jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yingqi Shang | Hongquan Zhang | Yan Zhang
Source: Micromachines (article, 2021-01-27)
Field: Materials Science — Materials Chemistry

Research on Sapphire Deep Cavity Corrosion and Mask Selection Technology Aimed at the problem of the small wet etching depth in sapphire microstructure processing technology, a multilayer composite mask layer is proposed. The thickness of the mask layer is studied, combined with the corrosion rate of different materials on sapphire in the sapphire etching solution, different mask layers are selected for the corrosion test on the sapphire sheet, and then the corrosion experiment is carried out. The results show that at 250 °C, the choice is relatively high when PECVD (Plasma Enhanced Chemical Vapor Deposition) is used to make a double-layer composite film of silicon dioxide and silicon nitride. When the temperature rises to 300 °C, the selection ratio of the silicon dioxide layer grown by PECVD is much greater than that of the silicon nitride layer. Therefore, under high temperature conditions, a certain thickness of silicon dioxide can be used as a mask layer for deep cavity corrosion. Materials science ; Corrosion ; Sapphire ; Plasma-enhanced chemical vapor deposition ; Layer (electronics) ; Silicon nitride ; Etching (microfabrication) ; Silicon on sapphire ; Silicon ; Chemical vapor deposition ; Optoelectronics ; Composite material ; Metallurgy ; Optics ; Laser ; Silicon on insulator High voltage insulation and dielectric phenomena ; Building materials and conservation ; Electrohydrodynamics and Fluid Dynamics
Authors: Lingling Zhang | Rui Hao | Hongjun You | Nan Hu | Yanzhu Dai | Jixiang Fang
Source: Research Square (preprint, 2021-01-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultra-rapid and highly efficient enrichment of organic pollutants via magnetic nanoparticles/mesoporous nanosponge compounds for ultrasensitive nanosensors Abstract Developing advanced sensing and detection technologies to effectively monitor organic micropollutants in water is under urgent demand in both scientific and industrial communities. Currently, owing to the ultrahigh sensitivity on the single-molecule level with highly informative spectra characteristics, SERS technique is regarded as the most direct and effective detection technique. However, some weakly adsorbed molecules, such as most of persistent organic pollutants, cannot exhibit strong SERS signals, which is a long-standing key challenge that has not been solved. Here, we show an enrichment-typed sensing strategy based on a powerful porous composite material, call mesoporous nanosponge. The nanosponge consists of magnetic nanoparticles immobilized porous β-cyclodextrin polymers, demonstrating remarkable capability of effective and fast removal of organic micropollutants, e.g. ~90% removal efficiency within ~1 min. With the anchoring of magnetic nanoparticles, the current new polymer adsorbent can be easily recycled from water and re-dispersed in ethanol so that the target molecules in the cavity of adsorbent is concentrated, with an enrichment factor up to ~103. By means of the current enrichment strategy, the limit of detection (LOD) of the typical organic pollutants can be significantly improved, i.e. increasing 2~3 orders of magnitude, compared with the detection without molecule enrichment protocol. Consequently, the current enrichment strategy is proved to be applicable in a variety of fields for portable and fast detection, such as Raman and fluorescent. Nanosensor ; Mesoporous material ; Nanoparticle ; Nanotechnology ; Pollutant ; Chemistry ; Magnetic nanoparticles ; Metal-organic framework ; Materials science ; Adsorption ; Organic chemistry ; Catalysis Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Nanomaterials for catalytic reactions
Authors: Wenyou Qiao | Tao Jin | Peng Li | Mengmeng Xing | Huinan Yang | Jun Chen | Mingxu Su
Source: Spectroscopy (article, 2021-10-01)
Field: Chemistry — Analytical Chemistry

Determining Nitrophenol Isomers Using Raman Spectroscopy Nitrophenol is one of the most highly toxic heavy pollutants in the environment. It has a slow degradation rate, and is harmful to humans even at low concentrations. Therefore, it is crucial to detect nitrophenol quickly, easily, and accurately. In this study, the nitrophenol isomers (o-nitrophenol, m-nitrophenol, and p-nitrophenol), in solid and liquid phases with methanol or acetone as the solvent, were investigated using Raman spectroscopy. The characteristic Raman peaks of nitrophenol isomers were observed. In addition, the intensities of the corresponding Raman peaks for nitrophenol isomers in the liquid phase with methanol or acetone as solvent at different concentrations (0.3%, 0.6%, 1.2%, 1.8%, 2.4%, 3.0%, and 3.6%) were analyzed. It revealed that the intensities increased when the concentrations increased, and the four corresponding linear correlation coefficients between intensities and concentrations were greater than 0.99. Furthermore, the fitted linear relationships were verified by nitrophenol isomers in the liquid phase with methanol or acetone as the solvent at known concentrations (0.9%, 1.5%, 2.1%, 2.7%, and 3.3%), and the average deviations between the measured data and the known values were 4.94%, 2.69%, 4.37%, and 1.86%, respectively. Raman spectroscopy ; Methanol ; Acetone ; Nitrophenol ; Chemistry ; Solvent ; 4-Nitrophenol ; Analytical Chemistry (journal) ; Phase (matter) ; Spectroscopy ; Chromatography ; Organic chemistry ; Catalysis Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Xiaobin Wang | Chunjiang Zhao
Source: Polish Journal of Food and Nutrition Sciences (article, 2021-10-26)
Field: Chemistry — Analytical Chemistry

Non-Destructive Quantitative Analysis of Azodicarbonamide Additives in Wheat Flour by High-Throughput Raman Imaging Azodicarbonamide (ADA) additives are limited or prohibited from being added to wheat flour by various countries because they may produce carcinogenic semicarbazide in humid and hot conditions. This study aimed to realize the non-destructive detection of ADA additives in wheat flour using high-throughput Raman imaging and establish a quantitative analysis model. Raman images of pure wheat flour, pure ADA, and wheat flour-ADA mixed samples were collected respectively, and the average Raman spectra of each sample were calculated. A partial least squares (PLS) model was established by using the linear combination spectra of pure wheat flour and pure ADA and the average Raman spectra of mixed samples. The regression coefficients of the PLS model were used to reconstruct the 3D Raman images of mixed samples into 2D grayscale images. Threshold segmentation was used to classify wheat flour pixels and ADA pixels in grayscale images, and a quantitative analysis model was established based on the number of ADA pixels. The results showed that the minimum detectable content of ADA in wheat flour was 100 mg/kg. There was a good linear relationship between the ADA content in the mixed sample and the number of pixels classified as ADA in the grayscale image in the range of 100 -10,000 mg/kg, and the correlation coefficient was 0.9858. This study indicated that the combination of PLS regression coefficients with threshold segmentation had provided a non-destructive method for quantitative detection of ADA in Raman images of wheat flour-ADA mixed samples. Wheat flour ; Food science ; Chemistry ; Raman spectroscopy Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Isotope Analysis in Ecology
Authors: Yanlong Sun | Xinming Qian | Yangyang Liu | Jianwei Wang | Qunbo Lv | Mengqi Yuan
Source: Remote Sensing (article, 2021-07-02)
Field: Chemistry — Analytical Chemistry

Identification of Typical Solid Hazardous Chemicals Based on Hyperspectral Imaging The identification of hazardous chemicals based on hyperspectral imaging is an important emergent means for the prevention of explosion accidents and the early warning of secondary hazards. In this study, we used a combination of spectral curve matching based on full-waveform characteristics and spectral matching based on spectral characteristics to identify the hazardous chemicals, and proposed a method to quantitatively characterize the matching degree of the spectral curves of hazardous chemicals. The results showed that the four hazardous chemicals, sulfur, red phosphorus, potassium permanganate, and corn starch had bright colors, distinct spectral curve characteristics, and obvious changes in reflectivity, which were easy to identify. Moreover, the matching degree of their spectral curves was positively correlated with their reflectivity. However, the spectral characteristics of carbon powder, strontium nitrate, wheat starch, and magnesium–aluminum alloy powder were not obvious, with no obvious characteristic peaks or trends of change in reflectivity. Except for the reflectivity and the matching degree of the carbon powder being maintained at a low level, the reflectivity of the remaining three samples was relatively close, so that it was difficult to identify with the spectral curves alone, and color information should be considered for further identification. Hyperspectral imaging ; Hazardous waste ; Reflectivity ; Materials science ; Spectral signature ; Environmental science ; Remote sensing ; Mineralogy ; Optics ; Geology Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Keyuan Ding | Tianci Li | Bin Chen | Feng Rao
Source: Frontiers in Nanotechnology (article, 2021-03-26)
Field: Materials Science — Materials Chemistry

Reliable 2D Phase Transitions for Low-Noise and Long-Life Memory Programming Extending cycling endurance and suppressing programming noise of phase-change random-access memory (PCRAM) are the key challenges with respect to the development of nonvolatile working memory and high-accuracy neuromorphic computing devices. However, the large-scale atomic migration along electrical pulse direction in the unconstrained three-dimensional phase transitions of the phase-change materials (PCMs) induces big resistance fluctuations upon repeated programming and renders the classic PCRAM devices into premature failure with limited cycling endurance. Previous efforts of superlattice-like and superlattice PCM schemes cannot effectively resolve such issues. In this work, we demonstrated that, through fine-tuning the sputtering techniques, a phase-change heterostructure (PCH) of Sb 2 Te 3 /TiTe 2 can be successfully constructed. In contrast to its superlattice-like counterpart with inferior crystal quality, the well-textured PCH architecture ensures the reliable (well-confined) two-dimensional phase transitions, promoting an ultralow-noise and long-life operation of the PCRAM devices. Our study thus provides a useful reference for better manufacturing the PCH architecture and further exploring the excellent device performances and other new physics. Superlattice ; Phase-change memory ; Noise (video) ; Materials science ; Computer science ; Neuromorphic engineering ; Optoelectronics ; Electronic engineering ; Nanotechnology ; Engineering ; Artificial intelligence ; Layer (electronics) Phase-change materials and chalcogenides ; Advanced Memory and Neural Computing ; Transition Metal Oxide Nanomaterials
Authors: Huanyang Chen
Source: Frontiers in Materials (article, 2021-05-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advances and Frontiers in Metamaterials In optics, the direction of light propagation is manipulated by the shapes of objects and their refractive indexes, thus achieving optical devices such as convex and concave lenses. With increased understanding of materials, researchers are exploring deeper into the microscopic fields and have focused on microstructures of materials. In condensed matters, crystals are solid-state materials with constituents and atoms arranged periodically in space resulting in a variety of extraordinary properties. When it comes to the classical systems, structures of subwavelength scales also exhibit many novel phenomena, such as negative refraction, invisible cloaking and superresolution imaging, and they are usually analyzed in the fields of wave optics or acoustics. Metamaterial ; Nanotechnology ; Geology ; Materials science ; Optoelectronics Metamaterials and Metasurfaces Applications ; Topological Materials and Phenomena ; Acoustic Wave Phenomena Research
Authors: Yutao Li | Liliang Wang | Hao Yu | Zheng Qian
Source: Sensors (article, 2021-11-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Research of Probability-Based Tunneling Magnetoresistive Sensor Static Hysteresis Model Tunneling magnetoresistive (TMR) sensors have broad application prospects because of their high sensitivity and small volume. However, the inherent hysteresis characteristics of TMR affect its applications in high accuracy scenarios. It is essential to build a model to describe the attributes of hysteresis of TMR accurately. Preisach model is one of the popular models to describe the behavior of inherent hysteresis for TMR, whereas it presents low accuracy in high-order hysteresis reversal curves. Furthermore, the traditional Preisach model has strict congruence constraints, and the amount of data seriously affects the accuracy. This paper proposes a hysteresis model from a probability perspective. This model has the same computational complexity as the classic Preisach model while presenting higher accuracy, especially in high-order hysteresis reversal curves. When measuring a small amount of data, the error of this method is significantly reduced compared with the classical Preisach model. Besides, the proposed model’s congruence in this paper only needs equal vertical chords. Hysteresis ; Magnetoresistance ; Quantum tunnelling ; Sensitivity (control systems) ; Congruence (geometry) ; Magnetic hysteresis ; Statistical physics ; Materials science ; Computer science ; Physics ; Condensed matter physics ; Mathematics ; Electronic engineering ; Engineering ; Quantum mechanics ; Magnetic field ; Magnetization Magnetic Properties and Applications ; Piezoelectric Actuators and Control ; Non-Destructive Testing Techniques
Authors: Maksym Zhelyeznyakov | Steven L. Brunton | Arka Majumdar
Source: Conference on Lasers and Electro-Optics (preprint, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep learning to accelerate Maxwell’s equations for inverse design of dielectric metasurfaces We present a data-driven forward simulation framework for the inverse design of metasurfaces that is more accurate than methods based on the local phase approximation, a factor of 10 4 times faster than mesh based solvers. Inverse ; Lens (geology) ; Inverse problem ; Differentiable function ; Physics ; Optics ; Wavelength ; Focus (optics) ; Dielectric ; Field (mathematics) ; Representation (politics) ; Electromagnetic field ; Computer science ; Mathematical analysis ; Geometry ; Mathematics ; Optoelectronics ; Quantum mechanics Metamaterials and Metasurfaces Applications ; Millimeter-Wave Propagation and Modeling ; Radio Wave Propagation Studies
Authors: Roohi Jan
Source: International Journal for Research in Applied Science and Engineering Technology (article, 2021-05-22)
Field: Chemistry — Analytical Chemistry

Recognition of Dry Fruits using Deep Convolutional Neural Network Deep Convolutional Neural Network (CNN) with a unique structure for combining the feature extraction and classification stages has been considered to be a state-of-the-art computer vision technique for classification tasks. Though there is much technical advancement, Dry fruit recognition is still a difficult task in daily market. The CNN with deep layers has a tremendous achievement in many image classification applications. In this research work, a novel method has been presented for dry fruits recognition. This model was trained and tested on a primary Dry Fruit image dataset containing a total of 800 images belonging to 4 different classes of Dry fruits. The CNN model performs Dry fruit recognition and was able to achieve an overall classification accuracy of 94%. Convolutional neural network ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Lince Rachel Varghese | Vanitha Kandasamy
Source: Journal of ICT Research and Applications (article, 2021-10-19)
Field: Chemistry — Analytical Chemistry

Convolution and Recurrent Hybrid Neural Network for Hevea Yield Prediction Deep learning techniques have been used effectively for rubber crop yield prediction. A hybrid of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) is the best technique for crop yield prediction because it can effectively handle uncertainty of features. Hence, in this paper, a hybrid CNN-RNN method is proposed to forecast Hevea yields based on environmental data in Kerala state, India. The proposed hybrid CNN-RNN method reduces the internal covariate shift of CNN by batch normalization and solves the gradient vanishing or exploding problem of RNN using LSTM with a cell activation mechanism. The proposed method has three essential characteristics: (i) it captures the time dependency of environmental factors and improves the inherent computational time; (ii) it is capable of generalizing the yield prediction under uncertain conditions without loss of prediction accuracy; (iii) combined with the back propagation and feed forward method it can reveal the extent to which samples of weather conditions and soil data conditions are suitable to provide a clear boundary between rubber yield variations. Normalization (sociology) ; Computer science ; Recurrent neural network ; Artificial intelligence ; Convolutional neural network ; Convolution (computer science) ; Hevea ; Deep learning ; Pattern recognition (psychology) ; Yield (engineering) ; Artificial neural network ; Machine learning ; Algorithm ; Natural rubber ; Hevea brasiliensis Spectroscopy and Chemometric Analyses
Authors: Putra Sumari | Wan Muhammad Azimuddin Wan Ahmad | Faris Hadi | Muhammad Mazlan | Nur Anis Liyana | Rotimi-Williams Bello | Ahmad Sufril Azlan Mohamed | Abdullah Zawawi Talib
Source: Revue d intelligence artificielle (article, 2021-10-31)
Field: Chemistry — Organic Chemistry

A Precision Agricultural Application: Manggis Fruit Classification Using Hybrid Deep Learning Fruits come in different variants and subspecies. While some subspecies of fruits can be easily differentiated, others may require an expertness to differentiate them. Although farmers rely on the traditional methods to identify and classify fruit types, the methods are prone to so many challenges. Training a machine to identify and classify fruit types in place of traditional methods can ensure precision fruit classification. By taking advantage of the state-of-the-art image recognition techniques, we approach fruits classification from another perspective by proposing a high performing hybrid deep learning which could ensure precision mangosteen fruit classification. This involves a proposed optimized Convolutional Neural Network (CNN) model compared to other optimized models such as Xception, VGG16, and ResNet50 using Adam, RMSprop, Adagrad, and Stochastic Gradient Descent (SGD) optimizers on specified dense layers and filters numbers. The proposed CNN model has three types of layers that make up its model, they are: 1) the convolutional layers, 2) the pooling layers, and 3) the fully connected (FC) layers. The first convolution layer uses convolution filters with a filter size of 3x3 used for initializing the neural network with some weights prior to updating to a better value for each iteration. The CNN architecture is formed from stacking these layers. Our self-acquired dataset which is composed of four different types of Malaysian mangosteen fruit, namely Manggis Hutan, Manggis Mesta, Manggis Putih and Manggis Ungu was employed for the training and testing of the proposed CNN model. The proposed CNN model achieved 94.99% classification accuracy higher than the optimized Xception model which achieved 90.62% accuracy in the second position. Artificial intelligence ; Computer science ; Initialization ; Convolutional neural network ; Pattern recognition (psychology) ; Convolution (computer science) ; Contextual image classification ; Artificial neural network ; Machine learning ; Image (mathematics) Wood and Agarwood Research ; Smart Agriculture and AI ; Agricultural and Environmental Management
Authors: Cristobal Colón-Ruíz
Source:  (preprint, 2021-12-08)
Field: Materials Science — Materials Chemistry

Semi-Supervised Generative Adversarial Network for Sentiment Analysis of drug reviews &lt;div&gt;Sentiment analysis has become a very popular research topic and covers a wide range of domains such as economy, politics and health. In the pharmaceutical field, automated analysis of online user reviews provides information on the effectiveness and potential side effects of drugs, which could be used to improve pharmacovigilance systems. Deep learning approaches have revolutionized the field of Natural Language Processing (NLP), achieving state-of-the-art results in many tasks, such as sentiment analysis.&lt;/div&gt;&lt;div&gt;These methods require large annotated datasets to train their models. However, in most real-world scenarios, obtaining high-quality labeled datasets is an expensive and time-consuming task. In contrast, unlabeled texts task can be, generally, easily obtained. &lt;/div&gt;&lt;div&gt;In this work, we propose a semi-supervised approach based on a Semi-Supervised Generative Adversarial Network (SSGAN) to address the lack of labeled data for the sentiment analysis of drug reviews, and improve the results provided by supervised approaches in this task.&lt;/div&gt;&lt;div&gt;To evaluate the real contribution of this approach, we present a benchmark comparison between our semi-supervised approach and a supervised approach, which uses a similar architecture but without the generative adversal setting. &lt;/div&gt;&lt;div&gt;Experimental results show better performance of the semi-supervised approach when annotated reviews are less than ten percent of the training set, obtaining a significant improvement for the classification of neutral reviews, the class with least examples. To the best of our knowledge, this is the first study that applies a SSGAN to the sentiment classification of drug reviews. Our semi-supervised approach provides promising results for dealing with the shortage of annotated dataset, but there is still much room to improvement.&lt;/div&gt; Computer science ; Benchmark (surveying) ; Sentiment analysis ; Artificial intelligence ; Task (project management) ; Machine learning ; Generative grammar ; Field (mathematics) ; Set (abstract data type) ; Deep learning ; Supervised learning ; Natural language processing ; Artificial neural network ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods