insulation and dielectric phenomena ; Surface Modification and Superhydrophobicity ; Aerosol Filtration and Electrostatic Precipitation
Authors: Qinghua Fu | Xiaomei Liu | Yan Li | Peng Wang | Wu Tian | Haihan Xiao | Yameng Zhao | Qichao Liao | Ziyi Song
Source: Molecules (article, 2022-07-30)
Field: Chemistry — Organic Chemistry

Discovery of New Inhibitors of eEF2K from Traditional Chinese Medicine Based on In Silico Screening and In Vitro Experimental Validation Eukaryotic elongation factor 2 kinase (eEF2K) is a highly conserved α kinase and is increasingly considered as an attractive therapeutic target for cancer as well as other diseases. However, so far, no selective and potent inhibitors of eEF2K have been identified. In this study, pharmacophore screening, homology modeling, and molecular docking methods were adopted to screen novel inhibitor hits of eEF2K from the traditional Chinese medicine database (TCMD), and then cytotoxicity assay and western blotting were performed to verify the validity of the screen. Resultantly, after two steps of screening, a total of 1077 chemicals were obtained as inhibitor hits for eEF2K from all 23,034 compounds in TCMD. Then, to verify the validity, the top 10 purchasable chemicals were further analyzed. Afterward, Oleuropein and Rhoifolin, two reported antitumor chemicals, were found to have low cytotoxicity but potent inhibitory effects on eEF2K activity. Finally, molecular dynamics simulation, pharmacokinetic and toxicological analyses were conducted to evaluate the property and potential of Oleuropein and Rhoifolin to be drugs. Together, by integrating in silico screening and in vitro biochemical studies, Oleuropein and Rhoifolin were revealed as novel eEF2K inhibitors, which will shed new lights for eEF2K-targeting drug development and anticancer therapy. In silico ; Pharmacophore ; Virtual screening ; Oleuropein ; Computational biology ; Pharmacology ; Cytotoxicity ; ADME ; Docking (animal) ; In vitro ; Chemistry ; Drug ; Biology ; Medicine ; Biochemistry Synthesis and biological activity ; Cancer therapeutics and mechanisms ; Bioactive Compounds and Antitumor Agents
Authors: Zeyu Zhao | Jie You | Jun Zhang | Shiyin Du | Zilong Tao | Yuhua Tang | Tian Jiang
Source: Nanophotonics (article, 2022-09-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data enhanced iterative few‐sample learning algorithm‐based inverse design of 2D programmable chiral metamaterials A data enhanced iterative few-sample (DEIFS) algorithm is proposed to achieve the accurate and efficient inverse design of multi-shaped 2D chiral metamaterials. Specifically, three categories of 2D diffractive chiral structures with different geometrical parameters, including widths, separation spaces, bridge lengths, and gold lengths are studied utilising both the conventional rigorous coupled wave analysis (RCWA) approach and DEIFS algorithm, with the former approach assisting the training process for the latter. The DEIFS algorithm can be divided into two main stages, namely data enhancement and iterations. Firstly, some "pseudo data" are generated by a forward prediction network that can efficiently predict the circular dichroism (CD) response of 2D diffractive chiral metamaterials to reinforce the dataset after necessary denoising. Then, the algorithm uses the CD spectra and the predictions of parameters with smaller errors iteratively to achieve accurate values of the remaining parameters. Meanwhile, according to the impact of geometric parameters on the chiroptical response, a new functionality is added to interpret the experimental results of DEIFS algorithm from the perspective of data, improving the interpretability of the DEIFS. In this way, the DEIFS algorithm replaces the time-consuming iterative optimization process with a faster and simpler approach that achieves accurate inverse design with dataset whose amount is at least one to two orders of magnitude less than most previous deep learning methods, reducing the dependence on simulated spectra. Furthermore, the fast inverse design of multiple shaped metamaterials allows for different light manipulation, demonstrating excellent potentials in applications of optical coding and information processing. This work belongs to one of the first attempts to thoroughly characterize the flexibility, interpretability, and generalization ability of DEIFS algorithm in studying various chiroptical effects in metamaterials and accelerating the inverse design of hypersensitive photonic devices. Metamaterial ; Inverse ; Nanomaterials ; Materials science ; Iterative learning control ; Computer science ; Iterative method ; Iterative design ; Sample (material) ; Nanotechnology ; Algorithm ; Optoelectronics ; Artificial intelligence ; Mathematics ; Physics ; Composite material Metamaterials and Metasurfaces Applications ; Machine Learning and ELM ; Advanced Memory and Neural Computing