jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: João Marcos da Silveira | Hiroki Chikuma | Gentoku Takasao | Toru Wada | Patchanee Chammingkwan | Toshiaki Taniike
Source: ChemRxiv (preprint, 2023-12-05)
Field: Chemistry — Organic Chemistry

Deciphering the Role of Internal Donors in Shaping Heterogeneous Ziegler-Natta Catalysts Based on Non-Empirical Structural Determination The addition of internal donors (IDs) to the heterogeneous Ziegler-Natta catalyst for propylene polymerization is fundamental to obtain highly isotactic polypropylene, but its role in shaping the stereospecific structure of the catalyst is still not comprehended. In this work, we apply non-empirical structure determination, based on a genetic algorithm and density functional theory calculations, to ternary systems composing MgCl2, TiCl4, and ID. We found that the co-presence of TiCl4 and ID led to the preferential formation of specific surface motifs, involving sharp edges and concavities. This was achieved by concerted adsorption of chelating ID and octahedral TiCl4, and significantly promoted TiCl4 situated in stereospecific environments. The multiplicity of adsorption modes of IDs was found to have a direct consequence in the structural diversity, where phthalate exhibited far greater diversity than 1,3-diether. In conclusion, this study revealed the essentiality of ID-driven reconstruction of MgCl2 in understanding the structure and function of this catalyst. Natta ; Tacticity ; Catalysis ; Stereospecificity ; Polymerization ; Adsorption ; Octahedron ; Ternary operation ; Density functional theory ; Materials science ; Chemistry ; Computational chemistry ; Chemical engineering ; Polymer chemistry ; Crystallography ; Polymer ; Physical chemistry ; Organic chemistry ; Crystal structure ; Computer science Organometallic Complex Synthesis and Catalysis ; biodegradable polymer synthesis and properties ; Polymer crystallization and properties
Authors: Nathan Jiscoot | Evgeny A. Uslamin | Evgeny A. Pidko
Source: ChemRxiv (preprint, 2023-01-31)
Field: Materials Science — Materials Chemistry

Model-based evaluation and data requirements for parallel kinetic experimentation and data-driven reaction identification and optimization Recently there has been growing interest in implementing the high-throughput approach to access the dynamics of chemical processes across different fields. With an ever-increasing amount of data catalyzed by high-throughput experimentation, the development of fully-integrated workflows becomes crucial. These workflows should combine novel experimental tools and interpretation methods to convert the data into valuable information. To design feasible data-driven workflows it is necessary to estimate the value of information and balance it with the number of experiments and resources required. Basing this kind of workflow on actual physical models appears to be a more feasible strategy as compared to data-extensive empirical statistical methods. Here we show an algorithm that constructs and evaluates kinetic models of different complexity. The algorithm facilitates the evaluation of the experimental data quality and quantity requirements needed for reliable discovery of the rates driving the corresponding chemical models. The influence of the quality and quantity of data on the obtained results was indicated by the accuracy of the estimates of the kinetic parameters. We also show that this method can be used to find correct reaction scenarios directly from simulated kinetic data with little to no overfitting. Well-fitting models for theoretical data can then be used as a proxy for optimizing the underlying chemical systems. Taking real physical effects into account, this approach goes beyond: we show that with the kinetic models one can make a direct, unbiased, quantitative connection between kinetic data and the reaction scenario. Computer science ; Workflow ; Overfitting ; Data mining ; Throughput ; Identification (biology) ; Experimental data ; Machine learning ; Database ; Mathematics Machine Learning in Materials Science ; Scientific Computing and Data Management ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Muzamil Hussain Memon | Maria Mustafa | Zeeshan Ali
Source: Mehran University Research Journal of Engineering and Technology (article, 2023-12-30)
Field: Materials Science — Polymers and Plastics

Fabrication of low-cost and environmental-friendly EHD printable thin film nanocomposite triboelectric nanogenerator using household recyclable materials Humans generate massive amounts of plastic and electronic waste, which pollute our environment, particularly our water supplies, and cause fatal difficulties. In addition, the increased use of fossil fuels is wreaking havoc on the ecosystem. In order to solve these issues, we describe a simple, low-cost, and environmentally-friendly triboelectric nanogenerator (TENG) made of electronic waste and recycled plastic, and we add nanomaterial to improve power generation using biomechanical energy. The present investigation involves synthesizing carbon dots (CDs) nano-material through a single-step hydrothermal technique and CDs nano-material characterized via UV.Vis Spectroscopy. The proposed carbon dot-graphite nano composite-based TENGs (CGC-TENGs) are created by reusing dry cells (electronic waste) to obtain graphite, plastic bottles to obtain plastic, and synthesized CDs. CGC-TENGs manufactures a simple, low-cost, and environmentally friendly In-house quick and bulk fabrication printed electro hydrodynamics (EHD) electrospray process that uses less solvent and does not require specialist equipment or knowledge. Comparing fabricate TENG device results, in which CDs used produced high voltage (127.31 V)/current (107.12 μA), while not using CDs produced low voltage (95.23 V)/current (104.12 μA) at similar fabrication parameters, the size of the devices are 4.5 cm × 7 cm, and 15 N force applied. The CGC-TENG (δ) has maximum output performance and is thoroughly investigated using an open-circuit voltage of 171.30 V, a short circuit current of 111.39 μA, and a maximum output power density of 53.08 μW/cm2. CGC-TENG (δ) was used to power an electronic glucose monitoring device, and twenty-three blue light-emitting diodes (LEDs) to demonstrate its practical applications. The approach we propose produces renewable energy sources by reutilizing plastic waste and technological waste, providing a practical and sustainable path toward our goal of creating a green planet. Triboelectric effect ; Nanogenerator ; Materials science ; Environmentally friendly ; Fabrication ; Screen printing ; Nanotechnology ; Nanomaterials ; Graphite ; Nanocomposite ; Composite material Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Supercapacitor Materials and Fabrication
Authors: Dibyendu Mondal | Harrison M. Snodgrass | Christian A. Gomez | Jared C. Lewis
Source: ChemRxiv (preprint, 2023-04-09)
Field: Chemistry — Organic Chemistry

Non-Native Site-Selective Enzyme Catalysis The ability to a site-selectively modify equivalent functional groups in a molecule has the potential to streamline syntheses and increase product yields by lowering step counts. Enzymes catalyze site-selective transformations throughout primary and secondary metabolism, but leveraging this capability for non-native substrates and reactions requires a detailed understanding of the potential and limitations of enzyme catalysis and how these bounds can be extended by protein engineering. In this review, we discuss representative examples of site-selective enzyme catalysis involving functional group manipulation and C-H bond functionalization. We include illustrative examples of native catalysis, but our focus is on cases involving non-native substrates and reactions often using engineered enzymes. We then discuss the use of these enzymes for chemoenzymatic transformations and target-oriented synthesis and conclude with a survey of tools and techniques that could expand the scope of non-native site-selective enzyme catalysis. Enzyme ; Active site ; Combinatorial chemistry ; Chemistry ; Catalysis ; Enzyme catalysis ; Functional group ; Protein engineering ; Scope (computer science) ; Stereochemistry ; Biochemistry ; Organic chemistry ; Computer science Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Biochemical and Molecular Research
Authors: Jonathan Schwartz | Zichao Wendy Di | Yi Jiang | Jason Manassa | Jacob Pietryga | Yiwen Qian | Min Gee Cho | Jonathan L. Rowell | Huihuo Zheng | Richard D. Robinson | Junsi Gu | Alexey Kirilin | Steve Rozeveld | Peter Ercius | Jeffrey A. Fessler | Ting Xu | Mary Scott | Robert Hovden
Source: arXiv (Cornell University) (preprint, 2023-04-24)
Field: Materials Science — Surfaces, Coatings and Films

Imaging 3D Chemistry at 1 nm Resolution with Fused Multi-Modal Electron Tomography Measuring the three-dimensional (3D) distribution of chemistry in nanoscale matter is a longstanding challenge for metrological science. The inelastic scattering events required for 3D chemical imaging are too rare, requiring high beam exposure that destroys the specimen before an experiment completes. Even larger doses are required to achieve high resolution. Thus, chemical mapping in 3D has been unachievable except at lower resolution with the most radiation-hard materials. Here, high-resolution 3D chemical imaging is achieved near or below one nanometer resolution in a Au-Fe$_3$O$_4$ metamaterial, Co$_3$O$_4$ - Mn$_3$O$_4$ core-shell nanocrystals, and ZnS-Cu$_{0.64}$S$_{0.36}$ nanomaterial using fused multi-modal electron tomography. Multi-modal data fusion enables high-resolution chemical tomography often with 99\% less dose by linking information encoded within both elastic (HAADF) and inelastic (EDX / EELS) signals. Now sub-nanometer 3D resolution of chemistry is measurable for a broad class of geometrically and compositionally complex materials. Resolution (logic) ; Chemical imaging ; Nanoscopic scale ; Tomography ; Materials science ; Nanometre ; Nanomaterials ; Electron tomography ; Image resolution ; Optics ; Nanotechnology ; Chemistry ; Hyperspectral imaging ; Physics ; Transmission electron microscopy ; Scanning transmission electron microscopy ; Computer science Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Nuclear Physics and Applications
Authors: Buda Li | Hangbo Qi | Menglu Li | Xiaotao Zu | Liang Qiao | Haiyan Xiao
Source: Crystals (article, 2023-10-13)
Field: Materials Science — Materials Chemistry

The Effects of Chlorine Doping on the Mechanical Properties of Bi2O2Se In this work, first-principle calculations based on density functional theory are employed to investigate how chlorine doping influences the elastic moduli, ductility, and lattice thermal conductivity of Bi2O2Se, aiming to explore an effective method to improve its mechanical properties for its applications under thermal stress. Our findings reveal that chlorine(Cl) doping significantly affects the electronic structure and mechanical properties of Bi2O2Se. The electrons are distributed on the Fermi level, and the Cl-doped Bi2O2Se exhibits metal-like properties. In addition, Cl doping enhances the ductility and toughness of Bi2O2Se and reduces its lattice thermal conductivity. These results suggest that Cl doping is an effective approach for tuning the mechanical properties of Bi2O2Se. Materials science ; Doping ; Ductility (Earth science) ; Thermal conductivity ; Chlorine ; Electrical resistivity and conductivity ; Condensed matter physics ; Composite material ; Metallurgy ; Optoelectronics ; Creep Advanced Thermoelectric Materials and Devices ; MXene and MAX Phase Materials ; 2D Materials and Applications
Authors: Fatima A. Adam
Source: Egyptian Journal of Chemistry (article, 2023-10-02)
Field: Chemistry — Organic Chemistry

Purification of water contaminated with heavy metals, exemplified by lead cations, utilizing Fe2O3@Al2O3 nanocomposites. The poisoning of natural water resources by waste discharge is a significant problem, and the search for a solution to a problem of this magnitude is a matter of concern on a global scale. Novel Fe2O3@Al2O3 nanocomposite was synthesized via a simple, green, and eco-friendly method utilizing glucose as a capping material. Transmission electron microscopy (TEM) results for the synthesized Fe2O3@Al2O3 revealed a particle size ranging between 3.5 and 13.9 nm. The sorbent possessed a specific surface area of 113.46 m2 g-1 and was tested for removing heavy metals from water, exemplified by Pb2+. Fe2O3@Al2O3 was proven to be an excellent sorbent for Pb2+, with a qt value of 246.4 mg g-1 for a 2.0 h equilibrium time. The Pb2+ removal fitted a pseudo-second-order (PSO) model, and the Pb2+ sorption onto Fe2O3@Al2O3 was controlled via a liquid-film diffusion model (LFM), which implied an excellent sorbent–sorbate affinity. The equilibrium studies revealed a better agreement of Pb2+ sorption with the Freundlich isotherm model (FIM), while the thermodynamic results indicated spontaneous endothermic physisorption. Fe2O3@Al2O3 showed an average efficiency of 96.8% in treated seawater (SW) and groundwater (GW) spiked with Pb2+, and the sorbent activity after four cycles was 94.2% of its original efficiency, with an RSD of 5.19. Lead (geology) ; Nanocomposite ; Contaminated water ; Heavy metals ; Contamination ; Environmental chemistry ; Materials science ; Nanotechnology ; Chemistry ; Metallurgy ; Environmental science ; Geology ; Biology Nanomaterials for catalytic reactions
Authors: Jeff Guo | Philippe Schwaller
Source: ChemRxiv (preprint, 2023-05-22)
Field: Materials Science — Materials Chemistry

Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Design Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy desired objectives under minimal oracle evaluations (computational prediction or wet-lab experiment). This problem becomes more apparent when using oracles that can provide increased predictive accuracy but impose a significant cost. Molecular generative models have shown remarkable sample efficiency when coupled with reinforcement learn- ing, as demonstrated in the Practical Molecular Optimization (PMO) benchmark. Here, we propose a novel algorithm called Augmented Memory that combines data augmentation with experience replay. We show that scores obtained from oracle calls can be reused to update the model multiple times. We compare Augmented Memory to previously proposed algorithms and show significantly enhanced sample efficiency in an exploitation task and a drug discovery case study requiring both exploration and exploitation. Our method achieves a new state-of-the-art in the PMO benchmark which enforces a computational budget, and outperforms the previous best performing method on 19/23 tasks. Benchmark (surveying) ; Oracle ; Computer science ; Task (project management) ; Generative model ; Sample (material) ; Machine learning ; Generative grammar ; Reinforcement learning ; Sample complexity ; Artificial intelligence ; Programming language ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Data Classification
Authors: Chengwei Zhang | Igor Zhitomirsky
Source: Nanomaterials (article, 2023-05-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Effect of High-Energy Ball Milling, Capping Agents and Alkalizer on Capacitance of Nanostructured FeOOH Anodes This investigation is motivated by interest in nanostructured FeOOH anodes for aqueous asymmetric supercapacitors operating in Na<sub>2</sub>SO<sub>4</sub> electrolyte. The research goal is the fabrication of anodes with high active mass loading of 40 mg cm<sup>-2</sup>, high capacitance and low resistance. The influence of high-energy ball milling (HEBM), capping agents and alkalizer on the nanostructure and capacitive properties is investigated. HEBM promotes the crystallization of FeOOH, which results in capacitance reduction. Capping agents from the catechol family, such as tetrahydroxy-1,4-benzoquinone (THB) and gallocyanine (GC), facilitate the fabrication of FeOOH nanoparticles, eliminate the formation of micron size particles and allow the fabrication of anodes with enhanced capacitance. The analysis of testing results provided insight into the influence of the chemical structure of the capping agents on nanoparticle synthesis and dispersion. The feasibility of a conceptually new strategy for the synthesis of FeOOH nanoparticles is demonstrated, which is based on the use of polyethylenimine as an organic alkalizer-dispersant. The capacitances of materials prepared using different nanotechnology strategies are compared. The highest capacitance of 6.54 F cm<sup>-2</sup> is obtained using GC as a capping agent. The obtained electrodes are promising for applications as anodes for asymmetric supercapacitors. Materials science ; Capacitance ; Supercapacitor ; Fabrication ; Anode ; Nanoparticle ; Chemical engineering ; Ball mill ; Electrolyte ; Nanotechnology ; Polyethylenimine ; Nanostructure ; Electrode ; Metallurgy ; Chemistry Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Advancements in Battery Materials
Authors: Zixu Wang | Yangyang Chen | Xiucai Ye
Source: ChemRxiv (preprint, 2023-09-05)
Field: Materials Science — Materials Chemistry

DiffSeqMol: A Non-Autoregressive Diffusion-Based Approach for Molecular Sequence Generation and Optimization The application of deep generative models for molecular discovery has witnessed a significant surge in recent years. Currently, the field of molecular generation and molecular optimization is predominantly governed by autoregressive models regardless of how molecular data is represented. However, an emerging paradigm in the generation domain is diffusion models, which treat data non-autoregressively and has achieved significant breakthroughs in areas such as image generation. The potential and capability of diffusion models in molecular generation and optimization tasks remain largely unexplored. In order to investigate the potential applicability of diffusion models in the domain of molecular exploration, we proposed DiffSeqMol, a molecular sequence generation model, underpinned by diffusion process. DiffSeqMol distinguishes itself from traditional autoregressive methods by its capacity to draw samples from random noise and direct generating the entire molecule. Through experiment evaluations, we demonstrated that DiffSeqMol can achieve, even surpass, the performance of established state-of-the-art models on unconditional generation tasks and molecular optimization tasks. Autoregressive model ; Computer science ; Generative grammar ; Diffusion ; Domain (mathematical analysis) ; Artificial intelligence ; Mathematics ; Econometrics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Hiroyuki Sakai | Kenro Matsuda | Nobuaki Kikkawa | Seiji Kajita
Source:  (preprint, 2023-12-21)
Field: Materials Science — Materials Chemistry

Human Divergent Exploration Capacity for Material Design: A Comparison with Artificial Intelligence Applications of artificial intelligence (AI) to material design have attracted increasing attention in recent years. Although AI-aided material design holds great promise for some applications, whether it has surpassed human creativity remains uncertain. The aim of the current study was to compare the divergent exploration capacity of AI with that of humans on a material design task. Human participants were asked to find a high-performance lubricant molecule under searching conditions comparable to a state- of-the-art AI system. Results indicated that, on average, AI was able to find significantly better lubricant molecules. However, the best molecule found by AI fell short of the best molecule found by a human participant. Furthermore, the structural characteristics of the molecules found by AI and human participants differed significantly. These find- ings suggest that a state-of-the-art AI system is capable of surpassing human divergent exploration capacity in material design, as in other fields in which AI has advanced. Nevertheless, our results also demonstrate that human intelligence and AI can play complementary roles. This investigation opens up new possibilities for collaborative systems involving both AI and humans in material design. Human intelligence ; Creativity ; Lubricant ; Artificial intelligence ; Computer science ; Cognitive science ; Psychology ; Engineering ; Social psychology ; Mechanical engineering Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Design Education and Practice
Authors: Fran Adar
Source: Spectroscopy (article, 2023-12-01)
Field: Materials Science — Biomaterials

Raman Spectra Used to Understand the Origins of Banding in Spherulites The formation of spherulites in polymers is a well-known phenomenon; when the polymer is crystallized by cooling from the melt, crystal lamellae grow out from a nucleation site in a spherical pattern. If the material is annealed on a planar surface, and viewed between crossed polarizers in a microscope, a Maltese cross with a banding pattern is observed. Where the crystals grow in a direction not parallel to the polarizers, the sample lights up. Often banding of the lit regions is observed, and is believed to be due to rotations of the crystal lamellae around the growth direction. Because it is well known that polarized Raman spectra respond to crystal orientation, we thought it would be interesting to try to document the relationship between the banding behavior and Raman polarization/orientation behavior. In this column I will show results of such an investigation of spherulites of poly(hydroxybutyate-co-hydroxyhexanoate) (PHBHx) with varying composition. Raman spectroscopy ; Polarizer ; Materials science ; Nucleation ; Polarization Microscopy ; Crystal (programming language) ; Spherulite (polymer physics) ; Polarization (electrochemistry) ; Crystallography ; Polymer ; Microscopy ; Optics ; Birefringence ; Chemistry ; Composite material ; Physics ; Physical chemistry biodegradable polymer synthesis and properties ; Polymer crystallization and properties ; Microplastics and Plastic Pollution
Authors: Zheyong Fan | Keke Song | Rui Zhao | Jiahui Liu | Yanzhou Wang | Eric Lindgren | Yong Wang | Shunda Chen | Ke Xu | Ting Liang | Penghua Ying | Nan Xu | Zhiqiang Zhao | Jiuyang Shi | Junjie Wang | Shuang Lyu | Zezhu Zeng | Shirong Liang | Haikuan Dong | Ligang Sun | Yue Chen | Zhuhua Zhang | Wanlin Guo | Ping Qian | Jian Sun | Paul Erhart | Tapio Ala-Nissilä | Yanjing Su
Source: Research Square (preprint, 2023-12-08)
Field: Materials Science — Materials Chemistry

General-purpose machine-learned potential for 16 elemental metals and their alloys Generality ; Computer science ; Component (thermodynamics) ; Interpolation (computer graphics) ; Atom (system on chip) ; Computational science ; Artificial intelligence ; Thermodynamics ; Physics ; Parallel computing Machine Learning in Materials Science ; Ion-surface interactions and analysis ; Nuclear Materials and Properties