jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Barathan Jeevaretanam | Mostafa Abuseada | Chuyu Wei | Nicolas Minesi | Timothy S. Fisher | R. Mitchell Spearrin
Source: Applications in Energy and Combustion Science (article, 2023-11-10)
Field: Chemistry — Spectroscopy

Transient analysis of solar pyrolysis and hydrogen yield via interband cascade laser absorption spectroscopy of methane, acetylene, ethylene, and ethane A mid-infrared laser absorption sensing method has been developed to measure species concentrations of four hydrocarbons and gas temperature over a range of temperatures in mixture compositions relevant to the pyrolytic decomposition of natural gas. The four measured species (methane, acetylene, ethylene, and ethane) are the most abundant hydrocarbons during the pyrolysis of natural gas, and provide a means to monitor decomposition progress and hydrogen yield through molar balance. In this work, time-division multiplexed signals of three distributed-feedback interband cascade lasers are used to make simultaneous measurements of select C-H stretch rovibrational transitions of the target hydrocarbons in the 3.00–3.34 μm range. The sensor was validated over a range of temperatures and pressures (300–1000 K, 0.03–1 atm) at relevant mixture compositions, with correction methods developed to mitigate cross-species interference. The sensor was demonstrated on a solar-thermal pyrolysis reactor, where time-resolved measurements of species mole fractions were performed across a range of insolation conditions to capture the transient response of the reactor. Methane ; Acetylene ; Analytical Chemistry (journal) ; Hydrogen ; Pyrolysis ; Materials science ; Absorption (acoustics) ; Natural gas ; Chemistry ; Organic chemistry Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Atmospheric Ozone and Climate
Authors: Iyll‐Joon Doh | Diana Vanessa Sarria Zuniga | Sungho Shin | Robert E. Pruitt | Bartek Rajwa | J. Paul Robinson | Euiwon Bae
Source: Sensors (article, 2023-03-27)
Field: Chemistry — Analytical Chemistry

Bacterial Colony Phenotyping with Hyperspectral Elastic Light Scattering Patterns The elastic light-scatter (ELS) technique, which detects and discriminates microbial organisms based on the light-scatter pattern of their colonies, has demonstrated excellent classification accuracy in pathogen screening tasks. The implementation of the multispectral approach has brought further advantages and motivated the design and validation of a hyperspectral elastic light-scatter phenotyping instrument (HESPI). The newly developed instrument consists of a supercontinuum (SC) laser and an acousto-optic tunable filter (AOTF). The use of these two components provided a broad spectrum of excitation light and a rapid selection of the wavelength of interest, which enables the collection of multiple spectral patterns for each colony instead of relying on single band analysis. The performance was validated by classifying microflora of green-leafed vegetables using the hyperspectral ELS patterns of the bacterial colonies. The accuracy ranged from 88.7% to 93.2% when the classification was performed with the scattering pattern created at a wavelength within the 473-709 nm region. When all of the hyperspectral ELS patterns were used, owing to the vastly increased size of the data, feature reduction and selection algorithms were utilized to enhance the robustness and ultimately lessen the complexity of the data collection. A new classification model with the feature reduction process improved the overall classification rate to 95.9%. Hyperspectral imaging ; Multispectral image ; Supercontinuum ; Feature selection ; Artificial intelligence ; Robustness (evolution) ; Light scattering ; Wavelength ; Remote sensing ; Computer science ; Pattern recognition (psychology) ; Optics ; Materials science ; Biological system ; Scattering ; Biology ; Physics ; Geology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Cell Image Analysis Techniques
Authors: Yiting Luo | Muaoer Fang | Hanqing Wang | Xiangrong Dai | Rongkui Su | Xiancheng Ma
Source: Nanomaterials (article, 2023-09-15)
Field: Materials Science — Materials Chemistry

Revealing the Adsorption Mechanisms of Methanol on Lithium-Doped Porous Carbon through Experimental and Theoretical Calculations Previous reports have shown that it is difficult to improve the methanol adsorption performance of nitrogen and oxygen groups due to their low polarity. Here, we first prepared porous carbon with a high specific surface area and large pore volume using benzimidazole as a carbon precursor and KOH as an activating agent. Then, we improved the surface polarity of the porous carbon by doping with Lithium (Li) to enhance the methanol adsorption performance. The results showed that the methanol adsorption capacity of Li-doped porous carbon reached 35.4 mmol g<sup>-1</sup>, which increased by 57% compared to undoped porous carbon. Molecular simulation results showed that Li doping not only improved the methanol adsorption performance at low pressure, but also at relatively high pressure. This is mainly because Li-modified porous carbon has higher surface polarity than nitrogen and oxygen-modified surfaces, which can generate stronger electrostatic interactions. Furthermore, through density functional theory (DFT) calculations, we determined the adsorption energy, adsorption distance, and charge transfer between Li atom and methanol. Our results demonstrate that Li doping enhances the adsorption energy, reduces the adsorption distance, and increases the charge transfer in porous carbon. The mechanism of methanol adsorption by Li groups was revealed through experimental and theoretical calculations, providing a theoretical basis for the design and preparation of methanol adsorbents. Adsorption ; Lithium (medication) ; Carbon fibers ; Materials science ; Methanol ; Doping ; Porosity ; Chemical engineering ; Porous medium ; Inorganic chemistry ; Physical chemistry ; Chemistry ; Organic chemistry ; Composite material ; Composite number ; Psychology ; Engineering Catalytic Processes in Materials Science ; Membrane Separation and Gas Transport ; Supercapacitor Materials and Fabrication
Authors: Yonghui Xu | Shuwen Yin | Xiudi Wu | Chuansheng Zhong | Xi‐Bao Zhang | Liang Zhou | Hongpeng You
Source: Journal of the American Ceramic Society (article, 2023-09-27)
Field: Materials Science — Materials Chemistry

Highly efficient blue‐emitting phosphor via charge compensation strategy and its application for white LED lighting Abstract Highly efficient phosphors are indispensable for near‐ultraviolet (n‐UV) pumped white light emitting diodes (WLEDs). In this work, a novel blue emitting Sr 6 GdSc(BO 3 ) 6 :0.08Ce 3+ phosphor was synthesized, exhibiting a broadband blue emission (full width at half‐maximum (FWMH) = 133 nm) excited by 375 nm. Furthermore, the blue emission of Sr 6 GdSc(BO 3 ) 6 :0.08Ce 3+ was enhanced by 3.9 times through codoping Na + ions due to the influence of charge compensation and micromorphology and size of phosphor particles. At the same time, the internal quantum efficiency was improved from 45.4% to 98.6% and the external quantum efficiency up to 75.0%. Finally, the 365 nm excited two WLED devices were constructed by integrating Sr 6 GdSc(BO 3 ) 6 :0.08Ce 3+ , Sr 6 GdSc(BO 3 ) 6 :0.08Ce 3+ ,0.08Na + , commercial green and red phosphors with high color rendering index. These results reveal that the phosphors have prospective application in high‐quality warm white lighting. Phosphor ; Color rendering index ; Quantum efficiency ; Optoelectronics ; Materials science ; Excited state ; Color temperature ; Diode ; Ultraviolet ; Ion ; Optics ; Chemistry ; Physics ; Atomic physics Luminescence Properties of Advanced Materials ; Gas Sensing Nanomaterials and Sensors ; Ga2O3 and related materials
Authors: Dan Guo | Melanie Christine Föll | Kylie Ariel Bemis | Olga Vitek
Source: Bioinformatics (article, 2023-02-01)
Field: Chemistry — Spectroscopy

A noise-robust deep clustering of biomolecular ions improves interpretability of mass spectrometric images Supplementary data are available at Bioinformatics online. Interpretability ; Cluster analysis ; Noise (video) ; Computer science ; Artificial intelligence ; Ion ; Pattern recognition (psychology) ; Data mining ; Image (mathematics) ; Chemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Kun Jia | Mingkun Yang | Xin Liu | Qi Zhang | Gaoxiang Cao | Feng Ge | Jindong Zhao
Source: PLANT PHYSIOLOGY (article, 2023-09-28)
Field: Chemistry — Spectroscopy

Deciphering the structure, function, and mechanism of lysine acetyltransferase cGNAT2 in cyanobacteria Lysine acetylation is a conserved regulatory posttranslational protein modification that is performed by lysine acetyltransferases (KATs). By catalyzing the transfer of acetyl groups to substrate proteins, KATs play critical regulatory roles in all domains of life; however, no KATs have yet been identified in cyanobacteria. Here, we tested all predicted KATs in the cyanobacterium Synechococcus sp. PCC 7002 (Syn7002) and demonstrated that A1596, which we named cyanobacterial Gcn5-related N-acetyltransferase (cGNAT2), can catalyze lysine acetylation in vivo and in vitro. Eight amino acid residues were identified as the key residues in the putative active site of cGNAT2, as indicated by structural simulation and site-directed mutagenesis. The loss of cGNAT2 altered both growth and photosynthetic electron transport in Syn7002. In addition, quantitative analysis of the lysine acetylome identified 548 endogenous substrates of cGNAT2 in Syn7002. We further demonstrated that cGNAT2 can acetylate NAD(P)H dehydrogenase J (NdhJ) in vivo and in vitro, with the inability to acetylate K89 residues, thus decreasing NdhJ activity and affecting both growth and electron transport in Syn7002. In summary, this study identified a KAT in cyanobacteria and revealed that cGNAT2 regulates growth and photosynthesis in Syn7002 through an acetylation-mediated mechanism. Lysine ; Biochemistry ; Acetylation ; Acetyltransferase ; Cyanobacteria ; Biology ; Photosynthesis ; Chloroplast ; NAD+ kinase ; Mutagenesis ; Enzyme ; Amino acid ; Mutant ; Bacteria ; Gene ; Genetics Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Enzyme Structure and Function
Authors: Ahmed Hussein Salman | Waleed A. Mahmoud Al‐Jawher
Source: International Journal of Innovative Computing (article, 2023-09-13)
Field: Chemistry — Analytical Chemistry

A Hybrid Multiwavelet Transform with Grey Wolf Optimization Used for an Efficient Classification of Documents In machine learning, feature selection is crucial to increase performance and shorten the model's learning time. It seeks to discover the pertinent predictors from high-dimensional feature space. However, a tremendous increase in the feature dimension space poses a severe obstacle to feature selection techniques. Study process to address this difficulty, the authors suggest a hybrid feature selection method consisting of the Multiwavelet transform and Gray Wolf optimization. The proposed approach minimizes the overall downsides while cherry picking the benefits of both directions. This notable wavelet transform development employs both wavelet and vector scaling functions. Additionally, multiwavelets have orthogonality, symmetry, compact support, and significant vanishing moments. One of the most advanced areas of study of artificial intelligence is optimization algorithms. Grey Wolf Optimization (GWO) here produced artificial techniques that yielded good performance results and were more responsive to current needs. Keywords — About four key words or phrases in order of importance, separated by commas, used to compile the subject index for the last issue for the year. Artificial intelligence ; Computer science ; Orthogonality ; Pattern recognition (psychology) ; Wavelet transform ; Feature vector ; Feature selection ; Wavelet ; Feature (linguistics) ; Machine learning ; Mathematics Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Image and Signal Denoising Methods
Authors: Xiaomei Zhang | Xiaoxiang Hou | Yiming Su | XiaoBin Yan | Xingxing Qiao | Wude Yang | Meichen Feng | Huihua Kong | Zhou Zhang | Fahad Shafiq | Wenjie Han | Guangxin Li | Ping Chen | Chao Wang
Source: Chemical and Biological Technologies in Agriculture (article, 2023-08-28)
Field: Chemistry — Analytical Chemistry

Analyzing protein concentration from intact wheat caryopsis using hyperspectral reflectance Abstract Background Winter wheat grain samples from 185 sites across southern Shanxi region were processed and analyzed using a non-destructive approach. For this purpose, spectral data and protein content of grain and grain powder were obtained. After combining six types of preprocessed spectra and four types of multivariate statistical models, a relationship between hyperspectral datasets and grain protein is presented. Results It was found that the hyperspectral reflectance of winter wheat grain and powder was positively correlated with the protein contents, which provide the possibility for hyperspectral quantitative assessment. The spectral characteristic bands of protein content in winter wheat extracted based on the SPA algorithm were proved to be around 350–430 nm; 851–1154 nm; 1300–1476 nm; and 1990–2050 nm. In powder samples, SG-BPNN had the best monitoring effect, with the accuracy of R v 2 = 0.814, RMSE v = 0.024 g/g, and RPD v = 2.318. While in case of grain samples, the SG-SVM model exhibited the best monitoring effect, with the accuracy of R v 2 = 0.789, RMSE v = 0.026 g/g, and RPD v = 2.177. Conclusions Based on the experimental findings, we propose that a combination of spectral pretreatment and multivariate statistical modeling is helpful for the non-destructive and rapid estimation of protein content in winter wheat. Graphical Abstract Hyperspectral imaging ; Multivariate statistics ; Winter wheat ; Grain size ; Reflectivity ; Mathematics ; Chemistry ; Analytical Chemistry (journal) ; Agronomy ; Remote sensing ; Materials science ; Biology ; Statistics ; Environmental chemistry ; Geology ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Remote Sensing in Agriculture
Authors: Aneta Břízová | Vladimír Pitschmann
Source: Biosensors (article, 2023-11-22)
Field: Chemistry — Spectroscopy

Simple Chemical and Cholinesterase Methods for the Detection of Nerve Agents Using Optical Evaluation The extreme toxicity of nerve agents and the broad spectrum of their physical and chemical properties, enabling the use of these agents in a variety of tactical situations, is a continuing challenge in maintaining the knowledge and capability to detect them, as well as in finding new effective methods. Despite significant advances in the instrumentation of the analysis of nerve agents, relatively simple methods based on the evaluation of colour signals (absorption and fluorescence), in particular those using the cholinesterase reaction, continue to be of importance. This review provides a brief presentation of the current status of these simple methods, with an emphasis on military applications, and illustrates the high interest of the professional community in their further development. At the same time, it also contains some peculiarities (high reliability and durability, resistance to extreme climatic conditions, work in deployed means of protection, low purchase prices, economic availability especially in a state of war, etc.) that the authors believe research and development of simple methods and means for the detection of nerve agents should respect. Risk analysis (engineering) ; Biochemical engineering ; Computer science ; Reliability (semiconductor) ; Cholinesterase ; Simple (philosophy) ; Variety (cybernetics) ; Instrumentation (computer programming) ; Environmental science ; Engineering ; Business ; Medicine ; Artificial intelligence ; Pharmacology Molecular Sensors and Ion Detection ; Pesticide Exposure and Toxicity ; Cholinesterase and Neurodegenerative Diseases
Authors: Zhihai Liu | Xiang Li | Siying Cheng | Yaru Li | Wei Jin | Yu Zhang | Yifan Qin | Yaxun Zhang | Shanshan Li | Andriy Lotnyk | Libo Yuan
Source: Nanophotonics (article, 2023-06-12)
Field: Materials Science — Materials Chemistry

All‐optical nonvolatile optical modulator for in‐fiber operation The control of information is a defining feature of the information age, and the optical modulator likewise has a crucial role in optical networks. The transmission, processing, and storage of data have demanded low energy consumption and high speed for photonic systems, promoting the development of electro-optic modulators to all-optical modulators. Although these all-optical modulation methods eliminate the photoelectric conversion, the disadvantage of volatile materials requiring continuous power supply when processing and retaining data in new materials-based devices increase energy consumption. We propose a Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub> (GST) integrated all-optical, nonvolatile optical modulator for in-fiber operation. The pulse-induced GST phase transition changes the reflectivity of the fiber end face, and this difference affects the result of the interference, achieving a modulation of output light intensity in interference spectra. The experimental results reveal that the device has obtained 13 dB interference intensity contrast in the telecommunications bands, and its response to a pump pulse is around 100 ns. Furthermore, we demonstrated the operation of the device as a scalar multiplication unit and a logic operation unit. The signal can be transmitted, processed, and stored in the fiber without photoelectric conversion. With the benefits of the switching power consumption of less than 100 nJ and the nonvolatile nature of GST, the device will be more energy-efficient in synchronous processing and storing. This in-fiber operating modulator lays the foundation for developing all-optical devices and networks. Materials science ; Optical transistor ; Photonics ; Optoelectronics ; Optical fiber ; Optical modulator ; Optical switch ; Modulation (music) ; Computer science ; Optics ; Phase modulation ; Telecommunications ; Electrical engineering ; Transistor ; Engineering ; Physics ; Voltage Phase-change materials and chalcogenides ; Photonic and Optical Devices ; Optical Network Technologies
Authors: Max Kerr Winter | Ilian Pihlajamaa | Vincent E. Debets | Liesbeth M. C. Janssen
Source: The Journal of Chemical Physics (article, 2023-06-27)
Field: Materials Science — Materials Chemistry

A deep learning approach to the measurement of long-lived memory kernels from generalized Langevin dynamics Memory effects are ubiquitous in a wide variety of complex physical phenomena, ranging from glassy dynamics and metamaterials to climate models. The Generalized Langevin Equation (GLE) provides a rigorous way to describe memory effects via the so-called memory kernel in an integro-differential equation. However, the memory kernel is often unknown, and accurately predicting or measuring it via, e.g., a numerical inverse Laplace transform remains a herculean task. Here, we describe a novel method using deep neural networks (DNNs) to measure memory kernels from dynamical data. As a proof-of-principle, we focus on the notoriously long-lived memory effects of glass-forming systems, which have proved a major challenge to existing methods. In particular, we learn the operator mapping dynamics to memory kernels from a training set generated with the Mode-Coupling Theory (MCT) of hard spheres. Our DNNs are remarkably robust against noise, in contrast to conventional techniques. Furthermore, we demonstrate that a network trained on data generated from analytic theory (hard-sphere MCT) generalizes well to data from simulations of a different system (Brownian Weeks-Chandler-Andersen particles). Finally, we train a network on a set of phenomenological kernels and demonstrate its effectiveness in generalizing to both unseen phenomenological examples and supercooled hard-sphere MCT data. We provide a general pipeline, KernelLearner, for training networks to extract memory kernels from any non-Markovian system described by a GLE. The success of our DNN method applied to noisy glassy systems suggests that deep learning can play an important role in the study of dynamical systems with memory. Computer science ; Deep learning ; Kernel (algebra) ; Statistical physics ; Artificial intelligence ; Laplace transform ; Theoretical computer science ; Algorithm ; Physics ; Mathematics ; Mathematical analysis Material Dynamics and Properties ; Plant Water Relations and Carbon Dynamics ; Thermoregulation and physiological responses
Authors: Alicia Y. Leem | Haoyuan Chen
Source: Molecular Simulation (article, 2023-06-08)
Field: Chemistry — Inorganic Chemistry

Simulating water adsorption in metal–organic frameworks with open metal sites using the 12-6-4 Lennard–Jones potential Metal–organic frameworks (MOFs) with coordinatively unsaturated open metal sites (OMSs) are promising sorbent materials in atmospheric water harvesting (AWH) systems at low relative humidity (RH) due to their strong interactions with water molecules. However, accurate computational modelling of water adsorption in those materials are challenging, as standard force fields (FFs) based on the 12-6 Lennard–Jones (L–J) potential cannot properly describe the water–OMS interactions. In biomolecular simulations, the 12-6-4 L–J potential has been successfully used to model metal ion–water interactions in which the extra 1/r4-term is for charge-induced dipole electrostatics. In this work, we adopted this strategy and used the 12-6-4 L–J potential to model water–OMS interactions in MOFs for low RH AWH applications. Without any modifications, the parameters used for aqueous metal ions were able to greatly improve the accuracy in predicting water adsorption isotherms in MOF-74, which highlights the simplicity and transferability of this method. Adsorption ; Metal ; Chemical polarity ; Aqueous solution ; Metal ions in aqueous solution ; Electrostatics ; Water model ; Molecule ; Transferability ; Dipole ; Chemistry ; Metal-organic framework ; Chemical physics ; Molecular dynamics ; Materials science ; Computational chemistry ; Physical chemistry ; Computer science ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advanced NMR Techniques and Applications ; Spectroscopy and Quantum Chemical Studies