Structural Design for EMI Shielding: From Underlying Mechanisms to Common Pitfalls

Advanced Materials, Journal Year: 2024, Volume and Issue: 36(24)

Published: March 12, 2024

Abstract Modern human civilization deeply relies on the rapid advancement of cutting‐edge electronic systems that have revolutionized communication, education, aviation, and entertainment. However, electromagnetic interference (EMI) generated by digital poses a significant threat to society, potentially leading future crisis. While numerous efforts are made develop nanotechnological shielding mitigate detrimental effects EMI, there is limited focus creating absorption‐dominant solutions. Achieving EMI shields requires careful structural design engineering, starting from smallest components considering most effective wave attenuating factors. This review offers comprehensive overview structures, emphasizing critical elements design, mechanisms, limitations both traditional shields, common misconceptions about foundational principles science. systematic serves as scientific guide for designing structures prioritize absorption, highlighting an often‐overlooked aspect

Language: Английский

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Progress toward the computational discovery of new metal–organic framework adsorbents for energy applications

Nature Energy, Journal Year: 2024, Volume and Issue: 9(2), P. 121 - 133

Published: Jan. 9, 2024

Language: Английский

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Nanozymes for nanohealthcare

Nature Reviews Methods Primers, Journal Year: 2024, Volume and Issue: 4(1)

Published: May 30, 2024

Language: Английский

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ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs

ACS Central Science, Journal Year: 2023, Volume and Issue: 9(11), P. 2161 - 2170

Published: Nov. 10, 2023

We leveraged the power of ChatGPT and Bayesian optimization in development a multi-AI-driven system, backed by seven large language model-based assistants equipped with machine learning algorithms, that seamlessly orchestrates multitude research aspects chemistry laboratory (termed Research Group). Our approach accelerated discovery optimal microwave synthesis conditions, enhancing crystallinity MOF-321, MOF-322, COF-323 achieving desired porosity water capacity. In this human researchers gained assistance from these diverse AI collaborators, each unique role within environment, spanning strategy planning, literature search, coding, robotic operation, labware design, safety inspection, data analysis. Such comprehensive enables single researcher working concert to achieve productivity levels analogous those an entire traditional scientific team. Furthermore, reducing biases screening experimental conditions deftly balancing exploration exploitation parameters, our search precisely zeroed on pool 6 million significantly shortened time scale. This work serves as compelling proof concept for AI-driven revolution laboratory, painting future where becomes efficient collaborator, liberating us routine tasks focus pushing boundaries innovation.

Language: Английский

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Shaping the Water-Harvesting Behavior of Metal–Organic Frameworks Aided by Fine-Tuned GPT Models

Journal of the American Chemical Society, Journal Year: 2023, Volume and Issue: 145(51), P. 28284 - 28295

Published: Dec. 13, 2023

We construct a data set of metal-organic framework (MOF) linkers and employ fine-tuned GPT assistant to propose MOF linker designs by mutating modifying the existing structures. This strategy allows model learn intricate language chemistry in molecular representations, thereby achieving an enhanced accuracy generating structures compared with its base models. Aiming highlight significance design strategies advancing discovery water-harvesting MOFs, we conducted systematic variant expansion upon state-of-the-art MOF-303 utilizing multidimensional approach that integrates extension multivariate tuning strategies. synthesized series isoreticular aluminum termed Long-Arm MOFs (LAMOF-1 LAMOF-10), featuring bear various combinations heteroatoms their five-membered ring moiety, replacing pyrazole either thiophene, furan, or thiazole rings combination two. Beyond consistent robust architecture, as demonstrated permanent porosity thermal stability, LAMOF offers generalizable synthesis strategy. Importantly, these 10 LAMOFs establish new benchmarks for water uptake (up 0.64 g g-1) operational humidity ranges (between 13 53%), expanding diversity MOFs.

Language: Английский

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Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials

Advanced Energy Materials, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 10, 2024

Abstract This review highlights recent advances in machine learning (ML)‐assisted design of energy materials. Initially, ML algorithms were successfully applied to screen materials databases by establishing complex relationships between atomic structures and their resulting properties, thus accelerating the identification candidates with desirable properties. Recently, development highly accurate interatomic potentials generative models has not only improved robust prediction physical but also significantly accelerated discovery In past couple years, methods have enabled high‐precision first‐principles predictions electronic optical properties for large systems, providing unprecedented opportunities science. Furthermore, ML‐assisted microstructure reconstruction physics‐informed solutions partial differential equations facilitated understanding microstructure–property relationships. Most recently, seamless integration various platforms led emergence autonomous laboratories that combine quantum mechanical calculations, language models, experimental validations, fundamentally transforming traditional approach novel synthesis. While highlighting aforementioned advances, existing challenges are discussed. Ultimately, is expected fully integrate atomic‐scale simulations, reverse engineering, process optimization, device fabrication, empowering system design. will drive transformative innovations conversion, storage, harvesting technologies.

Language: Английский

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Machine Learning-Assisted Discovery of Propane-Selective Metal–Organic Frameworks

Journal of the American Chemical Society, Journal Year: 2024, Volume and Issue: 146(10), P. 6955 - 6961

Published: Feb. 29, 2024

Machine learning is gaining momentum in the prediction and discovery of materials for specific applications. Given abundance metal–organic frameworks (MOFs), computational screening existing MOFs propane/propylene (C3H8/C3H6) separation could be equally important developing new MOFs. Herein, we report a machine learning-assisted strategy C3H8-selective from CoRE MOF database. Among four algorithms applied learning, random forest (RF) algorithm displays highest degree accuracy. We experimentally verified identified top-performing (JNU-90) with its benchmark selectivity performance directly producing C3H6. Considering excellent hydrolytic stability, JNU-90 shows great promise energy-efficient C3H8/C3H6. This work may accelerate development challenging separations.

Language: Английский

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Image and data mining in reticular chemistry powered by GPT-4V

Digital Discovery, Journal Year: 2024, Volume and Issue: 3(3), P. 491 - 501

Published: Jan. 1, 2024

The integration of artificial intelligence into scientific research opens new avenues with the advent GPT-4V, a large language model equipped vision capabilities.

Language: Английский

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Accelerating materials language processing with large language models

Communications Materials, Journal Year: 2024, Volume and Issue: 5(1)

Published: Feb. 15, 2024

Abstract Materials language processing (MLP) can facilitate materials science research by automating the extraction of structured data from papers. Despite existence deep learning models for MLP tasks, there are ongoing practical issues associated with complex model architectures, extensive fine-tuning, and substantial human-labelled datasets. Here, we introduce use large models, such as generative pretrained transformer (GPT), to replace architectures prior strategic designs prompt engineering. We find that in-context GPT few or zero-shots provide high performance text classification, named entity recognition extractive question answering limited datasets, demonstrated various classes materials. These also help identify incorrect annotated data. Our GPT-based approach assist material scientists in solving knowledge-intensive even if they lack relevant expertise, offering guidelines applicable any domain. In addition, outcomes expected reduce workload researchers, manual labelling, producing an initial labelling set verifying human-annotations.

Language: Английский

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Accelerating the Discovery of Oxygen Reduction Electrocatalysts: High‐Throughput Screening of Element Combinations in Pt‐Based High‐Entropy Alloys

Angewandte Chemie International Edition, Journal Year: 2024, Volume and Issue: 63(37)

Published: June 27, 2024

The vast number of element combinations and the explosive growth composition space pose significant challenges to development high-entropy alloys (HEAs). Here, we propose a procedural research method aimed at accelerating discovery efficient electrocatalysts for oxygen reduction reaction (ORR) based on Pt-based quinary HEAs. begins with an library provided by large language model (LLM), combined microscale precursor printing pulse high-temperature synthesis techniques prepare multi-element combination HEA array in one step. Through high-throughput measurement using scanning electrochemical cell microscopy (SECCM), precise identification highly active exploration specific are achieved. Advantageous further validated practical electrocatalytic evaluations. contributions individual sites synergistic effects among elements such HEAs enhancing activity elucidated via density functional theory (DFT) calculations. This integrates experiments, catalyst validation, DFT calculations, providing new pathway materials field energy catalysis.

Language: Английский

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