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Defining heat treatments for compositionally functionally graded materials (FGMs) is challenging due to varying processing conditions in terminal alloys and gradient regions. In the present work, we studied the impact of heat treatments on phase transformations and the resulting mechanical properties along an FGM grading from stainless steel 304L (SS304L) to Inconel 625 (IN625) FGM fabricated using directed energy deposition (DED) additive manufacturing (AM). We applied heat treatments at 700 °C, 900 °C, and 1150 °C and the microstructure and hardness, as a function of layer-wise composition and applied heat treatment, were characterized. The applicability of computational methods previously developed by the team to predict experimentally observed phases by the hybrid Scheil-equilibrium approach was evaluated. This approach improves the accuracy of predicting phases formed after heat treatment compared to equilibrium thermodynamic calculations using the overall layer compositions and provides a simple pathway to assist in designing heat treatment for FGMs. 

The Fe-Nb and Fe-Nb-Ni systems are remodeled using updated sublattice models for the topologically close packed (TCP) phases of Laves_C14, δ and μ with new experimental data and first-principles and phonon calculations based on density functional theory (DFT). Experimental techniques are used to determine phase compositions and tie-lines in the Fe-Nb-Ni system. The three-, three-, and five- sublattice models are used for Laves_C14, δ, and μ phases, respectively. DFT calculations are employed to predict thermochemical data as a function of temperature for Laves_C14, δ, and μ phases. The new thermodynamic description of the Fe-Nb-Ni system includes a new hexagonal phase named - hP24 - and the updates for the Fe-Nb system and reproduces better the experimental and computational thermochemical and phase equilibrium data from the present study and the literature. The new results will improve thermodynamic predictions of TCP and other phases in both Fe-based and Ni-based alloy systems. 

A database for the Cr-Ni-V system was constructed by modeling the binary Cr-V and ternary Cr-Ni-V systems using the CALPHAD approach aided by density functional theory (DFT)-based first-principles calculations and ab initio molecular dynamics (AIMD) simulations. To validate this new database, a functionally graded material (FGM) using Ni-20Cr and V was fabricated using directed energy deposition additive manufacturing (DED AM) and experimentally characterized. The deposited Ni-20Cr was pure fcc phase, while increasing V content across the gradient resulted in sigma phase formation, followed by bcc phase formation. The experimentally measured phases were compared with CALPHAD computations made using a Cr-Ni-V thermodynamic database from the literature and the database developed in the present work. The newly developed database was shown to better predict the experimentally observed phases due to its accurate modeling of binary systems within the database and the ternary liquid phase, which is critical for accurate Scheil calculations. 

We report and validate a new computational method to design tetrapeptides that assemble in response to pH stimuli to form beta-sheeted nanoassemblies and hydrogels. 

In functionally graded materials (FGMs) fabricated using directed energy deposition (DED) additive manufacturing (AM), cracks may form due to interdendritic stress during solidification, the formation of deleterious phases, or the buildup of residual stresses. This study builds on our previously proposed concept of FGM feasibility diagrams to identify gradient pathways that avoid deleterious phases in FGMs by also considering hot cracking. Here, five hot cracking criteria were integrated into the feasibility diagrams, and equilibrium simulations were carried out based on Scheil results (termed hybrid Scheil-equilibrium simulation) to predict phase formation below the solidus temperature considering solidification micro-segregation. The new feasibility diagrams were applied to four previously studied FGMs, and the newly proposed approach predicted high crack susceptibility, detrimental phase formation, or interdendritic BCC phase formation in the experimentally observed cracking region. This demonstrates the utility of the proposed framework for crack prediction in the design of future FGMs gradient pathways. 

Nanotubular structures possess remarkable advantages in a broad range of areas, such as catalysis, sensing, microencapsulation, selective mass transport, filtration, and drug delivery. While the fields of carbon nanotubes and nanotubes made of several noncarbon materials (e.g., metals, oxides, semiconductors) have been progressing rapidly, proteinaceous nanotubes remained largely underexplored. Here, by retrofitting a template wetting approach with multiple silk-based suspensions, we present a rapidly scalable and robust technology for fabricating large arrays (e.g., 20 × 20 cm2) of well-aligned 1D nanostructures made of silk proteins. Benefiting from the polymorphic nature of silk, precise control over the size, density, aspect ratio, and morphology (tubes versus pillars) of silk nanostructures is achieved, which then allows for programmable modulation of the end materials’ functions and properties (e.g., hydrophobicity, oleophilicity, and gas permeability). The silk nanotube arrays fabricated present great utility as antifouling coatings against marine algae and in oil extraction from oil–water mixtures. 

PUF tags made from stochastic assembly of silk microparticles provide a unique solution for anticounterfeiting. 

In this work, we develop a computational method to provide real-time detection for water bottom topography based on observations on surface measurements, and we design an inverse problem to achieve this task. The forward model that we use to describe the feature of the water surface is thetruncated Korteweg-de Vries equation, and we formulate the inversion mechanism as an online parameter estimation problem, which is solved by a direct filter method. Numerical experiments are carried out to show that our method can effectively detect abrupt changes of water depth.

 

The ubiquitous existence of microbial communities marks the importance of understanding how species interact within the community to coexist and their spatial organization. We study a two-species mutualistic cross-feeding model through a stochastic cellular automaton on a square lattice using kinetic Monte Carlo simulation. Our model encapsulates the essential dynamic processes such as cell growth, and nutrient excretion, diffusion and uptake. Focusing on the interplay among nutrient diffusion and individual cell division, we discover three general classes of colony morphology: co-existing sectors, co-existing spirals, and engulfment. When the cross-feeding nutrient is widely available, either through high excretion or fast diffusion, a stable circular colony with alternating species sector emerges. When the consumer cells rely on being spatially close to the producers, we observe a stable spiral. We also see one species being engulfed by the other when species interfaces merge due to stochastic fluctuation. By tuning the diffusion rate and the growth rate, we are able to gain quantitative insights into the structures of the sectors and the spirals.