jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhonglei Ma | Xiaolian Xiang | Liang Shao | Yali Zhang | Junwei Gu
Source: Angewandte Chemie (article, 2022-02-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multifunctional Wearable Silver Nanowire Decorated Leather Nanocomposites for Joule Heating, Electromagnetic Interference Shielding and Piezoresistive Sensing Abstract Multifunctional wearable electronic devices based on natural materials are highly desirable for versatile applications of energy conversion, electronic skin and artificial intelligence. Herein, multifunctional wearable silver nanowire decorated leather (AgNW/leather) nanocomposites with hierarchical structures for integrated visual Joule heating, electromagnetic interference (EMI) shielding and piezoresistive sensing are fabricated via the facile vacuum‐assisted filtration process. The AgNWs penetrate the micro‐nanoporous structures in the corium side of leather constructing highly‐efficient conductive networks. The resultant flexible and mechanically strong AgNW/leather nanocomposites exhibit extremely low sheet resistance of 0.8 Ω/sq, superior visual Joule heating temperatures up to 108 °C at low supplied voltage of 2.0 V due to efficient energy conversion, excellent EMI shielding effectiveness (EMI SE) of ≈55 dB and outstanding piezoresistive sensing ability in human motion detection. This work demonstrates the fabrication of multifunctional AgNW/leather nanocomposites for next‐generation wearable electronic devices in energy conversion, electronic skin and artificial intelligence, etc. Materials science ; Piezoresistive effect ; Electromagnetic interference ; Joule heating ; Electromagnetic shielding ; Nanocomposite ; Nanotechnology ; EMI ; Electrical conductor ; Fabrication ; Electronics ; Triboelectric effect ; Optoelectronics ; Composite material ; Electrical engineering Electromagnetic wave absorption materials ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Jingyi Zhou | Ke Tian | Felix Steinke | Norbert Stock | Zhiguo Zhang | Zongbi Bao | Xin He | Qilong Ren | Qiwei Yang
Source: Journal of the American Chemical Society (article, 2022-07-18)
Field: Chemistry — Inorganic Chemistry

Tunable Confined Aliphatic Pore Environment in Robust Metal–Organic Frameworks for Efficient Separation of Gases with a Similar Structure The fine-tuning of the pore structure of metal-organic frameworks (MOFs) is of critical importance to developing energy-efficient processes for the challenging separation of structurally similar molecules. Herein, we demonstrate a strategy to realize a quasi-three-dimensional refinement of the pore structure that utilizes the tunability of ring size and number in polycycloalkane-dicarboxylate ligands. Two hydrolytically stable MOFs with a confined aliphatic pore environment, ZUL-C1 and ZUL-C2, were, for the first time, synthesized and applied in separating low-concentration C2-C3 hydrocarbons from natural gas and ultralow-concentration Xe from used nuclear fuel (UNF) off-gas. Validated by X-ray diffraction and modeling, an expansion of the polycycloalkane moiety enables sub-angstrom contraction in specific directions and forms a pore surface with more alkyl sites, which affords stronger trapping of guest molecules with relatively higher polarizability. The resultant material exhibits record C<sub>2</sub>H<sub>6</sub>/CH<sub>4</sub> and C<sub>3</sub>H<sub>8</sub>/CH<sub>4</sub> selectivities coupled with a benchmark low-pressure C<sub>2</sub>H<sub>6</sub> capacity in alkane mixture separation and also a benchmark Xe capacity at extremely diluted feed concentration and record Kr productivity for the Xe/Kr (20:80, v/v) mixture in Xe/Kr separation. Chemistry ; Polarizability ; Metal-organic framework ; Moiety ; Molecule ; Alkyl ; Alkane ; Metal ; Gas separation ; Chemical engineering ; Chemical physics ; Hydrocarbon ; Organic chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Inorganic Fluorides and Related Compounds
Authors: Qing Zhu | Fei Zhang | Yan Huang | Hengyu Xiao | LuYuan Zhao | Xuchun Zhang | Tao Song | Xinsheng Tang | Xiang Li | He Guo | Baochen Chong | Junyi Zhou | YiHan Zhang | Baicheng Zhang | Jiaqi Cao | Man Luo | Song Wang | GuiLin Ye | Wanjun Zhang | Xin Chen | Shuang Cong | Donglai Zhou | Huirong Li | Jialei Li | Gang Zou | Weiwei Shang | Jun Jiang | Yi Luo
Source: National Science Review (article, 2022-09-06)
Field: Materials Science — Materials Chemistry

An all-round AI-Chemist with a scientific mind The realization of automated chemical experiments by robots unveiled the prelude to an artificial intelligence (AI) laboratory. Several AI-based systems or robots with specific chemical skills have been demonstrated, but conducting all-round scientific research remains challenging. Here, we present an all-round AI-Chemist equipped with scientific data intelligence that is capable of performing basic tasks generally required in chemical research. Based on a service platform, the AI-Chemist is able to automatically read the literatures from a cloud database and propose experimental plans accordingly. It can control a mobile robot in-house or online to automatically execute the complete experimental process on 14 workstations, including synthesis, characterization and performance tests. The experimental data can be simultaneously analysed by the computational brain of the AI-Chemist through machine learning and Bayesian optimization, allowing a new hypothesis for the next iteration to be proposed. The competence of the AI-Chemist has been scrutinized by three different chemical tasks. In the future, the more advanced all-round AI-Chemists equipped with scientific data intelligence may cause changes to the landscape of the chemical laboratory. Chemist ; Computer science ; Artificial intelligence ; Competence (human resources) ; Realization (probability) ; Robotics ; Robot ; Process (computing) ; Human–computer interaction ; Chemistry Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Advanced Chemical Sensor Technologies
Authors: Terry J. Hendricks | T. Caillat | Takao Mori
Source: Energies (article, 2022-10-05)
Field: Materials Science — Materials Chemistry

Keynote Review of Latest Advances in Thermoelectric Generation Materials, Devices, and Technologies 2022 The last decade created tremendous advances in new and unique thermoelectric generation materials, devices, fabrication techniques, and technologies via various global research and development. This article seeks to elucidate and highlight some of these advances to lay foundations for future research work and advances. New advanced methods and demonstrations in TE device and material measurement, materials fabrication and composition advances, and device design and fabrication will be discussed. Other articles in this Special Issue present additional new research into materials fabrication and composition advances, including multi-dimensional additive manufacturing and advanced silicon germanium technologies. This article will discuss the most recent results and findings in thermoelectric system economics, including highlighting and quantifying the interrelationships between thermoelectric (TE) material costs, TE manufacturing costs and most importantly, often times dominating, the heat exchanger costs in overall TE system costs. We now have a methodology for quantifying the competing TE system cost-performance effects and impacts. Recent findings show that heat exchanger costs usually dominate overall TE system cost-performance tradeoffs, and it is extremely difficult to escape this condition in TE system design. In regard to material performance, novel or improved enhancement principles are being effectively implemented. Furthermore, in addition to further advancements in properties and module developments of relatively established champion materials such as skutterudites, several high performance ZT ≈≥ 2 new material systems such as GeTe, Mg3(Sb,Bi)2 have also been relatively recently unearthed and module applications also being considered. These recent advancements will also be covered in this review. Thermoelectric materials ; Thermoelectric effect ; Nanotechnology ; Fabrication ; Systems engineering ; Computer science ; Manufacturing engineering ; Engineering ; Materials science Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Chalcogenide Semiconductor Thin Films
Authors: Hiba Mohammed Taha | Reza Aalizadeh | ‪Nikiforos Alygizakis | Jean‐Philippe Antignac | Hans Peter H. Arp | Richard Bade | Nancy Baker | Lidia Belova | Lubertus Bijlsma | Evan Bolton | Werner Brack | Alberto Celma | Wen‐Ling Chen | Tiejun Cheng | Parviel Chirsir | Ľuboš Čirka | Lisa A. D’Agostino | Yannick Djoumbou-Feunang | Valeria Dulio | Stellan Fischer | Pablo Gago-Ferrero | Aikaterini Galani | Birgit Geueke | Natalia Głowacka | Juliane Glüge | Ksenia J. Groh | Sylvia Grosse | Peter Haglund | Pertti J. Hakkinen | Sarah E. Hale | Félix Hernández | Elisabeth M.‐L. Janssen | Tim Jonkers | Karin Kiefer | Michal Kirchner | Jan Koschorreck | Martin Krauß | Jessy Krier | M.H. Lamoree | Marion Letzel | Thomas Letzel | Qingliang Li | James J. Little | Yanna Liu | David M. Lunderberg | Jonathan W. Martin | Andrew D. McEachran | John A. McLean | Christiane Meier | Jeroen Meijer | Frank Menger | Carla Merino | Jane Muncke | Matthias Muschket | Michael Neumann | Vanessa Neveu | Kelsey Ng | Herbert Oberacher | Jake O’Brien | Peter Oswald | Martina Oswaldova | Jaqueline A. Picache | Cristina Postigo | Noelia Ramírez | Thorsten Reemtsma | Justin B. Renaud | Paweł Rostkowski | Heinz Rüdel | Reza M. Salek | Saer Samanipour | Martin Scheringer | Ivo Schliebner | W. Schulz | Tobias Schulze | Manfred Sengl | Benjamin A. Shoemaker | Kerry Sims | Heinz Singer | Randolph R. Singh | Mark W. Sumarah | Paul Thiessen | Kevin V. Thomas | Sònia Torres | Xenia Trier | Annemarie P. van Wezel | Roel Vermeulen | Jelle Vlaanderen | Peter C. von der Ohe | Zhanyun Wang | Antony Williams | Egon Willighagen | David S. Wishart | Jian Zhang | Νikolaos S. Τhomaidis | Juliane Hollender | Jaroslav Slobodnı́k | Emma Schymanski
Source: Environmental Sciences Europe (article, 2022-10-21)
Field: Chemistry — Spectroscopy

The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry The online version contains supplementary material available at 10.1186/s12302-022-00680-6. Suspect ; Computer science ; Resource (disambiguation) ; Identifier ; Library science ; World Wide Web ; Political science ; Law Mass Spectrometry Techniques and Applications ; Analytical chemistry methods development ; Pharmaceutical and Antibiotic Environmental Impacts
Authors: Xu Long | Minghui Mao | Tianxiong Su | Yutai Su | Mengke Tian
Source: Defence Technology (article, 2022-02-14)
Field: Materials Science — Materials Chemistry

Machine learning method to predict dynamic compressive response of concrete-like material at high strain rates Machine learning (ML) methods with good applicability to complex and highly nonlinear sequences have been attracting much attention in recent years for predictions of complicated mechanical properties of various materials. As one of the widely known ML methods, back-propagation (BP) neural networks with and without optimization by genetic algorithm (GA) are also established for comparisons of time cost and prediction error. With the aim to further increase the prediction accuracy and efficiency, this paper proposes a long short-term memory (LSTM) networks model to predict the dynamic compressive performance of concrete-like materials at high strain rates. Dynamic explicit analysis is performed in the finite element (FE) software ABAQUS to simulate various waveforms in the split Hopkinson pressure bar (SHPB) experiments by applying different stress waves in the incident bar. The FE simulation accuracy is validated against SHPB experimental results from the viewpoint of dynamic increase factor. In order to cover more extensive loading scenarios, 60 sets of FE simulations are conducted in this paper to generate three kinds of waveforms in the incident and transmission bars of SHPB experiments. By training the proposed three networks, the nonlinear mapping relations can be reasonably established between incident, reflect, and transmission waves. Statistical measures are used to quantify the network prediction accuracy, confirming that the predicted stress-strain curves of concrete-like materials at high strain rates by the proposed networks agree sufficiently with those by FE simulations. It is found that compared with BP network, the GA-BP network can effectively stabilize the network structure, indicating that the GA optimization improves the prediction accuracy of the SHPB dynamic responses by performing the crossover and mutation operations of weights and thresholds in the original BP network. By eliminating the long-time dependencies, the proposed LSTM network achieves better results than the BP and GA-BP networks, since smaller mean square error (MSE) and higher correlation coefficient are achieved. More importantly, the proposed LSTM algorithm, after the training process with a limited number of FE simulations, could replace the time-consuming and laborious FE pre- and post-processing and modelling. Split-Hopkinson pressure bar ; Artificial neural network ; Nonlinear system ; Waveform ; Computer science ; Bar (unit) ; Transmission (telecommunications) ; Strain rate ; Backpropagation ; Structural engineering ; Algorithm ; Materials science ; Machine learning ; Engineering ; Composite material High-Velocity Impact and Material Behavior ; Rock Mechanics and Modeling ; Structural Response to Dynamic Loads
Authors: Ileana-Cristina Benea-Chelmus | Sydney Mason | Maryna L. Meretska | Delwin L. Elder | Dmitry Kazakov | Amirhassan Shams‐Ansari | Larry R. Dalton | Federico Capasso
Source: Nature Communications (article, 2022-06-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Gigahertz free-space electro-optic modulators based on Mie resonances Electro-optic modulators are essential for sensing, metrology and telecommunications. Most target fiber applications. Instead, metasurface-based architectures that modulate free-space light at gigahertz (GHz) speeds can boost flat optics technology by microwave electronics for active optics, diffractive computing or optoelectronic control. Current realizations are bulky or have low modulation efficiencies. Here, we demonstrate a hybrid silicon-organic metasurface platform that leverages Mie resonances for efficient electro-optic modulation at GHz speeds. We exploit quasi bound states in the continuum (BIC) that provide narrow linewidth (Q = 550 at [Formula: see text] nm), light confinement to the non-linear material, tunability by design and voltage and GHz-speed electrodes. Key to the achieved modulation of [Formula: see text] are molecules with r<sub>33</sub> = 100 pm/V and optical field optimization for low-loss. We demonstrate DC tuning of the resonant frequency of quasi-BIC by [Formula: see text] 11 nm, surpassing its linewidth, and modulation up to 5 GHz (f<sub>EO,-3dB</sub> = 3 GHz). Guided mode resonances tune by [Formula: see text] 20 nm. Our hybrid platform may incorporate free-space nanostructures of any geometry or material, by application of the active layer post-fabrication. Laser linewidth ; Physics ; Modulation (music) ; Optoelectronics ; Optics ; Electronic circuit ; Materials science ; Laser Metamaterials and Metasurfaces Applications ; Photonic and Optical Devices ; Plasmonic and Surface Plasmon Research
Authors: Lars‐Åke Näslund | Ingemar Persson
Source: Applied Surface Science (article, 2022-04-19)
Field: Materials Science — Materials Chemistry

XPS spectra curve fittings of Ti3C2Tx based on first principles thinking MXenes are an interesting family of 2D materials that have the potential to meet challenges in many applications. A useful tool in the work of understanding the nature of the MXenes, as well as exploring their capabilities, is X-ray photoelectron spectroscopy (XPS). In analyzing XPS spectra it might be necessary to use curve fitting to extract valuable information. However, approaches toward the curve fitting procedure have been different in many studies and introductions of questionable assumptions, unverified feature assignments, and inconsistent curve fitting have led to contrasting conclusions from XPS analysis. It is therefore motivated to show curve fittings of F 1s, O 1s, Ti 2p, and C 1s XPS spectra obtained from high quality Ti3C2Tx that are based on fundamental knowledge applied step by step through the strategy of first principles thinking. With the use of first principles thinking the curve fittings and the subsequent analysis became more realistic compared to what have been presented in recent studies. The results of the curve fittings presented in this work are well founded and can be used as a model for future curve fittings of MXenes. The strategy of first principles thinking is advantageous in XPS curve fittings in general. X-ray photoelectron spectroscopy ; MXenes ; Curve fitting ; Spectral line ; Computer science ; Materials science ; Nanotechnology ; Physics ; Engineering ; Machine learning ; Chemical engineering MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Yang Dang | Guangtu Wang | Gehong Su | Zhiwei Lu | Yanying Wang | Tao Liu | Xiang Pu | Xianxiang Wang | Wu Chun | Chang Song | Qingbiao Zhao | Hanbing Rao | Mengmeng Sun
Source: ACS Nano (article, 2022-03-03)
Field: Materials Science — Materials Chemistry

Rational Construction of a Ni/CoMoO<sub>4</sub> Heterostructure with Strong Ni–O–Co Bonds for Improving Multifunctional Nanozyme Activity Due to the lack of a general descriptor to predict the activity of nanomaterials, the current exploration of nanozymes mainly depended on trial-and-error strategies, which hindered the effective design of nanozymes. Here, with the help of a large number of Ni-O-Co bonds at the interface of heterostructures, a prediction descriptor was successfully determined to reveal the double enzyme-like activity mechanisms for Ni/CoMoO<sub>4</sub>. Additionally, DFT calculations revealed that interface engineering could accelerate the catalytic kinetics of the enzyme-like activity. Ni-O-Co bonds were the main active sites for enzyme-like activity. Finally, the colorimetric signal and intelligent biosensor of Ni/CoMoO<sub>4</sub> based on deep learning were used to detect organophosphorus and ziram sensitively. Meanwhile, the <i>in situ</i> FTIR results uncovered the detection mechanism: the target molecules could block Ni-O-Co active sites at the heterostructure interface leading to the signal peak decreasing. This study not only provided a well design strategy for the further development of nanozymes or other advanced catalysts, but it also designed a multifunctional intelligent biosensor platform. Furthermore, it also provided preferable ideas regarding the catalytic mechanism and detection mechanism of heterostructure nanozymes. Heterojunction ; Biosensor ; Nanomaterials ; Catalysis ; Materials science ; Nanotechnology ; Rational design ; Molecule ; Interface (matter) ; Mechanism (biology) ; Combinatorial chemistry ; Computer science ; Chemistry ; Optoelectronics ; Organic chemistry ; Physics Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Nanocluster Synthesis and Applications
Authors: Xin Ma | Facundo M. Fernández
Source: Mass Spectrometry Reviews (review, 2022-09-06)
Field: Chemistry — Spectroscopy

Advances in mass spectrometry imaging for spatial cancer metabolomics Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particular, spatial metabolomics using MS imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in-depth understanding of cancer-associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics. In this review, we aim to cover the basic principles of MSI experiments for the nonspecialists, including fundamentals, the sample preparation process, the evolution of the mass spectrometry techniques used, and data analysis strategies. We also review MSI advances associated with cancer research in the last 5 years, including spatial lipidomics and glycomics, the adoption of three-dimensional and multimodal imaging MSI approaches, and the implementation of artificial intelligence/machine learning in MSI-based cancer studies. The adoption of MSI in clinical research and for single-cell metabolomics is also discussed. Spatially resolved studies on other small molecule metabolites such as amino acids, polyamines, and nucleotides/nucleosides will not be discussed in the context. Metabolomics ; Mass spectrometry imaging ; Chemistry ; Computational biology ; Context (archaeology) ; Lipidomics ; Biomarker discovery ; Cancer ; Cancer biomarkers ; Mass spectrometry ; Proteomics ; Biochemistry ; Chromatography ; Biology Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Zhoumeng Lin | Wei-Chun Chou | Yi-Hsien Cheng | Chunla He | Nancy A Monteiro-Riviere | Jim E Riviere
Source: International Journal of Nanomedicine (article, 2022-03-01)
Field: Materials Science — Biomaterials

Predicting Nanoparticle Delivery to Tumors Using Machine Learning and Artificial Intelligence Approaches This study provides a quantitative model to improve the design of cancer nanomedicine with greater tumor delivery efficiency. These results help to improve our understanding of the causes of low NP tumor delivery efficiency. This study demonstrates the feasibility of integrating artificial intelligence with PBPK modeling approaches to study cancer nanomedicine. Nanomedicine ; Applications of artificial intelligence ; Cancer ; Nanoparticle ; Cancer therapy ; Artificial neural network Nanoparticle-Based Drug Delivery ; Cancer Research and Treatment ; Radiomics and Machine Learning in Medical Imaging