Graphene is a two-dimensional material that offers a unique combination of low density, exceptional mechanical properties, large surface area and excellent electrical conductivity. Recent progress ...has produced bulk 3D assemblies of graphene, such as graphene aerogels, but they possess purely stochastic porous networks, which limit their performance compared with the potential of an engineered architecture. Here we report the fabrication of periodic graphene aerogel microlattices, possessing an engineered architecture via a 3D printing technique known as direct ink writing. The 3D printed graphene aerogels are lightweight, highly conductive and exhibit supercompressibility (up to 90% compressive strain). Moreover, the Young's moduli of the 3D printed graphene aerogels show an order of magnitude improvement over bulk graphene materials with comparable geometric density and possess large surface areas. Adapting the 3D printing technique to graphene aerogels realizes the possibility of fabricating a myriad of complex aerogel architectures for a broad range of applications.
Graphene is an atomically thin, two-dimensional (2D) carbon material that offers a unique combination of low density, exceptional mechanical properties, thermal stability, large surface area, and ...excellent electrical conductivity. Recent progress has resulted in macro-assemblies of graphene, such as bulk graphene aerogels for a variety of applications. However, these three-dimensional (3D) graphenes exhibit physicochemical property attenuation compared to their 2D building blocks because of one-fold composition and tortuous, stochastic porous networks. These limitations can be offset by developing a graphene composite material with an engineered porous architecture. Here, we report the fabrication of 3D periodic graphene composite aerogel microlattices for supercapacitor applications, via a 3D printing technique known as direct-ink writing. The key factor in developing these novel aerogels is creating an extrudable graphene oxide-based composite ink and modifying the 3D printing method to accommodate aerogel processing. The 3D-printed graphene composite aerogel (3D-GCA) electrodes are lightweight, highly conductive, and exhibit excellent electrochemical properties. In particular, the supercapacitors using these 3D-GCA electrodes with thicknesses on the order of millimeters display exceptional capacitive retention (ca. 90% from 0.5 to 10 A·g–1) and power densities (>4 kW·kg–1) that equal or exceed those of reported devices made with electrodes 10–100 times thinner. This work provides an example of how 3D-printed materials, such as graphene aerogels, can significantly expand the design space for fabricating high-performance and fully integrable energy storage devices optimized for a broad range of applications.
Abstract
Despite ML’s impressive performance in commercial applications, several unique challenges exist when applying ML in materials science applications. In such a context, the contributions of ...this work are twofold. First, we identify common pitfalls of existing ML techniques when learning from underrepresented/imbalanced material data. Specifically, we show that with imbalanced data, standard methods for assessing quality of ML models break down and lead to misleading conclusions. Furthermore, we find that the model’s own confidence score cannot be trusted and model introspection methods (using simpler models) do not help as they result in loss of predictive performance (reliability-explainability trade-off). Second, to overcome these challenges, we propose a general-purpose explainable and reliable machine-learning framework. Specifically, we propose a generic pipeline that employs an ensemble of simpler models to reliably predict material properties. We also propose a transfer learning technique and show that the performance loss due to models’ simplicity can be overcome by exploiting correlations among different material properties. A new evaluation metric and a trust score to better quantify the confidence in the predictions are also proposed. To improve the interpretability, we add a rationale generator component to our framework which provides both model-level and decision-level explanations. Finally, we demonstrate the versatility of our technique on two applications: (1) predicting properties of crystalline compounds and (2) identifying potentially stable solar cell materials. We also point to some outstanding issues yet to be resolved for a successful application of ML in material science.
Low-density metal foams have many potential applications in electronics, energy storage, catalytic supports, fuel cells, sensors, and medical devices. Here, we report a new method for fabricating ...ultralight, conductive silver aerogel monoliths with predictable densities using silver nanowires. Silver nanowire building blocks were prepared by polyol synthesis and purified by selective precipitation. Silver aerogels were produced by freeze-casting nanowire aqueous suspensions followed by thermal sintering to weld the nanowire junctions. As-prepared silver aerogels have unique anisotropic microporous structures, with density precisely controlled by the nanowire concentration, down to 4.8 mg/cm3 and an electrical conductivity up to 51 000 S/m. Mechanical studies show that silver nanowire aerogels exhibit “elastic stiffening” behavior with a Young’s modulus up to 16 800 Pa.
Background
Several studies have demonstrated that allergen‐specific immunotherapy (SIT) can be an effective treatment for atopic dermatitis (AD). However, there is no relevant mouse model to ...investigate the mechanism and validate the novel modality of SIT in AD.
Methods
NC/Nga mice with induced AD‐like skin lesions received a subcutaneous injection of SIT (an extract of the house dust mite Dermatophagoides farinae DfE) or placebo for 5 weeks). Clinical and histological improvements of AD‐like skin lesions were examined. The responses of local and systemic regulatory T (Treg) cells, natural killer (NK) cells, B cells, serum immunoglobulin, and T‐cell cytokine response to DfE were evaluated to determine the underlying mechanism of the observed results.
Results
Specific immunotherapy significantly improved AD‐like skin lesions. Histologically, SIT decreased epidermal thickness and reduced inflammatory cell infiltration, especially that of eosinophils. Concomitantly, SIT suppressed Th2 responses and induced local infiltration of Treg cells into the skin. Also, SIT induced the immunoglobulin G4 and attenuated allergen‐specific immunoglobulin E. Furthermore, SIT induced local and systemic IL‐10‐producing Treg cells and regulatory NK cells.
Conclusion
We established a SIT model on AD mice and showed that our model correlates well with previous reports about SIT‐treated patients. Also, we revealed NK cells as another possible resource of IL‐10 in SIT. Based on our results, we suggest our SIT model as a useful tool to investigate mechanism of action of SIT and to validate the efficacy of new SIT modalities for the treatment of AD.
To expedite new molecular compound development, a long-sought goal within the chemistry community has been to predict molecules’ bulk properties of interest a priori to synthesis from a chemical ...structure alone. In this work, we demonstrate that machine learning methods can indeed be used to directly learn the relationship between chemical structures and bulk crystalline properties of molecules, even in the absence of any crystal structure information or quantum mechanical calculations. We focus specifically on a class of organic compounds categorized as energetic materials called high explosives (HE) and predicting their crystalline density. An ongoing challenge within the chemistry machine learning community is deciding how best to featurize molecules as inputs into machine learning modelswhether expert handcrafted features or learned molecular representations via graph-based neural network modelsyield better results and why. We evaluate both types of representations in combination with a number of machine learning models to predict the crystalline densities of HE-like molecules curated from the Cambridge Structural Database, and we report the performance and pros and cons of our methods. Our message passing neural network (MPNN) based models with learned molecular representations generally perform best, outperforming current state-of-the-art methods at predicting crystalline density and performing well even when testing on a data set not representative of the training data. However, these models are traditionally considered black boxes and less easily interpretable. To address this common challenge, we also provide a comparison analysis between our MPNN-based model and models with fixed feature representations that provides insights as to what features are learned by the MPNN to accurately predict density.
Abstract
Machine learning models are increasingly used in materials studies because of their exceptional accuracy. However, the most accurate machine learning models are usually difficult to explain. ...Remedies to this problem lie in explainable artificial intelligence (XAI), an emerging research field that addresses the explainability of complicated machine learning models like deep neural networks (DNNs). This article attempts to provide an entry point to XAI for materials scientists. Concepts are defined to clarify what explain means in the context of materials science. Example works are reviewed to show how XAI helps materials science research. Challenges and opportunities are also discussed.
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