jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shuxin Zhang | Jingyu Xiao | Geng Zhong | Tailin Xu | Xueji Zhang
Source: The Analyst (article, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

Design and application of dual-emission metal–organic framework-based ratiometric fluorescence sensors Metal-organic frameworks (MOFs) are novel inorganic-organic hybridized crystals with a wide range of applications. In the last twenty years, fluorescence sensing based on MOFs has attracted much attention. MOFs can exhibit luminescence from metal nodes, ligands or introduced guests, which provides an excellent fluorescence response in sensing. However, single-signal emitting MOFs are susceptible to interference from concentration, environment, and excitation intensity, resulting in poor accuracy. To overcome the shortcomings, dual-emission MOF-based ratiometric fluorescence sensors have been proposed and rapidly developed. In this review, we first introduce the luminescence mechanisms, synthetic methods, and detection mechanisms of dual-emission MOFs, highlight the strategies for constructing ratiometric fluorescence sensors based on dual-emission MOFs, and classify them into three categories: intrinsic dual-emission and single-emission MOFs with luminescent guests, and non-emission MOFs with other luminescent materials. Then, we summarize the recent advances in dual-emission MOF-based ratiometric fluorescence sensors in various analytical industries. Finally, we discuss the current challenges and prospects for the future development of these sensors. Fluorescence ; Dual (grammatical number) ; Metal ; Environmental chemistry ; Materials science ; Environmental science ; Nanotechnology ; Remote sensing ; Photochemistry ; Computer science ; Chemistry ; Optics ; Geology ; Physics ; Metallurgy ; Art Metal-Organic Frameworks: Synthesis and Applications ; Molecular Sensors and Ion Detection ; Advanced Nanomaterials in Catalysis
Authors: Honorine Lardeux | Valentina D’Atri | Davy Guillarme
Source: TrAC Trends in Analytical Chemistry (article, 2024-05-15)
Field: Chemistry — Spectroscopy

Recent advances and current challenges in hydrophilic interaction chromatography for the analysis of therapeutic oligonucleotides Therapeutic oligonucleotides (ONs) are a new generation of drugs that have captured the attention of the pharmaceutical industry over the past decade, as evidenced by the FDA approval of 14 ONs drugs since 2018. Although therapeutic ONs are complex to analyze, there is an unprecedented need for powerful analytical methods to serve this scope, and liquid chromatography coupled to mass spectrometry (LC-MS) is one of the leading analytical techniques. Ion-pairing reversed-phase liquid chromatography (IP-RPLC) is the gold standard for ON characterization, but hydrophilic interaction chromatography (HILIC) has clearly emerged as an alternative option. This review critically examines recent advances in HILIC-MS analytical developments for the analysis of therapeutic oligonucleotides and their impurities. In particular, the critical selection and optimization of the chromatographic conditions are discussed, together with the latest technological advances required for HILIC-MS coupling. Finally, several applications of HILIC-MS in the context of pharmaceutical analysis and bioanalysis are reported. Oligonucleotide ; Current (fluid) ; Hydrophilic interaction chromatography ; Chemistry ; Computational biology ; Chromatography ; Computer science ; Biology ; Engineering ; Biochemistry ; High-performance liquid chromatography ; DNA Analytical Chemistry and Chromatography ; Protein purification and stability ; Chromatography in Natural Products
Authors: Pan Wu | Tianyu Yu | Ming‐Jun Chen | Nan Kang | Mohamed El Mansori
Source: Advanced Functional Materials (article, 2024-03-04)
Field: Materials Science — Polymers and Plastics

Electrically/Magnetically Dual‐Driven Shape Memory Composites Fabricated by Multi‐Material Magnetic Field‐Assisted 4D Printing Abstract Shape memory polymers (SMPs) are smart materials that enable to transform back to their original shape from the deformed state when subjected to external stimuli. They have shown great potential used as sensors and actuators in diverse applications. However, current research on SMPs primarily focuses on the utilization of a single source of stimuli (e.g., electricity, magnetism, light, etc.), which heavily restricts their potential in complex circumstances. In this study, a novel approach is developed to fabricate multi‐layer electrically/magnetically dual‐driven shape memory composites (ML‐EMSMCs) based on a magnetic field‐assisted digital light processing (MF‐DLP) 4D printing technique. The fabricated ML‐EMSMCs contain alternating high electric conductive layers (up to 5.37 × 10 −3 S cm −1 ) and magnetic responsive layers (10.7 emu g −1 ), enabling Joule heat‐based and high‐frequency magnetic field induction‐based stimuli. Furthermore, the ML‐EMSMCs exhibited excellent shape memory behavior, good formability, and magnetic properties. The developed 4D printing techniques allows for the alignment of magnetic particles with a unidirectional magnetic field, significantly improving their shape recovery speed. The developed electrically/magnetically dual‐driven and photocurable SMP composites shed light on the development of actuators and sensors with multiple functionalities. Materials science ; Shape-memory polymer ; Actuator ; Magnetic field ; Shape-memory alloy ; Joule heating ; Composite material ; Magnetism ; Electrical conductor ; Digital Light Processing ; 3D printing ; Optoelectronics ; Smart material ; Nanotechnology ; Mechanical engineering ; Computer science Polymer composites and self-healing ; Advanced Materials and Mechanics ; Advanced Sensor and Energy Harvesting Materials
Authors: Kha-Dinh Luong | Ambuj K. Singh
Source: Journal of Chemical Information and Modeling (article, 2024-05-30)
Field: Materials Science — Materials Chemistry

Application of Transformers in Cheminformatics By accelerating time-consuming processes with high efficiency, computing has become an essential part of many modern chemical pipelines. Machine learning is a class of computing methods that can discover patterns within chemical data and utilize this knowledge for a wide variety of downstream tasks, such as property prediction or substance generation. The complex and diverse chemical space requires complex machine learning architectures with great learning power. Recently, learning models based on transformer architectures have revolutionized multiple domains of machine learning, including natural language processing and computer vision. Naturally, there have been ongoing endeavors in adopting these techniques to the chemical domain, resulting in a surge of publications within a short period. The diversity of chemical structures, use cases, and learning models necessitate a comprehensive summarization of existing works. In this paper, we review recent innovations in adapting transformers to solve learning problems in chemistry. Because chemical data is diverse and complex, we structure our discussion based on chemical representations. Specifically, we highlight the strengths and weaknesses of each representation, the current progress of adapting transformer architectures, and future directions. Computer science ; Chemical space ; Automatic summarization ; Cheminformatics ; Artificial intelligence ; Machine learning ; Transformer ; Data science ; Drug discovery ; Chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Lingaraj Pradhan | Bishnupad Mohanty | Ganeswara Padhy | Ravi Trivedi | Debi Prasad Das | Brahmananda Chakraborty | Bikash Kumar Jena
Source: Chemical Engineering Journal (article, 2024-08-05)
Field: Materials Science — Materials Chemistry

Supercapacitor properties of partially oxidised-MXene quantum dots/graphene hybrids: Fabrication of flexible/wearable micro-supercapacitor devices Supercapacitor ; Graphene ; Materials science ; Capacitance ; Cyclic voltammetry ; Nanotechnology ; Energy storage ; Fabrication ; Power density ; Composite number ; Quantum dot ; Quantum capacitance ; Chemical engineering ; Electrochemistry ; Electrode ; Composite material ; Chemistry ; Power (physics) ; Physics MXene and MAX Phase Materials ; Supercapacitor Materials and Fabrication ; Graphene research and applications
Authors: Lila A. M. Mahmoud | Jemma Rowlandson | David J. Fermı́n | Valeska P. Ting | Sanjit Nayak
Source: RSC Applied Interfaces (article, 2024-10-30)
Field: Materials Science — Materials Chemistry

Porous carbons: a class of nanomaterials for efficient adsorption-based hydrogen storage This review provides an overview of porous carbon materials and their hydrogen storage properties with insight on strategic improvement by structural and chemical modifications and current trends of using machine learning and lifecycle assessment. Hydrogen storage ; Nanomaterials ; Class (philosophy) ; Materials science ; Nanotechnology ; Porosity ; Chemical engineering ; Hydrogen ; Chemistry ; Computer science ; Organic chemistry ; Engineering ; Composite material ; Artificial intelligence Hydrogen Storage and Materials ; Supercapacitor Materials and Fabrication ; Catalysis and Hydrodesulfurization Studies
Authors: Amir Sheikhmohammadi | Hassan Alamgholiloo | Mohammad Golaki | Parsa Khakzad | Esrafil Asgari | Faezeh Rahimlu
Source: Scientific Reports (article, 2024-06-15)
Field: Materials Science — Materials Chemistry

Cefixime removal via WO3/Co-ZIF nanocomposite using machine learning methods In this research, an upgraded and environmentally friendly process involving WO<sub>3</sub>/Co-ZIF nanocomposite was used for the removal of Cefixime from the aqueous solutions. Intelligent decision-making was employed using various models including Support Vector Regression (SVR), Genetic Algorithm (GA), Artificial Neural Network (ANN), Simulation Optimization Language for Visualized Excel Results (SOLVER), and Response Surface Methodology (RSM). SVR, ANN, and RSM models were used for modeling and predicting results, while GA and SOLVER models were employed to achieve the optimal conditions for Cefixime degradation. The primary goal of applying different models was to achieve the best conditions with high accuracy in Cefixime degradation. Based on R analysis, the quadratic factorial model in RSM was selected as the best model, and the regression coefficients obtained from it were used to evaluate the performance of artificial intelligence models. According to the quadratic factorial model, interactions between pH and time, pH and catalyst amount, as well as reaction time and catalyst amount were identified as the most significant factors in predicting results. In a comparison between the different models based on Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R<sup>2</sup> Score) indices, the SVR model was selected as the best model for the prediction of the results, with a higher R<sup>2</sup> Score (0.98), and lower MAE (1.54) and RMSE (3.91) compared to the ANN model. Both ANN and SVR models identified pH as the most important parameter in the prediction of the results. According to the Genetic Algorithm, interactions between the initial concentration of Cefixime with reaction time, as well as between the initial concentration of Cefixime and catalyst amount, had the greatest impact on selecting the optimal values. Using the Genetic Algorithm and SOLVER models, the optimum values for the initial concentration of Cefixime, pH, time, and catalyst amount were determined to be (6.14 mg L<sup>-1</sup>, 3.13, 117.65 min, and 0.19 g L<sup>-1</sup>) and (5 mg L<sup>-1</sup>, 3, 120 min, and 0.19 g L<sup>-1</sup>), respectively. Given the presented results, this research can contribute significantly to advancements in intelligent decision-making and optimization of the pollutant removal processes from the environment. Environmentally friendly ; Nanocomposite ; Cefixime ; Process (computing) ; Computer science ; Materials science ; Chemical engineering ; Process engineering ; Nanotechnology ; Chemistry ; Engineering ; Biology Advanced Nanomaterials in Catalysis ; Gas Sensing Nanomaterials and Sensors ; Biosensors and Analytical Detection
Authors: Marziyeh Nazari | Farnaz Zadehahmadi | Muhammad Sadiq | Ashley L. Sutton | Hamidreza Mahdavi | Matthew R. Hill
Source: Communications Materials (article, 2024-08-29)
Field: Chemistry — Inorganic Chemistry

Challenges and solutions to the scale-up of porous materials With increasing pace, crystalline open frameworks are moving to larger scale, mature applications that stretch as broadly as catalysis, separation, water purification, adsorption, sensing, biomineralization and energy storage. A particular challenge in this development can be the unexpected variation in material properties from batch to batch, even when a cursory analysis would indicate that no process changes occurred. Our team has lived this journey in many larger projects where pilot scale production of metal-organic frameworks for use in devices has been a key milestone and suffered the difficulties of unexpected performance departures. In this Perspective, we aim to share some of the learning outcomes in the hope that it will further speed development in the field. A major challenge in materials scale-up is the variation in properties between batches. Here, the difficulties in the pilot-scale production of metal-organic frameworks are discussed and suggestions are provided to help improve large-scale synthesis development. Scale (ratio) ; Porosity ; Materials science ; Environmental science ; Composite material ; Geography ; Cartography Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Covalent Organic Framework Applications
Authors: Hailu Dai | Samir Boulfrad | Xinrui Chu | Yueyuan Gu | Lei Bi | Qinfang Zhang
Source: Journal of Advanced Ceramics (article, 2024-09-18)
Field: Materials Science — Materials Chemistry

La <sub>0.5− <i>x</i> </sub>Sc <sub> <i>x</i> </sub>Sr <sub>0.5</sub>MnO <sub>3− <i>δ</i> </sub> cathodes for proton-conducting solid oxide fuel cells: Taking advantage of the secondary phase Designing high-performance cathodes is crucial for proton-conducting solid oxide fuel cells (H-SOFCs), as the cathode heavily influences cell performance. Although manganate cathodes exhibit superior stability and thermal compatibility, their poor cathode performance at intermediate temperatures renders them unsuitable for H-SOFC applications. To address this issue, Sc is utilized as a dopant to modify the traditional La<sub>0.5</sub>Sr<sub>0.5</sub>MnO<sub>3</sub> cathode at the La site. Although the solubility of Sc at the La site is restricted to 2.5%, this modest quantity of Sc doping can improve the material's oxygen and proton transport capabilities, hence improving cathode and fuel cell performance. Furthermore, when the doping concentration exceeds 2.5%, the secondary phase ScMnO<sub>3</sub> forms <i>in situ</i>, resulting in La<sub>0.475</sub>Sc<sub>0.025</sub>Sr<sub>0.5</sub>MnO<sub>3</sub> (LScSM)+ScMnO<sub>3</sub> nanocomposites. Although the secondary phase is often considered undesirable, the high protonation capacity of ScMnO<sub>3</sub> can compensate for the low proton diffusion ability of LScSM. These two phases complement each other to provide high-performance cathodes. The nominal La<sub>0.4</sub>Sc<sub>0.1</sub>Sr<sub>0.5</sub>MnO<sub>3</sub> is the optimal composition, which takes advantage of the excellent electronic conductivity and fast oxygen diffusion rates of LScSM, as well as the good proton diffusion capacity of ScMnO<sub>3</sub>, to produce a high fuel cell output of 1529 mW·cm<sup>−2</sup> at 700 °C. Furthermore, the fuel cell exhibited good operational stability under working conditions, indicating that La<sub>0.4</sub>Sc<sub>0.1</sub>Sr<sub>0.5</sub>MnO<sub>3</sub> is a viable cathode choice for H-SOFCs. Cathode ; Materials science ; X-ray crystallography ; Crystallography ; Mineralogy ; Physics ; Chemistry ; Physical chemistry ; Diffraction ; Optics Advancements in Solid Oxide Fuel Cells ; Magnetic and transport properties of perovskites and related materials ; Electronic and Structural Properties of Oxides
Authors: Tao Li | Yongyi Wu | Guoliang Yu | Shengxian Li | Yifeng Ren | Yadong Liu | Jiarui Liu | Hao Feng | Yu Deng | Mingxing Chen | Zhenyu Zhang | Tai Min
Source: Nature Communications (article, 2024-03-26)
Field: Materials Science — Materials Chemistry

Realization of sextuple polarization states and interstate switching in antiferroelectric CuInP2S6 Realization of higher-order multistates with mutual interstate switching in ferroelectric materials is a perpetual drive for high-density storage devices and beyond-Moore technologies. Here we demonstrate experimentally that antiferroelectric van der Waals CuInP<sub>2</sub>S<sub>6</sub> films can be controllably stabilized into double, quadruple, and sextuple polarization states, and a system harboring polarization order of six is also reversibly tunable into order of four or two. Furthermore, for a given polarization order, mutual interstate switching can be achieved via moderate electric field modulation. First-principles studies of CuInP<sub>2</sub>S<sub>6</sub> multilayers help to reveal that the double, quadruple, and sextuple states are attributable to the existence of respective single, double, and triple ferroelectric domains with antiferroelectric interdomain coupling and Cu ion migration. These findings offer appealing platforms for developing multistate ferroelectric devices, while the underlining mechanism is transformative to other non-volatile material systems. Antiferroelectricity ; Ferroelectricity ; Polarization (electrochemistry) ; Realization (probability) ; Condensed matter physics ; Multiferroics ; Materials science ; Electric field ; Optoelectronics ; Physics ; Chemistry ; Nanotechnology ; Chemical physics ; Quantum mechanics ; Dielectric Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Perovskite Materials and Applications
Authors: Faizan Ali | Ali Razzaq | Waheed Tariq | Akhtar Hameed | Abdul Rehman | Khizar Razzaq | Sohaib Sarfraz | Nasir Ahmed Rajput | Haitham E. M. Zaki | Muhammad Shafiq Shahid | Gabrijel Ondrašek
Source: Agronomy (article, 2024-09-30)
Field: Chemistry — Analytical Chemistry

Spectral Intelligence: AI-Driven Hyperspectral Imaging for Agricultural and Ecosystem Applications Ensuring global food security amid mounting challenges, such as population growth, disease infestations, resource limitations, and climate change, is a pressing concern. Anticipated increases in food demand add further complexity to this critical issue. Plant pathogens, responsible for substantial crop losses (up to 41%) in major crops like wheat, rice, maize, soybean, and potato, exacerbate the situation. Timely disease detection is crucial, yet current practices often identify diseases at advanced stages, leading to severe infestations. To address this, remote sensing and Hyperspectral imaging (HSI) have emerged as robust and nondestructive techniques, exhibiting promising results in early disease identification. Integrating machine learning algorithms with image data sets enables precise spatial–temporal disease identification, facilitating timely detection, predictive modeling, and effective disease management without compromising fitness or climate adaptability. By harnessing these cutting-edge technologies and data-driven decision-making, growers can optimize input costs while achieving enhanced yields, making significant strides toward global food security in the face of climate change risks. This review will discuss some of the foundational concepts of remote sensing, several platforms used for remote sensing data collection, successful application of the approach, and its future perspective. Hyperspectral imaging ; Ecosystem ; Imaging spectrometer ; Remote sensing ; Agriculture ; Environmental science ; Ecology ; Geography ; Biology ; Spectrometer Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Florian Knoop | Nina Shulumba | Aloïs Castellano | J. P. Alvarinhas Batista | Roberta Farris | Matthieu J. Verstraete | Matthew Heine | David Broido | Dennis Kim | Johan Klarbring | Igor A. Abrikosov | S. I. Simak | Olle Hellman
Source: The Journal of Open Source Software (article, 2024-02-01)
Field: Materials Science — Materials Chemistry

TDEP: Temperature Dependent EffectivePotentials The Temperature Dependent Effective Potential (TDEP) method is a versatile and efficient approach to include temperature in a binitio materials simulations based on phonon theory. TDEP can be used to describe thermodynamic properties in classical and quantum ensembles, and several response properties ranging from thermal transport to Neutron and Raman spectroscopy. A stable and fast reference implementation is given in the software package of the same name described here. The underlying theoretical framework and foundation is briefly sketched with an emphasis on discerning the conceptual difference between bare and effective phonon theory, in both self-consistent and non-self-consistent formulations. References to numerous applications and more in-depth discussions of the theory are given. Materials science Thermal properties of materials ; Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics
Authors: Rakhi Chandoliya | Shivika Sharma | Vikas Sharma | Rohit Joshi | Iyyakkannu Sivanesan
Source: Plants (review, 2024-10-23)
Field: Materials Science — Materials Chemistry

Titanium Dioxide Nanoparticle: A Comprehensive Review on Synthesis, Applications and Toxicity Nanotechnology has garnered significant interest worldwide due to its wide-ranging applications across various industries. Titanium dioxide nanoparticles are one type of nanoparticle that is commonly utilised in everyday use and can be synthesized by different techniques using physical, chemical and biological extracts. Green synthesis is an economical, environmentally benign and non-toxic method of synthesising nanoparticles. Titanium dioxide nanoparticles have a positive impact on plant physiology, particularly in response to biotic and abiotic stresses, depending on various factors like size, concentration, exposure of the nanoparticles and other variables. Further, titanium dioxide nanoparticles have many applications, such as being used as nano-fertilizers, adsorption of heavy metal from industrial wastewater and antimicrobial activity, as discussed in this review paper. Previous studies investigated whether titanium dioxide nanoparticles also induce genotoxicity may be due to mishandling procedure, exposure time, size, concentration and other variables. This is still contradictory and requires more research. The present review is a pragmatic approach to summarize the synthesis, application, nanotoxicity, genotoxicity and eco-friendly method of nanoparticle synthesis and disposable. Titanium dioxide ; Nanoparticle ; Nanotoxicology ; Genotoxicity ; Nanotechnology ; Nanomaterials ; Materials science ; Titanium ; Chemical engineering ; Biochemical engineering ; Environmental chemistry ; Chemistry ; Toxicity ; Metallurgy ; Organic chemistry ; Engineering Nanoparticles: synthesis and applications ; TiO2 Photocatalysis and Solar Cells ; Advanced Photocatalysis Techniques