jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yiping Zhao | Amit Kumar | Yanjun Yang
Source: Chemical Society Reviews (review, 2023-12-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Unveiling practical considerations for reliable and standardized SERS measurements: lessons from a comprehensive review of oblique angle deposition-fabricated silver nanorod array substrates Recently, there has been an exponential growth in the number of publications focusing on surface-enhanced Raman scattering (SERS), primarily driven by advancements in nanotechnology and the increasing demand for chemical and biological detection. While many of these publications have focused on the development of new substrates and detection-based applications, there is a noticeable lack of attention given to various practical issues related to SERS measurements and detection. This review aims to fill this gap by utilizing silver nanorod (AgNR) SERS substrates fabricated through the oblique angle deposition method as an illustrative example. The review highlights and addresses a range of practical issues associated with SERS measurements and detection. These include the optimization of SERS substrates in terms of morphology and structural design, considerations for measurement configurations such as polarization and the incident angle of the excitation laser, and exploration of enhancement mechanisms encompassing both intrinsic properties induced by the structure and materials, as well as extrinsic factors arising from wetting/dewetting phenomena and analyte size. The manufacturing and storage aspects of SERS substrates, including scalable fabrication techniques, contamination control, cleaning procedures, and appropriate storage methods, are also discussed. Furthermore, the review delves into device design considerations, such as well arrays, flow cells, and fiber probes, and explores various sample preparation methods such as drop-cast and immersion. Measurement issues, including the effect of excitation laser wavelength and power, as well as the influence of buffer, are thoroughly examined. Additionally, the review discusses spectral analysis techniques, encompassing baseline removal, chemometric analysis, and machine learning approaches. The wide range of AgNR-based applications of SERS, across various fields, is also explored. Throughout the comprehensive review, key lessons learned from collective findings are outlined and analyzed, particularly in the context of detailed SERS measurements and standardization. The review also provides insights into future challenges and perspectives in the field of SERS. It is our hope that this comprehensive review will serve as a valuable reference for researchers seeking to embark on in-depth studies and applications involving their own SERS substrates. Nanorod ; Nanotechnology ; Deposition (geology) ; Materials science ; Oblique case ; Geology Gold and Silver Nanoparticles Synthesis and Applications ; Optical Coatings and Gratings ; Near-Field Optical Microscopy
Authors: Priyanka Raghavan | Brittany C. Haas | Madeline E. Ruos | Jules Schleinitz | Abigail G. Doyle | Sarah E. Reisman | Matthew S. Sigman | Connor W. Coley
Source: ACS Central Science (review, 2023-12-08)
Field: Materials Science — Materials Chemistry

Dataset Design for Building Models of Chemical Reactivity Models can codify our understanding of chemical reactivity and serve a useful purpose in the development of new synthetic processes via, for example, evaluating hypothetical reaction conditions or in silico substrate tolerance. Perhaps the most determining factor is the composition of the training data and whether it is sufficient to train a model that can make accurate predictions over the full domain of interest. Here, we discuss the design of reaction datasets in ways that are conducive to data-driven modeling, emphasizing the idea that training set diversity and model generalizability rely on the choice of molecular or reaction representation. We additionally discuss the experimental constraints associated with generating common types of chemistry datasets and how these considerations should influence dataset design and model building. Generalizability theory ; Computer science ; Representation (politics) ; Set (abstract data type) ; Training set ; Domain (mathematical analysis) ; Reactivity (psychology) ; Applicability domain ; Model building ; In silico ; Machine learning ; Biochemical engineering ; Data mining ; Artificial intelligence ; Data science ; Quantitative structure–activity relationship ; Chemistry ; Engineering ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Yuan Xu | Zhixin Zhou | Nankai Deng | Kangchun Fu | Caixia Zhu | Hong Qing | Yanfei Shen | Songqin Liu | Yuanjian Zhang
Source: Science China Chemistry (review, 2023-02-09)
Field: Materials Science — Materials Chemistry

Molecular insights of nanozymes from design to catalytic mechanism Biochemical engineering ; Mechanism (biology) ; Nanotechnology ; Computer science ; Chemistry ; Materials science ; Engineering ; Physics Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Electrochemical sensors and biosensors
Authors: Melissa J. Buskes | Aaron Coffin | Dawn M. Troast | Rachel Stein | María‐Jesús Blanco
Source: ACS Medicinal Chemistry Letters (review, 2023-03-08)
Field: Chemistry — Organic Chemistry

Accelerating Drug Discovery: Synthesis of Complex Chemotypes via Multicomponent Reactions The generation of multiple bonds in one reaction step has attracted massive interest in drug discovery and development. Multicomponent reactions (MCRs) offer the advantage of combining three or more reagents in a one-pot fashion to effectively yield a synthetic product. This approach significantly accelerates the synthesis of relevant compounds for biological testing. However, there is a perception that this methodology will only produce simple chemical scaffolds with limited use in medicinal chemistry. In this Microperspective, we want to highlight the value of MCRs toward the synthesis of complex molecules characterized by the presence of quaternary and chiral centers. This paper will cover specific examples showing the impact of this technology toward the discovery of clinical compounds and recent breakthroughs to expand the scope of the reactions toward topologically rich molecular chemotypes. Drug discovery ; Combinatorial chemistry ; Yield (engineering) ; Chemistry ; Computer science ; Nanotechnology ; Biochemical engineering ; Computational biology ; Biology ; Materials science ; Engineering ; Biochemistry Multicomponent Synthesis of Heterocycles ; Chemical Synthesis and Analysis ; Click Chemistry and Applications
Authors: Xue Guo | Jianxiu Zhang | Lei Han | Juliet Lee | Shoshana C. Williams | Allison P. Forsberg | Yan Xu | Rachel N. Austin | Liang Feng
Source: Nature Communications (article, 2023-04-17)
Field: Materials Science — Materials Chemistry

Structure and mechanism of the alkane-oxidizing enzyme AlkB Alkanes are the most energy-rich form of carbon and are widely dispersed in the environment. Their transformation by microbes represents a key step in the global carbon cycle. Alkane monooxygenase (AlkB), a membrane-spanning metalloenzyme, converts straight chain alkanes to alcohols in the first step of the microbially-mediated degradation of alkanes, thereby playing a critical role in the global cycling of carbon and the bioremediation of oil. AlkB biodiversity is attributed to its ability to oxidize alkanes of various chain lengths, while individual AlkBs target a relatively narrow range. Mechanisms of substrate selectivity and catalytic activity remain elusive. Here we report the cryo-EM structure of AlkB, which provides a distinct architecture for membrane enzymes. Our structure and functional studies reveal an unexpected diiron center configuration and identify molecular determinants for substrate selectivity. These findings provide insight into the catalytic mechanism of AlkB and shed light on its function in alkane-degrading microorganisms. AlkB ; Alkane ; Chemistry ; Bioremediation ; Monooxygenase ; Substrate (aquarium) ; Dioxygenase ; Combinatorial chemistry ; Enzyme ; Catalysis ; Bacteria ; Organic chemistry ; Biochemistry ; Biology ; Cytochrome P450 ; Gene ; Ecology ; Genetics Enzyme Structure and Function ; Metal-Catalyzed Oxygenation Mechanisms ; Microbial bioremediation and biosurfactants
Authors: Monika Kaurav | Sakina Ruhi | Husni Ahmed Al-Goshae | Ashok Kumar Jeppu | Dhani Ramachandran | Ram Kumar Sahu | Ashish Sarkar | Jiyauddin Khan | Abu Md Ashif Ikbal
Source: Frontiers in Pharmacology (review, 2023-03-16)
Field: Materials Science — Polymers and Plastics

Dendrimer: An update on recent developments and future opportunities for the brain tumors diagnosis and treatment A brain tumor is an uncontrolled cell proliferation, a mass of tissue composed of cells that grow and divide abnormally and appear to be uncontrollable by the processes that normally control normal cells. Approximately 25,690 primary malignant brain tumors are discovered each year, 70% of which originate in glial cells. It has been observed that the blood-brain barrier (BBB) limits the distribution of drugs into the tumour environment, which complicates the oncological therapy of malignant brain tumours. Numerous studies have found that nanocarriers have demonstrated significant therapeutic efficacy in brain diseases. This review, based on a non-systematic search of the existing literature, provides an update on the existing knowledge of the types of dendrimers, synthesis methods, and mechanisms of action in relation to brain tumours. It also discusses the use of dendrimers in the diagnosis and treatment of brain tumours and the future possibilities of dendrimers. Dendrimers are of particular interest in the diagnosis and treatment of brain tumours because they can transport biochemical agents across the BBB to the tumour and into the brain after systemic administration. Dendrimers are being used to develop novel therapeutics such as prolonged release of drugs, immunotherapy, and antineoplastic effects. The use of PAMAM, PPI, PLL and surface engineered dendrimers has proven revolutionary in the effective diagnosis and treatment of brain tumours. Dendrimer ; Nanocarriers ; Medicine ; Brain tumor ; Immunotherapy ; Cancer research ; Pathology ; Neuroscience ; Pharmacology ; Drug ; Biology ; Internal medicine ; Cancer Dendrimers and Hyperbranched Polymers ; RNA Interference and Gene Delivery ; Advanced biosensing and bioanalysis techniques
Authors: Anne Carolina Rodrigues Klaar | Stéfano Frizzo Stefenon | Laio Oriel Seman | Viviana Cocco Mariani | Leandro dos Santos Coelho
Source: Sensors (article, 2023-03-17)
Field: Materials Science — Materials Chemistry

Optimized EWT-Seq2Seq-LSTM with Attention Mechanism to Insulators Fault Prediction Insulators installed outdoors are vulnerable to the accumulation of contaminants on their surface, which raise their conductivity and increase leakage current until a flashover occurs. To improve the reliability of the electrical power system, it is possible to evaluate the development of the fault in relation to the increase in leakage current and thus predict whether a shutdown may occur. This paper proposes the use of empirical wavelet transform (EWT) to reduce the influence of non-representative variations and combines the attention mechanism with a long short-term memory (LSTM) recurrent network for prediction. The Optuna framework has been applied for hyperparameter optimization, resulting in a method called optimized EWT-Seq2Seq-LSTM with attention. The proposed model had a 10.17% lower mean square error (MSE) than the standard LSTM and a 5.36% lower MSE than the model without optimization, showing that the attention mechanism and hyperparameter optimization is a promising strategy. Hyperparameter ; Computer science ; Arc flash ; Artificial intelligence ; Mean squared error ; Machine learning ; Fault (geology) ; Electric power system ; Power (physics) ; Insulator (electricity) ; Engineering ; Statistics ; Mathematics High voltage insulation and dielectric phenomena ; Image Enhancement Techniques ; Power Transformer Diagnostics and Insulation
Authors: Xue Han | Sihai Yang⧫ | Martin Schröder
Source: Journal of the American Chemical Society (review, 2023-01-23)
Field: Chemistry — Inorganic Chemistry

Metal–Organic Framework Materials for Production and Distribution of Ammonia The efficient production of ammonia (NH<sub>3</sub>) from dinitrogen (N<sub>2</sub>) and water (H<sub>2</sub>O) using renewable energy is an important step on the roadmap to the ammonia economy. The productivity of this conversion hinges on the design and development of new active catalysts. In the wide scope of materials that have been examined as catalysts for the photo- and electro-driven reduction of N<sub>2</sub> to NH<sub>3</sub>, functional metal-organic framework (MOF) catalysts exhibit unique properties and appealing features. By elucidating their structural and spectroscopic properties and linking this to the observed activity of MOF-based catalysts, valuable information can be gathered to inspire new generations of advanced catalysts to produce green NH<sub>3</sub>. NH<sub>3</sub> is also a surrogate for the hydrogen (H<sub>2</sub>) economy, and the potential application of MOFs for the practical and effective capture, safe storage, and transport of NH<sub>3</sub> is also discussed. This Perspective analyzes the contribution that MOFs can make toward the ammonia economy. Chemistry ; Ammonia ; Ammonia production ; Production (economics) ; Metal ; Distribution (mathematics) ; Environmental chemistry ; Chemical engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Ammonia Synthesis and Nitrogen Reduction ; Advanced Photocatalysis Techniques
Authors: Mahyar Panahi‐Sarmad | Shakiba Samsami | Ahmadreza Ghaffarkhah | Seyyed Alireza Hashemi | Shayan Ghasemi | Majed Amini | Stefan Wuttke | Orlando J. Rojas | Kam Chiu Tam | Feng Jiang | Mohammad Arjmand | Farhad Ahmadijokani | Milad Kamkar
Source: Advanced Functional Materials (article, 2023-06-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

MOF‐Based Electromagnetic Shields Multiscale Design: Nanoscale Chemistry, Microscale Assembly, and Macroscale Manufacturing Abstract The effects of electromagnetic (EM) radiation have received increased attention, closely associated with the widespread use of electronics and wireless communication. A significant development in the area is the recent adoption of metal‐organic frameworks (MOFs) to effectively enable electromagnetic interference (EMI) shielding. MOF tunable molecular scaffold architecture offers numerous pathways to generate customizable magnetic and electrical properties, which are prerequisite materials characteristics for efficient EMI shielding performance. Their flexibility in terms of structural design, accompanied by high porosity and large specific surface area, makes MOFs excellent candidates to shield EM waves at multiple scales. Herein, the crucial role of molecular‐, nano‐, micro‐, and macro‐scale structural design is reviewed in accordance with the shielding performance of MOFs. The current design strategies of MOF‐based EMI shields are systematically outlined, and the shielding mechanisms are also expounded based on their structural features. The factors that hinder the widespread utilization of functional MOF‐derived EMI shields are also examined. Future research directions are unveiled for the rational design of the next‐generation MOF‐based EMI shields to address the pressing EM radiation concerns. Electromagnetic shielding ; EMI ; Shields ; Microscale chemistry ; Materials science ; Electromagnetic interference ; Flexibility (engineering) ; Nanotechnology ; Metamaterial ; Electronics ; Mechanical engineering ; Computer science ; Optoelectronics ; Electrical engineering ; Engineering ; Composite material ; Telecommunications Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; MXene and MAX Phase Materials
Authors: Dongxiao Chen | Cheng Shang | Zhi‐Pan Liu
Source: npj Computational Materials (article, 2023-01-07)
Field: Materials Science — Materials Chemistry

Machine-learning atomic simulation for heterogeneous catalysis Abstract Heterogeneous catalysis is at the heart of chemistry. New theoretical methods based on machine learning (ML) techniques that emerged in recent years provide a new avenue to disclose the structures and reaction in complex catalytic systems. Here we review briefly the history of atomic simulations in catalysis and then focus on the recent trend shifting toward ML potential calculations. The advanced methods developed by our group are outlined to illustrate how complex structures and reaction networks can be resolved using the ML potential in combination with efficient global optimization methods. The future of atomic simulation in catalysis is outlooked. Catalysis ; Computer science ; Biochemical engineering ; Nanotechnology ; Reaction conditions ; Chemistry ; Materials science ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Anjana Talapatra | Blas P. Uberuaga | Christopher R. Stanek | Ghanshyam Pilania
Source: Communications Materials (article, 2023-06-10)
Field: Materials Science — Materials Chemistry

Band gap predictions of double perovskite oxides using machine learning Abstract The compositional and structural variety inherent to oxide perovskites spawn wide-ranging applications. In perovskites, the band gap E g , a key material parameter for these applications, can be optimally controlled by varying the composition. Here, we implement a hierarchical screening process in which two cross-validated and predictive machine learning models for band gap classification and regression, trained using exhaustive datasets that span 68 elements of the periodic table, are applied sequentially. The classification model separates wide band gap materials, with E g ≥ 0.5 eV, from materials which have zero or relatively small band gaps, namely E g &lt; 0.5 eV, and the second regression model quantitatively predicts the gap value of the wide band gap compounds. The study down-selects 13,589 cubic oxide perovskite compositions that are predicted to be experimentally formable, thermodynamically stable, and have a wide band gap. Of these, a subset of 310 compounds, which are predicted to be stable and formable with a confidence greater than 90%, are identified for further investigation. Our models are methodically analyzed via performance metrics and inter-dependence of model features to gain physical insight into the band gap prediction problem. Design maps to identify the variation of band gap with substitution of different elements are also presented. Band gap ; Oxide ; Perovskite (structure) ; Electronic band structure ; Materials science ; Regression ; Machine learning ; Computer science ; Artificial intelligence ; Condensed matter physics ; Chemistry ; Mathematics ; Physics ; Optoelectronics ; Statistics ; Crystallography Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Thermoelectric Materials and Devices
Authors: Giuseppina Gullifa | L. Barone | Elena Papa | Alessandro Giuffrida | Stefano Materazzi | Roberta Risoluti
Source: Frontiers in Chemistry (review, 2023-09-25)
Field: Chemistry — Analytical Chemistry

Portable NIR spectroscopy: the route to green analytical chemistry There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for <i>in situ</i> determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or <i>in situ</i> analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019-2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the "true green analytical chemistry" which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found. Chemometrics ; Computer science ; Robustness (evolution) ; Process engineering ; Spectrometer ; Biochemical engineering ; Sample (material) ; Field (mathematics) ; Sample preparation ; Near-infrared spectroscopy ; Nanotechnology ; Chemistry ; Materials science ; Engineering ; Mathematics ; Physics ; Machine learning ; Optics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Corentin Lefebvre | Johanna Klein | Hassan Khartabil | Jean-Charles Boisson | Éric Hénon
Source: Journal of Computational Chemistry (article, 2023-05-12)
Field: Chemistry — Physical and Theoretical Chemistry

IGMPlot: A program to identify, characterize, and quantify molecular interactions We describe the development and features of a program called IGMPlot, which is based on the independent gradient model (IGM) and its local descriptor <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>δ</mml:mi> <mml:mi>g</mml:mi></mml:mrow> </mml:math> . The IGM approach analyzes the gradient of the electron density (ED) in a molecular system to identify regions of space where chemical interactions take place. IGMPlot is intended for use by both experimental scientists and theoretical chemists. It is standalone software written in C++, with versions available for multiple platforms. Some key features are: probing and quantifying interactions between two given molecular fragments, determining bond strength (IBSI), estimating the atomic contributions to an intermolecular interaction and preparing data to build 2D and 3D representations of interaction regions. The software has been updated to include new features: critical point analysis of the ED, assessing ED asymmetry of a given bond (PDA) and a new descriptor called <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>q</mml:mi> <mml:mi>g</mml:mi></mml:mrow> </mml:math> designed to enhance the IGM- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>δ</mml:mi> <mml:mi>g</mml:mi></mml:mrow> </mml:math> analysis. The program can be found at: http://igmplot.univ-reims.fr. Computer science ; Software ; Key (lock) ; Intermolecular force ; Point (geometry) ; Theoretical computer science ; Physics ; Molecule ; Mathematics ; Programming language ; Quantum mechanics Crystallography and molecular interactions ; Advanced Chemical Physics Studies ; Chemical Thermodynamics and Molecular Structure