Current Opinion in Solid State & Materials Science最新文献

Metal additive manufacturing is a disruptive technology that is revolutionizing the manufacturing industry. Despite its unrivaled capability for directly fabricating metal parts with complex geometries, the wide realization of the technology is currently limited by microstructural defects and anomalies, which could significantly degrade the structural integrity and service performance of the product. Accurate detection, characterization, and prediction of these defects and anomalies have an important and immediate impact in manufacturing fully-dense and defect-free builds. This review seeks to elucidate common defects/anomalies and their formation mechanisms in powder bed fusion additive manufacturing processes. They could arise from raw materials, processing conditions, and post-processing. While defects/anomalies in laser welding have been studied extensively, their formation and evolution remain unclear. Additionally, the existence of powder in powder bed fusion techniques may generate new types of defects, e.g., porosity transferring from powder to builds. Practical strategies to mitigate defects are also addressed through fundamental understanding of their formation. Such explorations enable the validation and calibration of models and ease the process qualification without costly trial-and-error experimentation.

Nuclear materials are often demanded to function for extended time in extreme environments, including high radiation fluxes with associated transmutations, high temperature and temperature gradients, mechanical stresses, and corrosive coolants. They also have a wide range of microstructural and chemical makeups, resulting in multifaceted and often out-of-equilibrium interactions. Machine learning (ML) is increasingly being used to tackle these complex time-dependent interactions and aid researchers in developing models and making predictions, sometimes with better accuracy than traditional modeling that focuses on one or two parameters at a time. Conventional practices of acquiring new experimental data in nuclear materials research are often slow and expensive, limiting the opportunity for data-centric ML, but new methods are changing that paradigm. Here we review high-throughput computational and experimental data approaches, especially robotic experimentation and active learning that is based on Gaussian process and Bayesian optimization. We show ML examples in structural materials (e.g., reactor pressure vessel (RPV) alloys and radiation detecting scintillating materials) and highlight new techniques of high-throughput sample preparation and characterizations, and automated radiation/environmental exposures and real-time online diagnostics. This review suggests that ML models of material constitutive relations in plasticity, damage, and even electronic and optical responses to radiation are likely to become powerful tools as they develop. Finally, we speculate on how the recent trends of using natural language processing (NLP) to aid the collection and analysis of literature data, interpretable artificial intelligence (AI), and the use of streamlined scripting, database, workflow management, and cloud computing platforms that will soon make the utilization of ML techniques as commonplace as the spreadsheet curve-fitting practices of today.

Upon insertion and extraction of lithium, materials important for electrochemical energy storage can undergo changes in thermal conductivity (Λ) and elastic modulus (M). These changes are attributed to evolution of the intrinsic thermal carrier lifetime and interatomic bonding strength associated with structural transitions of electrode materials with varying degrees of reversibility. Using in situ time-domain thermoreflectance (TDTR) and picosecond acoustics, we systemically study Λ and M of conversion, intercalation and alloying electrode materials during cycling. The intercalation V₂O₅ and TiO₂ exhibit non-monotonic reversible Λ and M switching up to a factor of 1.8 (Λ) and 1.5 (M) as a function of lithium content. The conversion Fe₂O₃ and NiO undergo irreversible decays in Λ and M upon the first lithiation. The alloying Sb shows the largest and partially reversible order of the magnitude switching in Λ between the delithiated (18 W m⁻¹ K⁻¹) and lithiated states (<1 W m⁻¹ K⁻¹). The irreversible Λ is attributed to structural degradation and pulverization resulting from substantial volume changes during cycling. These findings provide new understandings of the thermal and mechanical property evolution of electrode materials during cycling of importance for battery design, and also point to pathways for forming materials with thermally switchable properties.

Extremely low pH requirement and additional sludge management for the homogeneous electro-Fenton (EF) process necessitated the development of heterogeneous electro-Fenton (HEF) reactions that utilize solid catalysts that can be recovered and reused. In the recent decades, supported catalysts have immensely attracted researchers owing to the outstanding physical, chemical, and electronic properties of the supports that benefit the EF process by enhancing the removal efficiency, reducing reaction time, and extending the operational pH range. This review enlightens the readers about various materials that have been used for supporting the catalysts, their importance, method of impregnation, and optimum conditions required to attain maximum pollutant removal. From the wide array of catalysts reviewed, porous supports with a high surface area such as activated carbon, biochar and fibres adsorbs the pollutants near their surface facilitating enhanced Fenton reactions and degradation of pollutants. Alginate-based catalysts can be prepared by a simple procedure and exhibit good degradation efficiency when used in batch and continuous EF reactors. Zeolite-based catalysts are structurally stable and display promising results for successive cycles. The flexible and conductive nature of fibre-based supports performs the dual role as a catalyst and cathode. The highly stable and conductive properties of graphene and carbon nanotubes promote electron transfer, much required for continuous EF reactions.

The COVID-19 pandemic revealed fundamental limitations in the current model for infectious disease diagnosis and serology, based upon complex assay workflows, laboratory-based instrumentation, and expensive materials for managing samples and reagents. The lengthy time delays required to obtain test results, the high cost of gold-standard PCR tests, and poor sensitivity of rapid point-of-care tests contributed directly to society’s inability to efficiently identify COVID-19-positive individuals for quarantine, which in turn continues to impact return to normal activities throughout the economy. Over the past year, enormous resources have been invested to develop more effective rapid tests and laboratory tests with greater throughput, yet the vast majority of engineering and chemistry approaches are merely incremental improvements to existing methods for nucleic acid amplification, lateral flow test strips, and enzymatic amplification assays for protein-based biomarkers. Meanwhile, widespread commercial availability of new test kits continues to be hampered by the cost and time required to develop single-use disposable microfluidic plastic cartridges manufactured by injection molding. Through development of novel technologies for sensitive, selective, rapid, and robust viral detection and more efficient approaches for scalable manufacturing of microfluidic devices, we can be much better prepared for future management of infectious pathogen outbreaks. Here, we describe how photonic metamaterials, graphene nanomaterials, designer DNA nanostructures, and polymers amenable to scalable additive manufacturing are being applied towards overcoming the fundamental limitations of currently dominant COVID-19 diagnostic approaches. In this paper, we review how several distinct classes of nanomaterials and nanochemistry enable simple assay workflows, high sensitivity, inexpensive instrumentation, point-of-care sample-to-answer virus diagnosis, and rapidly scaled manufacturing.

In this paper we review the work related to amorphization under mechanical stress. Beyond pressure, we highlight the role of deviatoric or shear stresses. We show that the most recent works make amorphization appear as a deformation mechanism in its own right, in particular under extreme conditions (shocks, deformations under high stresses, high strain-rates).

All-solid-state batteries (ASSBs) using inorganic solid electrolytes (SEs) are in the spotlight for next-generation energy storage devices because of their potential for outstanding safety and high energy density. Recent progress in this field has been primarily based on advances in materials, such as the discovery of SEs with high ionic conductivities and the improvement of interfacial stability in electrodes. However, the use of inelastic SEs causes severe electrochemo-mechanical failures, such as cathode active material (CAM) disintegration, CAM/SE contact loss, and stress build-up during cycling, deteriorating the Li⁺ and e⁻ transport pathways. Although these concerns have been addressed previously, they have not been contextualized systematically in terms of the mechanical interactions among the components and their impacts on electrochemical performance. Here, we categorize the electrochemo-mechanical effect in ASSBs and its ramifications in terms of stress sources, active materials, composite electrodes, and cell stacks.

Layered double hydroxides (LDHs) are emerging catalyst materials with inner layer water molecules and higher anion exchange capacity. They have been extensively used as catalyst materials owing to their high specific surface area, environmental friendliness, lower cost, and non-toxicity. However, the lower surface area and leaching of metal ions from LDHs composites reduce the process efficiency of the catalyst. Modifying the LDHs materials with other materials can improve the surface properties of the composite and enhance the catalytic performance. Herein, this review aims to summarize the recent progress of nanostructured modified LDHs materials, their classification, synthesis, and a detailed discussion on their characterization techniques. Further, this study also discusses the application of nanostructured modified LDHs materials as catalysts in advanced oxidation process (AOPs) for various organic pollutants removal.

Solid state battery (SSB) performance is largely governed by processes occurring at electrolyte–electrode interfaces. At the Li metal anode, where the overwhelming majority of solid electrolyte (SE) are unstable against Li metal, the interface can readily react to form emergent Li-solid electrolyte interphases (SEI) with ionic, electronic, chemical, mechanical, and electrochemical properties substantially distinct from the parent phase. Facing similar challenges with liquid electrolytes, the Li battery community underwent a half century-long effort, still in progress, to illuminate fundamental properties of the Li SEI—including chemistry, morphology, transport, and sources of Li loss upon cycling—from which guiding principles have emerged to drive improvement in electrolyte and interface design. The Li metal SEI with solid electrolytes presents both similarities and differences to that in liquid electrolytes, with differences defining unique research needs. Here, we examine current understanding of the Li-SE interface as well as learnings from the liquid electrolyte community that we propose might be adopted to help rationalize and improve SE integration with Li anodes. Through this lens, we inspect current state-of-understanding of Li SEI composition, structure, and properties, along with Coulombic efficiency values reported so far for Li cycling with SE. We also highlight potential Li modification strategies for SSB, which are informed by and exploit understanding of the ionic SEI phases; in some instances, engineering strategies utilize a liquid electrolyte SEI directly, making liquid-derived SEI knowledge of immediate relevance.

Coronavirus disease 2019 (COVID-19), caused by the highly contagious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become the worst pandemic disease of the current millennium. To address this crisis, therapeutic nanoparticles, including inorganic nanoparticles, lipid nanoparticles, polymeric nanoparticles, virus-like nanoparticles, and cell membrane-coated nanoparticles, have all offered compelling antiviral strategies. This article reviews these strategies in three categories: (1) nanoparticle-enabled detection of SARS-CoV-2, (2) nanoparticle-based treatment for COVID-19, and (3) nanoparticle vaccines against SARS-CoV-2. We discuss how nanoparticles are tailor-made to biointerface with the host and the virus in each category. For each nanoparticle design, we highlight its structure–function relationship that enables effective antiviral activity. Overall, nanoparticles bring numerous new opportunities to improve our response to the current COVID-19 pandemic and enhance our preparedness for future viral outbreaks.