jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tongtong Yang | Donglai Zhou | Sheng Ye | Xiyu Li | Huirong Li | Yi Feng | Zifan Jiang | Yang Li | Ke Ye | Yixi Shen | Shuang Jiang | Shuo Feng | Guozhen Zhang | Yan Huang | Song Wang | Jun Jiang
Source: Journal of the American Chemical Society (article, 2023-11-29)
Field: Materials Science — Materials Chemistry

Catalytic Structure Design by AI Generating with Spectroscopic Descriptors Generative artificial intelligence has depicted a beautiful blueprint for on-demand design in chemical research. However, the few successful chemical generations have only been able to implement a few special property values because most chemical descriptors are mathematically discrete or discontinuously adjustable. Herein, we use spectroscopic descriptors with machine learning to establish a quantitative spectral structure-property relationship for adsorbed molecules on metal monatomic catalysts. Besides catalytic properties such as adsorption energy and charge transfer, the complete spatial relative coordinates of the adsorbed molecule were successfully inverted. The spectroscopic descriptors and prediction models are generalized, allowing them to be transferred to several different systems. Due to the continuous tunability of the spectroscopic descriptors, the design of catalytic structures with continuous adsorption states generated by AI in the catalytic process has been achieved. This work paves the way for using spectroscopy to enable real-time monitoring of the catalytic process and continuous customization of catalytic performance, which will lead to profound changes in catalytic research. Chemistry ; Catalysis ; Combinatorial chemistry ; Computational chemistry ; Stereochemistry ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Electrocatalysts for Energy Conversion
Authors: Tianchen Zhao | James Stokes | Shravan Veerapaneni
Source: Machine Learning Science and Technology (article, 2023-06-01)
Field: Materials Science — Materials Chemistry

Scalable neural quantum states architecture for quantum chemistry Abstract Variational optimization of neural-network representations of quantum states has been successfully applied to solve interacting fermionic problems. Despite rapid developments, significant scalability challenges arise when considering molecules of large scale, which correspond to non-locally interacting quantum spin Hamiltonians consisting of sums of thousands or even millions of Pauli operators. In this work, we introduce scalable parallelization strategies to improve neural-network-based variational quantum Monte Carlo calculations for ab-initio quantum chemistry applications. We establish GPU-supported local energy parallelism to compute the optimization objective for Hamiltonians of potentially complex molecules. Using autoregressive sampling techniques, we demonstrate systematic improvement in wall-clock timings required to achieve coupled cluster with up to double excitations baseline target energies. The performance is further enhanced by accommodating the structure of resultant spin Hamiltonians into the autoregressive sampling ordering. The algorithm achieves promising performance in comparison with the classical approximate methods and exhibits both running time and scalability advantages over existing neural-network based methods. Scalability ; Computer science ; Quantum ; Pauli exclusion principle ; Artificial neural network ; Quantum computer ; Ab initio ; Statistical physics ; Computational science ; Physics ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum many-body systems
Authors: Kristen A. Fichthorn
Source: Chemical Reviews (review, 2023-03-21)
Field: Materials Science — Materials Chemistry

Theory of Anisotropic Metal Nanostructures A significant challenge in the development of functional materials is understanding the growth and transformations of anisotropic colloidal metal nanocrystals. Theory and simulations can aid in the development and understanding of anisotropic nanocrystal syntheses. The focus of this review is on how results from first-principles calculations and classical techniques, such as Monte Carlo and molecular dynamics simulations, have been integrated into multiscale theoretical predictions useful in understanding shape-selective nanocrystal syntheses. Also, examples are discussed in which machine learning has been useful in this field. There are many areas at the frontier in condensed matter theory and simulation that are or could be beneficial in this area and these prospects for future progress are discussed. Nanocrystal ; Anisotropy ; Nanotechnology ; Nanostructure ; Chemistry ; Density functional theory ; Monte Carlo method ; Statistical physics ; Field (mathematics) ; Molecular dynamics ; Development (topology) ; Physics ; Computational chemistry ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Xin Xie | Wenrou Yu | Zhaoxian Chen | Li Wang | Junjun Yang | Shihong Liu | Linze Li | Yanxi Li | Yingzhou Huang
Source: Nanoscale (article, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Early-stage oral cancer diagnosis by artificial intelligence-based SERS using Ag NWs@ZIF core–shell nanochains Surface-enhanced Raman spectroscopy (SERS) has great potential in the early diagnosis of diseases by detecting the changes of volatile biomarkers in exhaled breath, because of its high sensitivity, rich chemical molecular fingerprint information, and immunity to humidity. Here, an accurate diagnosis of oral cancer (OC) is demonstrated using artificial intelligence (AI)-based SERS of exhaled breath in plasmonic-metal organic framework (MOF) nanoparticles. These plasmonic-MOF nanoparticles were prepared using a zeolitic imidazolate framework coated on Ag nanowires (Ag NWs@ZIF), which offers Raman enhancement from the plasmonic nanowires and gas enrichment from the ZIF shells. Then, the core-shell nanochains of Ag NWs@ZIF prepared with 0.5 mL Ag NWs were selected to capture gaseous methanethiol, which is a tumor biomarker, from the exhalation of OC patients. The substrate was used to collect a total of 400 SERS spectra of exhaled breath of simulated healthy people and simulated OC patients. The artificial neural network (ANN) model in the AI algorithm was trained with these SERS spectra and could classify them with an accuracy of 99%. Notably, the model predicted OC with an area under the curve (AUC) of 0.996 for the simulated OC breath samples. This work suggests the great potential of the combination of breath analysis and AI as a method for the early-stage diagnosis of oral cancer. Shell (structure) ; Stage (stratigraphy) ; Core (optical fiber) ; Materials science ; Nanotechnology ; Cancer ; Computer science ; Medicine ; Composite material ; Internal medicine ; Geology Gold and Silver Nanoparticles Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Advanced biosensing and bioanalysis techniques
Authors: Аbdelkаrim Аit Mаnsour | Badr El-Haitout | Raihana Jannat Adnin | Hassane Lgaz | R. Salghi | Han‐Seung Lee | Mustafa R. Albayati | Mouslim Messali | Khadija Haboubi | Ismat H. Ali
Source: Metals (article, 2023-04-18)
Field: Materials Science — Materials Chemistry

Insights into the Corrosion Inhibition Performance of Isonicotinohydrazide Derivatives for N80 Steel in 15% HCl Medium: An Experimental and Molecular Level Characterization In this work, two compounds of isonicotinohydrazide organic class, namely (E)-N′-(1-(4-(dimethylamino)phenyl)ethylidene) isonicotinohydrazide (MAPEI) and (Z)-N′-(2-oxo-2, 3-dihydro-1H-inden-1-ylidene) isonicotinohydrazide (OHEI) were synthesized and evaluated for corrosion protection of N80 steel in a concentrated acidic medium (15 wt.% HCl) at a temperature of 303 K. The weight loss method (gravimetric method) and electrochemical techniques, i.e., electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization curves (PPC), were used to evaluate the inhibition and adsorption characteristics of tested compounds. Further, surface characterization using a scanning electron microscope (SEM) was used to assess the surface morphology of steel before and after inhibition. Weight loss experiments at 303 K and 363 K showed that tested compounds’ performance decreased with the increase in temperature, particularly at low concentrations of inhibitors whereas they exhibited good stability at higher concentrations. Electrochemical tests showed that MAPEI and OHEI inhibitors were effective at 5 × 10−3 mol/L, reaching an inhibition efficiency above 90%. It was also determined that the adsorption of both inhibitors followed the Langmuir adsorption isotherm model. Furthermore, SEM analysis showed that the investigated compounds can form a protective layer against steel corrosion in an acidic environment. On the other hand, the corrosion inhibition mechanism was established from density functional theory (DFT), and the self-consistent-charge density-functional tight-binding (SCC-DFTB) method which revealed that both inhibitors exerted physicochemical interactions by charge transfer between the s- and p-orbitals of tested molecules and the d-orbital of iron. The results of this work are intended to deepen the research on the products of this family to control the problem of corrosion. Chemistry ; Adsorption ; Dielectric spectroscopy ; Corrosion ; Electrochemistry ; Langmuir adsorption model ; Gravimetric analysis ; Density functional theory ; Scanning electron microscope ; Molecular orbital ; Molecule ; Nuclear chemistry ; Analytical Chemistry (journal) ; Inorganic chemistry ; Physical chemistry ; Computational chemistry ; Chromatography ; Organic chemistry ; Materials science ; Electrode Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Xu Xu | Muyao Ma | Tongxin Sun | Xin Zhao | Lei Zhang
Source: Biosensors (review, 2023-03-29)
Field: Materials Science — Materials Chemistry

Luminescent Guests Encapsulated in Metal–Organic Frameworks for Portable Fluorescence Sensor and Visual Detection Applications: A Review Metal-organic frameworks (MOFs) have excellent applicability in several fields and have significant structural advantages, due to their open pore structure, high porosity, large specific surface area, and easily modifiable and functionalized porous surface. In addition, a variety of luminescent guest (LG) species can be encapsulated in the pores of MOFs, giving MOFs a broader luminescent capability. The applications of a variety of LG@MOF sensors, constructed by doping MOFs with LGs such as lanthanide ions, carbon quantum dots, luminescent complexes, organic dyes, and metal nanoclusters, for fluorescence detection of various target analyses such as ions, biomarkers, pesticides, and preservatives are systematically introduced in this review. The development of these sensors for portable visual fluorescence sensing applications is then covered. Finally, the challenges that these sectors currently face, as well as the potential for future growth, are briefly discussed. Luminescence ; Nanotechnology ; Nanoclusters ; Fluorescence ; Lanthanide ; Metal-organic framework ; Materials science ; Porosity ; Metal ions in aqueous solution ; Metal ; Ion ; Chemistry ; Adsorption ; Optoelectronics ; Organic chemistry Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Sivakamavalli Jeyachandran | Ramachandran Srinivasan | Thiyagarajan Ramesh | Arumugam Parivallal | Jintae Lee | Ezhaveni Sathiyamoorthi
Source: Biomimetics (review, 2023-09-21)
Field: Materials Science — Materials Chemistry

Recent Development and Application of “Nanozyme” Artificial Enzymes—A Review Nanozymes represent a category of nano-biomaterial artificial enzymes distinguished by their remarkable catalytic potency, stability, cost-effectiveness, biocompatibility, and degradability. These attributes position them as premier biomaterials with extensive applicability across medical, industrial, technological, and biological domains. Following the discovery of ferromagnetic nanoparticles with peroxidase-mimicking capabilities, extensive research endeavors have been dedicated to advancing nanozyme utilization. Their capacity to emulate the functions of natural enzymes has captivated researchers, prompting in-depth investigations into their attributes and potential applications. This exploration has yielded insights and innovations in various areas, including detection mechanisms, biosensing techniques, and device development. Nanozymes exhibit diverse compositions, sizes, and forms, resembling molecular entities such as proteins and tissue-based glucose. Their rapid impact on the body necessitates a comprehensive understanding of their intricate interplay. As each day witnesses the emergence of novel methodologies and technologies, the integration of nanozymes continues to surge, promising enhanced comprehension in the times ahead. This review centers on the expansive deployment and advancement of nanozyme materials, encompassing biomedical, biotechnological, and environmental contexts. Expansive ; Biochemical engineering ; Nanotechnology ; Computer science ; Engineering ; Materials science Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Owen Tamin | Ervin Gubin Moung | Jamal Ahmad Dargham | Farashazillah Yahya | Sigeru Omatu
Source: International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering (review, 2023-01-31)
Field: Chemistry — Analytical Chemistry

A review of hyperspectral imaging-based plastic waste detection state-of-the-arts &lt;span lang="EN-US"&gt;Plastic waste issues emerged from the build-up of plastics that negatively impacts the environment. As a result, plastic waste detection is proposed in many research studies to tackle the problems. Therefore, this paper aims to review hyperspectral imaging techniques and machine learning in plastic waste detection. Hyperspectral imaging techniques are found to be effective in detecting plastic waste and microplastics as they were able to capture plastic reflectance spectral by using the near-infrared sensor. However, the review also shows that hyperspectral imaging techniques were less efficient in capturing the electromagnetic spectrum of black plastics due to carbon-black absorption properties. Carbon-black strongly absorbs light in the ultraviolet and infrared spectral range of the electromagnetic spectrum, therefore not detected by the near-infrared sensor. This paper also reviews how machine learning can alternatively detect and sort all types of waste, including plastics. Multiple studies show that the machine learning model achieved good accuracy in detecting all types of plastics based on the waste dataset. Finally, it can be seen that the spectral information of plastic can be used as feature extraction for machine learning models for better plastic detection. It is hoped that this study will contribute to more systematic research on the same topic.&lt;/span&gt; Hyperspectral imaging ; Plastic waste ; Microplastics ; Environmental science ; Computer science ; Spectral signature ; Feature (linguistics) ; Infrared ; Remote sensing ; Carbon black ; Materials science ; Process engineering ; Artificial intelligence ; Waste management ; Optics ; Engineering ; Environmental chemistry ; Chemistry ; Composite material ; Geology ; Physics Spectroscopy and Chemometric Analyses
Authors: Baoyu Huang | Xinwei Tong | Xiangpeng Zhang | Qiuxia Feng | M. N. Rumyantseva | Jai Prakash | Xiaogan Li
Source: Chemosensors (article, 2023-04-21)
Field: Materials Science — Materials Chemistry

MXene/NiO Composites for Chemiresistive-Type Room Temperature Formaldehyde Sensor In this work, MXene/NiO-composite-based formaldehyde (HCHO) sensing materials were successfully synthesized by an in situ precipitation method. The heterostructures between the MXene and NiO nanoparticles were verified by transmission electron microscopy (TEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). The HCHO sensing performance of the MXene/NiO-based chemiresistive-type sensors was investigated. Compared to pure MXene and NiO materials, the sensing performance of the MXene/NiO-P2-based sensor to HCHO gas at room temperature was significantly enhanced by the formation of MXene/NiO heterojunctions. The response of the MXene/NiO-P2 sensor to 50 ppm HCHO gas was 8.8, which was much higher than that of the pure MXene and NiO. At room temperature, the detectable HCHO concentration of the MXene/NiO-P2-based sensor was 1 ppm, and the response and recovery time to 2 ppm HCHO was 279 s and 346 s, respectively. The MXene/NiO-P2 sensor also exhibited a good selectivity and a long-term stability to HCHO gas for 56 days. The in situ Fourier transform infrared (FTIR) spectra of the MXene/NiO-P2 sensor, when exposed to HCHO gas at different times, were investigated to verify the adsorption reaction products of HCHO molecules. Non-blocking I/O ; X-ray photoelectron spectroscopy ; Formaldehyde ; Materials science ; Fourier transform infrared spectroscopy ; Adsorption ; Heterojunction ; Chemical engineering ; Chemistry ; Catalysis ; Optoelectronics ; Physical chemistry ; Organic chemistry MXene and MAX Phase Materials ; Gas Sensing Nanomaterials and Sensors ; 2D Materials and Applications
Authors: Sunardi Sunardi | Dody Ariawan | Eko Surojo | Aditya Rio Prabowo | Hammar Ilham Akbar | Bo Cao | Hermes Carvalho
Source: Journal of the Mechanical Behavior of Materials (article, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Assessment of eggshell-based material as a green-composite filler: Project milestones and future potential as an engineering material Abstract The utilization of eggshell (ES) waste as a composite filler has increased significantly in the last 5 years. This increase in usage took place due to its unique characteristics, which improve the properties of the resulting composite. Adding a weight fraction of ES particles into a composite can improve its mechanical properties, although not all studies have shown this phenomenon. Studies on these composites’ thermal and tribological properties are still limited, so more in-depth studies could be carried out. The degradation of composite performance due to friction or exposure to humidity and hot temperature is another area that is worthy of further study. In this work, we discuss changes in composites’ mechanical, thermal, and tribological properties associated with the addition of ES particles, examining both untreated particles and those treated with carbonation. This work can serve as a guide for the utilization of ES particles as an environmentally friendly composite material. Composite number ; Materials science ; Tribology ; Composite material ; Filler (materials) ; Carbonation ; Environmentally friendly ; Work (physics) ; Mechanical engineering ; Engineering Natural Fiber Reinforced Composites ; Aluminum Alloys Composites Properties ; Recycling and Waste Management Techniques
Authors: Xin Wang | Yang Xia | Juntong Huang | Suwei Yao | Zhi Chen | Linlin Chen | Zhaohui Wu | Zhijun Feng | Huiyong Yang | Xibao Li
Source: Journal of Advanced Ceramics (article, 2023-11-27)
Field: Materials Science — Materials Chemistry

A facile strategy for large-scale production of 2D nanosheets exfoliated by three-roll milling Two-dimensional (2D) nanomaterials, such as graphene, MoS<sub>2</sub>, and MAX, have attracted increasing research attention in recent years due to their unique structural and performance advantages. However, their complex production processes and equipment requirements are significant issues affecting their widespread use. Here, by using an exfoliation strategy by three-roll milling, we present a simple, cost-effective, and extensible method to produce multilayer graphene, BN, MoS<sub>2</sub>, and Ti<sub>3</sub>AlC<sub>2</sub> nanosheets. The roller and phenolic resin created three kinds of forces on the layered 2D materials, shear forces, compressive forces and adhesive forces, which exfoliated the layered materials from their edges and surfaces into nanosheets. Subsequently, the exfoliated materials were ultrasonically washed with alcohol, treated with ultrasonic vibration and centrifuged to obtain 2D nanomaterials. The easy operation and high yield are attractive for research based on the construction of high-performance 2D nanosheet-based devices at low cost. Herein, the obtained multilayer graphene and MoS<sub>2 </sub>nanosheets were used as the anode material of sodium/potassium-ion batteries, respectively, to test their electrochemical properties. Better performances are obtained compared with their primary bulk materials. Materials science ; Nanosheet ; Nanomaterials ; Exfoliation joint ; Graphene ; Anode ; Structural material ; Nanotechnology ; Nanomanufacturing ; Composite material ; Electrode Graphene research and applications ; MXene and MAX Phase Materials ; 2D Materials and Applications
Authors: Tong-Qing Chai | Jiali Wang | Guoying Chen | Lingxiao Chen | Feng Yang
Source: Sensors (article, 2023-09-28)
Field: Materials Science — Materials Chemistry

Tris-Copper Nanozyme as a Novel Laccase Mimic for the Detection and Degradation of Phenolic Compounds Phenolic compounds are one of the main organic pollutants in the environment that can seriously affect ecosystems, even at very low concentrations. Due to the resistance of phenolic compounds to microorganisms, conventional biological treatment methods face challenges in effectively addressing this pollution problem. In this study, a novel laccase mimic (Tris-Cu nanozyme) is prepared using a simple and rapid synthesis strategy based on the coordination of copper ions and amino groups in Tris(hydroxymethyl)aminomethane (Tris). It is found that the Tris-Cu nanozyme exhibits good catalytic activity against a variety of phenolic compounds, the <i>K<sub>m</sub></i>, <i>V<sub>max</sub></i> and <i>K<sub>cat</sub></i> are determined to be 0.18 mM, 15.62 μM·min<sup>-1</sup> and 1.57 × 10<sup>7</sup> min<sup>-1</sup> using 2,4-dichlorophenol (2,4-DP) as the substrate, respectively. Then, based on the laccase-like activity of the Tris-Cu nanozyme, a novel colorimetric method for 2,4-DP (the limit of detection (LOD) = 2.4 μM, S/N = 3) detection in the range of 10-400 μM was established, and its accuracy was verified by analyzing tap and lake water samples. In addition, the Tris-Cu nanozyme shows excellent removal abilities for six phenolic compounds in experiments. The removal percentages for 2,4-DP, 2-chlorophenol (2-CP), phenol, resorcinol, 2,6-dimethoxyphenol (2,6-DOP), and bisphenol A (BPA) are 100%, 100%, 100%, 100%, 87%, and 81% at 1 h, respectively. In the simulated effluent, the Tris-Cu nanozyme maintains its efficient catalytic activity towards 2,4-DP, with a degradation percentage of 76.36% at 7 min and a reaction rate constant (<i>k</i><sub>0</sub>) of 0.2304 min<sup>-1</sup>. Therefore, this metal-organic complex shows promise for applications in the monitoring and degrading of environmental pollutants. Tris ; Hydroxymethyl ; Chemistry ; Laccase ; Resorcinol ; Phenol ; Copper ; Phenols ; Bisphenol A ; Detection limit ; Substrate (aquarium) ; Nuclear chemistry ; Organic chemistry ; Chromatography ; Enzyme Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques