jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohammad Rahimi | Mohammad Hossein Abbaspour‐Fard | Abbas Rohani
Source: Renewable Energy (article, 2021-09-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A multi-data-driven procedure towards a comprehensive understanding of the activated carbon electrodes performance (using for supercapacitor) employing ANN technique Supercapacitor ; Capacitance ; Electrode ; Activated carbon ; Materials science ; Electrolyte ; Carbon fibers ; Microporous material ; Artificial neural network ; Biomass (ecology) ; Capacitor ; Computer science ; Biological system ; Chemical engineering ; Process engineering ; Chemistry ; Electrical engineering ; Composite material ; Machine learning ; Engineering Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Electrochemical sensors and biosensors
Authors: Russlan Jaafreh | Yoo Seong Kang | Kotiba Hamad
Source: ACS Applied Materials & Interfaces (article, 2021-11-22)
Field: Materials Science — Materials Chemistry

Lattice Thermal Conductivity: An Accelerated Discovery Guided by Machine Learning In the present work, we used machine learning (ML) techniques to build a crystal-based model that can predict the lattice thermal conductivity (LTC) of crystalline materials. To achieve this, first, LTCs of 119 compounds at various temperatures (100-1000 K) were obtained based on density functional theory (DFT) and phonon calculations, and then, these data were employed in the next learning process to build a predictive model using various ML algorithms. The ML results showed that the model built based on the random forest (RF) algorithm with an <i>R</i><sup>2</sup> score of 0.957 was the most accurate compared with the models built using other algorithms. Additionally, the accuracy of this model was validated using new cases of four compounds, which was not seen for the model before, where a good matching between calculated and predicted LTCs of the new compounds was found. To find candidates with ultralow LTCs (<1 W m<sup>-1</sup> K<sup>-1</sup>) at room temperature, the model was used to screen compounds (32116) in the Inorganic Crystal Structure Database. From the screened compounds, Cs<sub>2</sub>SnI<sub>6</sub> and SrS were selected to validate the ML prediction using the counterpart theoretical calculations (DFT and phonon), and it was found that the outcome behaviors by both methods (ML prediction and DFT/phonon calculations) are fairly consistent. Considering the type of employed feature, the prime novelty in this work is that the built model can credibly predict the LTC-temperature behaviors of new compounds that are constructed based on prototype structures and chemical compositions, without the use of any DFT-relaxed structure parameters. Accordingly, using the periodic table, prototype structures, and the RF-based model, the LTC-temperature behavior of a huge number of compounds can be predicated. Materials science ; Phonon ; Lattice (music) ; Machine learning ; Thermal conductivity ; Algorithm ; Crystal structure ; Random forest ; Crystal structure prediction ; Density functional theory ; Artificial intelligence ; Computer science ; Statistical physics ; Condensed matter physics ; Computational chemistry ; Physics ; Crystallography ; Chemistry Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Inorganic Chemistry and Materials
Authors: Zhiyun Chen | Jingyong Liu | Huashan Chen | Ziyi Ding | Xiaojie Tang | Fatih Evrendilek
Source: Renewable Energy (article, 2021-10-11)
Field: Materials Science — Materials Chemistry

Oxy-fuel and air atmosphere combustions of Chinese medicine residues: Performances, mechanisms, flue gas emission, and ash properties Atmosphere (unit) ; Flue gas ; Combustion ; Oxy-fuel ; Chemistry ; Reducing atmosphere ; Environmental chemistry ; Waste management ; Meteorology ; Organic chemistry Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes ; Petroleum Processing and Analysis
Authors: Shengli Jiang | Zhuo Xu | Medhavi Kamran | Stas Zinchik | Sidike Paheding | Armando G. McDonald | Ezra Bar‐Ziv | Ví­ctor M. Zavala
Source: Computers & Chemical Engineering (article, 2021-09-17)
Field: Chemistry — Analytical Chemistry

Using ATR-FTIR spectra and convolutional neural networks for characterizing mixed plastic waste Representation (politics) ; Convolutional neural network ; Pattern recognition (psychology) ; Fourier transform infrared spectroscopy ; Fourier transform ; Spectral line ; Matrix (chemical analysis) ; Artificial intelligence ; Attenuated total reflection ; Wavenumber ; Computer science ; Gramian matrix ; Spectroscopy ; Characterization (materials science) ; Infrared spectroscopy ; Biological system ; Analytical Chemistry (journal) ; Materials science ; Mathematics ; Chemistry ; Optics ; Physics ; Composite material ; Chromatography ; Nanotechnology ; Organic chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Mineral Processing and Grinding
Authors: Changwook Jeong | Sanghoon Myung | In Young Huh | Byung‐Seon Choi | Jinwoo Kim | Hyunjae Jang | Hojoon Lee | Daeyoung Park | Kyuhun Lee | Wonik Jang | Jisu Ryu | Moon-Hyun Cha | Jae Myung Choe | Mun‐Bo Shim | Dae Sin Kim
Source: IEEE Transactions on Electron Devices (article, 2021-08-24)
Field: Materials Science — Materials Chemistry

Bridging TCAD and AI: Its Application to Semiconductor Design There is a growing consensus that the physics-based model needs to be coupled with machine learning (ML) model relying on data or vice versa in order to fully exploit their combined strengths to address scientific or engineering problems that cannot be solved separately. We propose several methodologies of bridging technology computer-aided design (TCAD) simulation and artificial intelligence (AI) with its application to the tasks for which traditional TCAD faces challenges in terms of simulation runtime, coverage, and so on. AI-emulator that learns fine-grained information from rigorous TCAD enables simulation of process technologies and device in real-time as well as large-scale simulation such as full-pattern analysis of stress without high demand on computational resource. To accelerate atomistic molecular dynamics (MD) simulation, we have done a comparison study of descriptor-based and graph-based neural net potential, and also show their capability with large-scale and long-time simulation of silicon oxidation. Finally, we discuss the use of hybrid modeling of AI- and physics-based model for the case where physical equations are either fully or partially unknown. Exploit ; Bridging (networking) ; Computer science ; Artificial neural network ; Computer engineering ; Artificial intelligence ; Computational science Machine Learning in Materials Science ; Semiconductor materials and devices ; Advancements in Semiconductor Devices and Circuit Design
Authors: A.E. Kozhukhova | S.P. du Preez | Dmitri Bessarabov
Source: Energies (review, 2021-08-11)
Field: Materials Science — Materials Chemistry

Catalytic Hydrogen Combustion for Domestic and Safety Applications: A Critical Review of Catalyst Materials and Technologies Spatial heating and cooking account for a significant fraction of global domestic energy consumption. It is therefore likely that hydrogen combustion will form part of a hydrogen-based energy economy. Catalytic hydrogen combustion (CHC) is considered a promising technology for this purpose. CHC is an exothermic reaction, with water as the only by-product. Compared to direct flame-based hydrogen combustion, CHC is relatively safe as it foregoes COx, CH4, and under certain conditions NOx formation. More so, the risk of blow-off (flame extinguished due to the high fuel flow speed required for H2 combustion) is adverted. CHC is, however, perplexed by the occurrence of hotspots, which are defined as areas where the localized surface temperature is higher than the average surface temperature over the catalyst surface. Hotspots may result in hydrogen’s autoignition and accelerated catalyst degradation. In this review, catalyst materials along with the hydrogen technologies investigated for CHC applications were discussed. We showed that although significant research has been dedicated to CHC, relatively limited commercial applications have been identified up to date. We further showed the effect of catalyst support selection on the performance and durability of CHC catalysts, as well as a holistic summary of existing catalysts used for various CHC applications and catalytic burners. Lastly, the relevance of CHC applications for safety purposes was demonstrated. Combustion ; Catalysis ; Hydrogen ; Catalytic combustion ; Exothermic reaction ; Hydrogen fuel ; Chemical engineering ; Materials science ; Waste management ; Chemistry ; Engineering ; Organic chemistry Catalytic Processes in Materials Science ; Combustion and Detonation Processes ; Advanced Combustion Engine Technologies
Authors: Luigi Di Costanzo | Barbara Panunzi
Source: Molecules (review, 2021-05-16)
Field: Chemistry — Spectroscopy

Visual pH Sensors: From a Chemical Perspective to New Bioengineered Materials Many human activities and cellular functions depend upon precise pH values, and pH monitoring is considered a fundamental task. Colorimetric and fluorescence sensors for pH measurements are chemical and biochemical tools able to sense protons and produce a visible signal. These pH sensors are gaining widespread attention as non-destructive tools, visible to the human eye, that are capable of a real-time and in-situ response. Optical "visual" sensors are expanding researchers' interests in many chemical contexts and are routinely used for biological, environmental, and medical applications. In this review we provide an overview of trending colorimetric, fluorescent, or dual-mode responsive visual pH sensors. These sensors include molecular synthetic organic sensors, metal organic frameworks (MOF), engineered sensing nanomaterials, and bioengineered sensors. We review different typological chemical entities of visual pH sensors, three-dimensional structures, and signaling mechanisms for pH sensing and applications; developed in the past five years. The progression of this review from simple organic molecules to biological macromolecules seeks to benefit beginners and scientists embarking on a project of pH sensing development, who needs background information and a quick update on advances in the field. Lessons learned from these tools will aid pH determination projects and provide new ways of thinking for cell bioimaging or other cutting-edge in vivo applications. Nanotechnology ; Computer science ; Biochemical engineering ; Data science ; Human–computer interaction ; Materials science ; Engineering Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials ; Nanoplatforms for cancer theranostics
Authors: Ming‐Xiao Zhu | Jianyi Xue | Yanhui Wei | Guochang Li | Guanjun Zhang
Source: High Voltage (article, 2021-03-31)
Field: Materials Science — Materials Chemistry

Review of interface tailoring techniques and applications to improve insulation performance Abstract Interfaces between different materials, for instance, electrode–dielectrics and vacuum/air/SF 6 ‐insulators and oilpaper, are universal in high‐voltage insulation systems. Targeted tailoring of the interfacial properties, e.g. chemical structures, micro‐/nanoscale morphology, the charge injection barrier, trap states, and surface conductivity, is thought to be an effective approach to improve insulation properties such as breakdown and flashover strength, corona/tracking resistance, and wettability. In this article, the authors review recent progress in interface tailoring methods and their application to improve various insulation properties. The potential application scenarios of interface tailoring in different facilities are first summarised, and the desired insulation properties of various applications are highlighted. Interface tailoring methods are classified as physical/chemical surface modification and surface coating (inorganic, organic and composite), and the features of different techniques are described in detail. The structure–property relationships between interfacial states and various microscopic parameters such as charge injection barrier, trap distribution and surface conductivity are discussed together with the tailoring mechanisms for different insulation properties. Finally, the future development prospects of interface tailoring methods and their applications, e.g. multifunctional coating and stimuli–response smart coating, are presented. Materials science ; Coating ; Arc flash ; Wetting ; Nanotechnology ; Dielectric ; Interface (matter) ; Conductivity ; Composite material ; Insulator (electricity) ; Engineering physics ; Optoelectronics ; Engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Semiconductor materials and devices
Authors: Xiaohua Zhang | Bo Pang | Yaxin Liu | Shaoyu Liu | Peng Xu | Yan Li | Yifan Liu | Leijie Qi | Qing Xie
Source: Energies (article, 2021-11-17)
Field: Materials Science — Materials Chemistry

Review on Detection and Analysis of Partial Discharge along Power Cables Partial discharge (PD) detection and analysis plays a crucial role for acceptance testing and condition monitoring of power cables. Various aspects are related to PD in power cables from theory to practice. This paper first summarizes the PD mechanism and models used for PD analysis in power cables. Afterwards, PD detection is addressed in the aspects of off-line test, on-line test, and sensors. PD analysis is discussed in detail. Specifically, related quantities and algorithms for PD analysis are outlined. PD characteristics with affecting factors, e.g., dielectric type, load, and applied voltage are discussed. Experience on PD development trend with measurements in field is analyzed. Based on the comprehensive review, challenges of PD detection and analysis along a power cable are proposed. Partial discharge ; Power (physics) ; Power cable ; Field (mathematics) ; Power analysis ; Line (geometry) ; Electrical engineering ; Voltage ; Engineering ; Computer science ; Reliability engineering ; Electronic engineering ; Materials science ; Physics ; Nanotechnology ; Mathematics High voltage insulation and dielectric phenomena ; Lightning and Electromagnetic Phenomena ; Advanced Fiber Optic Sensors
Authors: N. Sowmya | Vijayakumar Ponnusamy
Source: IEEE Access (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Development of Spectroscopic Sensor System for an IoT Application of Adulteration Identification on Milk Using Machine Learning Adulteration in milk is a common scenario for gaining extra profit, which may cause severe harmful effects on humans. The qualitative spectroscopic technique provides a better solution for detecting the toxic contents of milk and foodstuffs. All the available spectroscopic methods for milk adulterant detection are based on laboratory-based with costly equipment. This laboratory-based detection takes a long time and is more expensive, which may not be afforded by a common man. To overcome this issue, this research work involves the design and development of a low-cost, portable, multispectral, AI-based, non-destructive spectroscopic sensor system that can be used to detect the milk adulterant in real-time. The designed sensor system uses the spectroscopic method with wavelength ranges from (410-940nm) which consists of three different bands Ultraviolet (UV), visible, and Infra-Red(IR) spectrum to improve the accuracy of detection. The sensor system is connected to the internet via the developed IoT application module, which displays the detected adulterant results in a dedicated web page designed for this purpose. This IoT application enables the adulterant detected results published on the internet immediately with location information for bringing transparency. Adulterant detection problem is formulated as a classification problem and solved by machine learning algorithms of a decision tree, Naive Bayes, linear discriminant analysis, support vector machine and neural network model. The average accuracy of linear discriminant analysis, support vector machine, Naive Bayes, decision tree and neural network model are obtained as 88.1%, 90%, 90%, 91.7% and 92.7% respectively. Genetic algorithm framework is formulated for hyperparameter tuning of neural network model which improved the accuracy from 92.7% to 100%. The model is trained for five different classes of four adulterants, namely Sodium Salicylate, Dextrose, Hydrogen Peroxide, Ammonium Sulphate, and one pure milk sample. Adulterant ; Computer science ; Artificial intelligence ; Machine learning ; Naive Bayes classifier ; Linear discriminant analysis ; Decision tree ; Support vector machine ; Artificial neural network ; Identification (biology) Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Wan Wang | Julio Iglesias | Yabin Jin | Bahram Djafari‐Rouhani | Abdelkrim Khelif
Source: APL Materials (article, 2021-05-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Experimental realization of a pillared metasurface for flexural wave focusing A metasurface is an array of subwavelength units with modulated wave responses that show great potential for the control of refractive/reflective properties in compact functional devices. In this work, we propose an elastic metasurface consisting of a line of pillars with gradient heights, erected on a homogeneous plate. The change in the resonant frequencies associated with the height gradient allows us to achieve transmitted phase response covering a range of 2π, while the amplitude response remains at a relatively high level. We employ the pillared units to design a focusing metasurface and compare the properties of the focal spots through simulation and experiment. The subwavelength transverse and lateral full width at half maximum of the focusing intensity profiles are observed in both simulation and experiment, with the underlying mechanism being the interference and diffraction of the scattered waves from the resonant pillars as well as the boundaries (especially for experiment). The good correspondence between the experimental and simulated relative focal lengths shows the robustness of the focusing pillared metasurfaces with respect to fabrication imperfections. This proposed compact, simple, and robust metasurface with unaffected mechanical properties provides a new platform for elastic wave manipulation for energy harvesting, wave communication, sensing, and non-destructive testing among others. Diffraction ; Materials science ; Optics ; Fabrication ; Amplitude ; Transverse plane ; Realization (probability) ; Phase (matter) ; Robustness (evolution) ; Physics Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Antenna Design and Analysis
Authors: Ning Gu | Zuoheng Zhang | Yan Li
Source: Nano Research (article, 2021-06-18)
Field: Materials Science — Biomaterials

Adaptive iron-based magnetic nanomaterials of high performance for biomedical applications With unique physicochemical properties and biological effects, magnetic nanomaterials (MNMs) play a crucial role in the biomedical field. In particular, magnetic iron oxide nanoparticles (MIONPs) are approved by the United States Food and Drug Administration (FDA) for clinical applications at present due to their low toxicity, biocompatibility, and biodegradability. Despite the unarguable effectiveness, massive space for improving such materials' performance still needs to be filled. Recently, many efforts have been devoted to improving the preparation methods based on the materials' biosafety. Besides, researchers have successfully regulated the performance of magnetic nanoparticles (MNPs) by changing their sizes, morphologies, compositions; or by aggregating as-synthesized MNPs in an orderly arrangement to meet various clinical requirements. The rise of cloud computing and artificial intelligence techniques provides novel ways for fast material characterization, automated data analysis, and mechanism demonstration. In this review, we summarized the studies that focused on the preparation routes and performance regulations of high-quality MNPs, and their special properties applied in biomedical detection, diagnosis, and treatment. At the same time, the future development of MNMs was also discussed. Nanomaterials ; Nanotechnology ; Materials science ; Magnetic nanoparticles ; Nanoparticle Nanoparticle-Based Drug Delivery ; Characterization and Applications of Magnetic Nanoparticles ; Advanced Nanomaterials in Catalysis
Authors: Nan Qin | Zhi‐Gang Qian | Chengzhe Zhou | Xiao‐Xia Xia | Tiger H. Tao
Source: Nature Communications (article, 2021-08-26)
Field: Materials Science — Biomaterials

3D electron-beam writing at sub-15 nm resolution using spider silk as a resist Electron beam lithography (EBL) is renowned to provide fabrication resolution in the deep nanometer scale. One major limitation of current EBL techniques is their incapability of arbitrary 3d nanofabrication. Resolution, structure integrity and functionalization are among the most important factors. Here we report all-aqueous-based, high-fidelity manufacturing of functional, arbitrary 3d nanostructures at a resolution of sub-15 nm using our developed voltage-regulated 3d EBL. Creating arbitrary 3d structures of high resolution and high strength at nanoscale is enabled by genetically engineering recombinant spider silk proteins as the resist. The ability to quantitatively define structural transitions with energetic electrons at different depths within the 3d protein matrix enables polymorphic spider silk proteins to be shaped approaching the molecular level. Furthermore, genetic or mesoscopic modification of spider silk proteins provides the opportunity to embed and stabilize physiochemical and/or biological functions within as-fabricated 3d nanostructures. Our approach empowers the rapid and flexible fabrication of heterogeneously functionalized and hierarchically structured 3d nanocomponents and nanodevices, offering opportunities in biomimetics, therapeutic devices and nanoscale robotics. Nanotechnology ; SILK ; Nanolithography ; Resist ; Electron-beam lithography ; Materials science ; Nanoscopic scale ; Lithography ; Fabrication ; Nanostructure ; Spider silk ; DNA origami ; Mesoscopic physics ; Biomimetics ; Optoelectronics ; Physics Silk-based biomaterials and applications ; Advanced Materials and Mechanics ; Surface Modification and Superhydrophobicity
Authors: Chin Hong Ooi | Raja Vadivelu | Jing Jin | Kamalalayam Rajan Sreejith | Pradip Singha | Nhat‐Khuong Nguyen | Nam‐Trung Nguyen | Nam-Trung Nguyen
Source: Lab on a Chip (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Liquid marble-based digital microfluidics – fundamentals and applications Liquid marbles are droplets with volume typically on the order of microliters coated with hydrophobic powder. Their versatility, ease of use and low cost make liquid marbles an attractive platform for digital microfluidics. This paper provides the state of the art of discoveries in the physics of liquid marbles and their practical applications. The paper first discusses the fundamental properties of liquid marbles, followed by the summary of different techniques for the synthesis of liquid marbles. Next, manipulation techniques for handling liquid marbles are discussed. Applications of liquid marbles are categorised according to their use as chemical and biological reactors. The paper concludes with perspectives on the future development of liquid marble-based digital microfluidics. Microfluidics ; Digital microfluidics ; Nanotechnology ; Engineering ; Materials science ; Electrical engineering ; Electrowetting Pickering emulsions and particle stabilization ; Botanical Research and Applications ; Proteins in Food Systems