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Drawing on a global, longitudinal case study of the vinyl record manufacturing practice following a global decline in sales, we examine the work undertaken to preserve it over time. Our process model of practice preservation highlights that a declining practice may persist across generations if it is made more accessible and resilient by reducing its dependence on specific types of meanings, materials, and competences that risk being lost. Our analysis suggests that these outcomes depend on the work of two types of custodians—legacy professionals and new enthusiasts—in terms of "diversifying" the practice to accommodate multiple configurations of meanings, materials, and competences, thus constituting new practice variants, as well as supporting the continued enactment of variants over time by engaging in processes we label "propping up," "recovering," "replenishing," and "releasing." In addition to developing a rich understanding of preservation, our findings contribute to a nuanced perspective on practice evolution by conceptualizing a practice as comprising multiple, potentially fluid variants. Moreover, by highlighting inclusive custodianship processes, which differ from gatekeeping and guarding so far emphasized, and by unpacking the role of materials to complement the traditional emphasis on symbolic elements, our findings enrich the nascent custodianship literature. [ABSTRACT FROM AUTHOR]

Extreme heat is the leading cause of weather-related mortality in the United States, and extreme heat events are projected to continue to increase in geographic extent, frequency, and severity in the United States as climate change progresses. Transportation infrastructure is a significant driver of the urban heat island (UHI) effect and exacerbating extreme heat events. Efforts to mitigate UHI impacts often focus on reflecting incoming solar radiation (i.e., increasing surface albedo) and providing shade (e.g., planting street trees). However, advanced and novel materials (ANM) for pavements that reduce heat storage, and green stormwater infrastructure (GSI) that promotes evaporative cooling, can provide additional heat mitigation pathways. Sidewalks facilitate non-motorized transportation, and are relatively low-risk, low-cost, and have simple structural requirements compared to other transportation infrastructure. Hence, sidewalks and adjacent planting strips can offer a logical test bed for new materials and designs. With the thermal comfort, safety, and efficiency of users in mind, environmentally responsible designs can also minimize energy embedded in construction materials and help maintain natural ecosystem processes. Although ANMs hold significant promise for heat mitigation, they have not yet achieved widespread implementation. This project systematically reviewed the growing literature related to theapplication of ANMs and GSI to reduce UHI effects and implemented a survey of urban planners and public works engineers to assess the current and planned use of these strategies and identify barriers to implementation. This report summarizes the emergent themes from the systematic literature review, survey results and policy recommendations for an anticipated reading audience of urban policy makers, planners, and practitioners. View the NCST Project Webpage

This paper introduces a new approach toward characterizing local structural features of two-dimensional particle systems. The approach can accurately identify and characterize defects in high-temperature crystals, distinguish a wide range of nominally disordered systems, and robustly describe complex structures such as grain boundaries. This paper also introduces two-dimensional functionality into the open-source software program VoroTop which automates this analysis. This software package is built on a recently-introduced multithreaded version of Voro++, enabling the analysis of systems with billions of particles on high-performance computer architectures.

The number of studies that apply machine learning (ML) to materials science has been growing at a rate of approximately 1.67 times per year over the past decade. In this review, I examine this growth in various contexts. First, I present an analysis of the most commonly used tools (software, databases, materials science methods, and ML methods) used within papers that apply ML to materials science. The analysis demonstrates that despite the growth of deep learning techniques, the use of classical machine learning is still dominant as a whole. It also demonstrates how new research can effectively build upon past research, particular in the domain of ML models trained on density functional theory calculation data. Next, I present the progression of best scores as a function of time on the matbench materials science benchmark for formation enthalpy prediction. In particular, a dramatic improvement of 7 times reduction in error is obtained when progressing from feature-based methods that use conventional ML (random forest, support vector regression, etc.) to the use of graph neural network techniques. Finally, I provide views on future challenges and opportunities, focusing on data size and complexity, extrapolation, interpretation, access, and relevance.

The atomic-level structure of interfaces between Pt and a transition form of Al2O3 were studied using a combination of electron microscopy and first principles calculations. A model system of Pt nanoprecipitates in Al2O3 were formed in sapphire wafers via high-energy ion implantation of Pt followed by thermal annealing at 1000 °C in air. The Pt nanoparticles took the form of tetrahedra and truncated tetrahedra primarily bound by {111}Pt facets. The high prevalence of these facets motivated the development of density functional theory (DFT) based models of (111)Pt interfaces with six different chemical terminations of (2¯01) θ-alumina. The atomic-level structure of the Pt/Al2O3 interfaces was characterized with aberration-corrected scanning transmission electron microscopy (STEM) and the experimental images were compared to STEM image simulations of the DFT models. The model interface with Pt bonded to oxygen-terminated θ-Al2O3, with the Pt located on top of the O and with an underlying layer of octahedral Al, provided the best match to the experimental images. This interfacial termination is also the most stable for the thermal annealing conditions used based on thermodynamic calculations of the interfacial energy as a function of temperature and oxygen partial pressure. This experimentally verified model provides a basis for improving models of Pt/γ-alumina interfaces.

Lattice deformation via substrate-driven mechanical straining of 2D materials can profoundly modulate their bandgap by altering the electronic band structure. However, such bandgap modulation is typically short-lived and weak due to substrate slippage, which restores lattice symmetry and limits strain transfer. Here, it is shown that a non-volatile thermomechanical strain induced during hot-press synthesis results in giant modulation of the inherent bandgap in quasi-2D tellurium nanoflakes (TeNFs). By leveraging the thermal expansion coefficient (TEC) mismatch and maintaining a pressure-enforced non-slip condition between TeNFs and the substrate, a non-volatile and anisotropic compressive strain is attained with ε = −4.01% along zigzag lattice orientation and average biaxial strain of −3.46%. This results in a massive permanent bandgap modulation of 2.3 eV at a rate S (ΔEg) of up to 815 meV/% (TeNF/ITO), exceeding the highest reported values by 200%. Furthermore, TeNFs display long-term strain retention and exhibit robust band-to-band blue photoemission featuring an intrinsic quantum efficiency of 80%. The results show that non-volatile thermomechanical straining is an efficient substrate-based bandgap modulation technique scalable to other 2D semiconductors and van der Waals materials for on-demand nano-optoelectronic properties.

Researchers from a broad spectrum of scientific and engineering disciplines are increasingly using scattering-type near-field infrared spectroscopic techniques to characterize materials non-destructively with nanoscale spatial resolution. However, a sub-optimal understanding of a technique's implementation can complicate data interpretation and act as a barrier to entering the field. Here the key detection and processing steps involved in producing scattering-type near-field nanoscale Fourier transform infrared spectra (nano-FTIR) are outlined. The self-contained mathematical and experimental work derives and explains: i) how normalized complex-valued nano-FTIR spectra are generated, ii) why the real and imaginary components of spectra qualitatively relate to dispersion and absorption respectively, iii) a new and generally valid equation for spectra which can be used as a springboard for additional modeling of the scattering processes, and iv) an algebraic expression that can be used to extract an approximation to the sample's local extinction coefficient from nano-FTIR. The algebraic model for weak oscillators is validated with nano-FTIR and attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectra on samples of polystyrene and Kapton and further provides a pedagogical pathway to cementing some of the technique's key qualitative attributes.

Short-range ordering (SRO) in multi-principal element alloys influences material properties such as strength and corrosion. While some degree of SRO is expected at equilibrium, predicting the kinetics of its formation is challenging. We present a simplified isothermal concentration-wave (CW) model to estimate an effective relaxation time of SRO formation. Estimates from the CW model agree to within a factor of five with relaxation times obtained from kinetic Monte Carlo (kMC) simulations when above the highest ordering instability temperature. The advantage of the CW model is that it only requires mobility and thermodynamic parameters, which are readily obtained from alloy mobility databases and Metropolis Monte Carlo simulations, respectively. The simple parameterization of the CW model and its analytical nature makes it an attractive tool for the design of processing conditions to promote or suppress SRO in multicomponent alloys.

Structural materials used in nuclear reactor environments are subjected to coupled extremes such as high temperature, irradiation, and corrosion which act in concert to degrade their functional performance. Connecting alloy microstructure such as grain boundaries, and accumulating point defects with corrosion attack and pore morphology is critical to understanding underlying mechanisms. We uncover the compositional variations and morphology at multiple length scales in corrosion-damaged Ni-Cr alloy after exposure to oxidants in molten fluoride salts. A complex network of dense corrosion pores is detected by surface-level SEM observations. The corrosion pores take on a 1-dimensional morphology and are enriched with Ni and depleted of Cr 1–5 µm from the pore surface. STEM-EDS and 4D-STEM strain maps acquired simultaneously highlight the local variations in composition and structure of a ≤ 200 nm Cr-rich layer identified from a cross-section taken at the bottom of an isolated corrosion pore between the Ni-Cr alloy matrix and the embedded salt. However, the absence of an observed interface between the Ni-Cr alloy matrix and the FCC-structured Cr-rich layer suggests that Cr plating from the salt did not transpire. These findings support a proposed Cr lattice diffusion mechanism rather than Cr-precipitation from the salt to accommodate temperature transient conditions during sample cooling. Through the development of a 1D phase field model, these results are rationalized by formation energies for the Ni- and Cr-oxidation into the molten salt. This study reveals the locally altered microstructure caused by high temperature corrosion in non-steady-state molten salt nuclear reactor environments.