B. Reeja‐Jayan

MXene is a class of compounds known for its superior electrical properties and versatile surface chemistries. However, its susceptibility to oxidation-induced degradation under ambient conditions prevents its incorporation into devices. In this work, we enhance the stability of MXene-based devices through encapsulation. We developed a sensor based on a heterojunction of Ti 3 C 2 T x MXene and silver nanoparticles for formaldehyde detection. This sensor is then encapsulated in poly(1,3,5,7-tetravinyl-1,3,5,7-tetramethylcyclotetrasiloxane) deposited via initiated chemical vapor deposition. Encapsulation substantially improved sensor stability, extending the half life span by more than 200%. These findings were reinforced by molecular dynamic simulations. Furthermore, upon hydration, siloxane in the encapsulant forms silanol which reacts with formaldehyde and boosts sensitivity by 1.7 times. We also demonstrate a rapid, low-energy regeneration process that enables the sensor to attain up to 90% of its previous response after degradation. These enhancements position this sensor as a reliable solution for real-time formaldehyde detection, in applications ranging from indoor air quality monitoring to industrial safety.

Abstract Laser powder bed fusion offers a high degree of geometric freedom for manufacturing with novel materials, yet failures during fabrication remain a critical barrier to achieving more complex components. Recoater blade collisions, cracking, and build plate delamination damage parts and performance, especially with hard and high-temperature materials. Cemented carbides are optimal for high-hardness machining and tooling parts, but high thermal gradients and complex composite behaviors exacerbate fabrication issues. Understanding the effects of build process parameters on macroscopic failure modes is critical to mitigate such issues. This study leverages thermomechanical modeling to investigate the effects of process parameter alterations on build stresses and deflection for WC-Ni part fabrication strategies. A comparative analysis revealed that reductions in laser energy density and part sizes reduced part deflection. The simulations reinforced that bed preheating reduced stresses and thermal gradients, but reductions in interlayer timing also benefited builds by adding additional interlayer heating. Sharp geometric features common in cemented carbide machining and tooling parts significantly increased deflection. Exploratory builds achieved high-density (> 97%) WC-17 wt% Ni parts, with delamination occurring only with the geometry with the highest simulated stress. Profilometry revealed over-melting around part edges and high surface roughness (arithmetic mean height up to 61.5 µm), indicating that localized features from scan strategies heavily contribute to recoater collisions. The successful fabrication of a range of parts, including drill bit geometries, demonstrated the effectiveness of parameter refinement as a tool to avoid catastrophic failure events with laser powder bed fusion.

Cemented carbides such as cemented tungsten carbide (WC) are known for their use in resilient wear-resistant applications where hardness and thermal stability are imperative. They are composed of carbide particles embedded in a metal binder. Laser Powder Bed Fusion (L-PBF) is a favorable method to form cemented carbides into complex geometries, but composites pose unique challenges relative to metals typically processed by L-PBF. Resolving the melt pool temperature distributions in L-PBF is key to understanding the underlying physics of the fusion process. Using a two-color thermal imaging method, melt pool thermal maps of WC 0.83 -Ni 0.17 were captured with linear energy densities ranging from 500–1750 J/m with and without powder. WC 0.83 -Ni 0.17 melt pools exhibit temperatures above 4000 K, which can lead to the generation of other WC phases. Compared to more common L-PBF materials such as 316L stainless steel (SS), WC 0.83 -Ni 0.17 melt pools reach higher temperatures. Our direct measurements find that the thermal conductivity of WC 0.83 -Ni 0.17 is 30 W/m-K at 300 K, which is higher than the thermal conductivity of 316L SS and suggests that other heat transfer limitations must cause the elevated melt pool temperatures. A FLOW-3D CFD model based on the composite properties was compared to both the melt pool centerline temperatures and width measurements of the samples fabricated by L-PBF. The simulations indicate that specifying the onset of fluidity is key to reproducing the high temperatures observed experimentally. Although Ni has a melting point of 1728 K, the simulations do not match experiments unless the onset of fluidity is set at the melting point of WC (3143 K). Within FLOW-3D, the onset of fluidity is controlled by the critical solid fraction, which is a uniquely important parameter for simulating composite materials.

Traditional methods for synthesizing two-dimensional Ti 3 C 2 T x MXenes such as hydrofluoric acid (HF) or LiF/HCl based etching can be time-consuming, complex, and often result in low yields. They generally involve multi-step processes involving >40 h of preparation time that can expose the materials to harsh conditions. In this study, we demonstrate a rapid single-step microwave (MW) synthesis method that significantly reduces production time to 90 min, achieving a 90 % yield and cutting energy consumption by 75 %. For the first time, synchrotron x-ray pair distribution function (PDF) analysis conducted on MW-synthesized MXene (MW-Ti 3 C 2 T x ) indicates greater structural fidelity in local atomic ordering, indicating high-quality which is comparable to conventionally synthesized counterparts (CO-Ti 3 C 2 T x ). This method achieves similar or greater structural quality in less time while also enhancing electromagnetic interference shielding (EMI SE) performance. A 15 μm MW-Ti 3 C 2 T x film demonstrated an impressive EMI SE of ∼67 dB in the X-band, compared to the ∼63 dB achieved by CO-Ti 3 C 2 T x . The enhanced EMI SE performance is attributed to the presence of fluorine terminations, which provide oxidation resistance, increased conductivity and improved absorption of EM waves. The MW-induced shocks during irradiation not only help remove O 2 /OH groups, preventing oxidation, but also tunes the functional groups, enhancing charge transport and effective EM wave attenuation. The MW synthesis method presents a fast, efficient, and scalable approach for producing high-quality MXene nanosheets, paving the way for advancements in EMI shielding and other applications. • Rapid MW synthesis of high-quality Ti 3 C 2 T X MXenes in under 90 minutes achieves 90% yield, and 75% less energy consumption. • Synchrotron Pair distribution analysis confirms high atomic ordering and structural fidelity compared to conventional MXene. • MW- Ti 3 C 2 T X films exhibit outstanding EMI SE of around 67 dB in the X-band. • Enhanced dielectric losses and conductivity in MW-MXenes due to increased interlayer spacing and fluorine terminations. • Scalable MW-synthesis method for MXenes eliminates the need for exfoliation or delamination.

Abstract Variability in the inherently dynamic nature of additive manufacturing introduces imperfections that hinder the commercialization of new materials. Binder jetting produces ceramic and metallic parts, but low green densities and spreading anomalies reduce the predictability and processability of resulting geometries. In situ feedback presents a method for robust evaluation of spreading anomalies, reducing the number of required builds to refine processing parameters in a multivariate space. In this study, we report layer-wise powder bed semantic segmentation for the first time with a visually light ceramic powder, alumina, or Al 2 O 3 , leveraging an image analysis software to rapidly segment optical images acquired during the additive manufacturing process. Using preexisting image analysis tools allowed for rapid analysis of 316 stainless steel and alumina powders with small data sets by providing an accessible framework for implementing neural networks. Models trained on five build layers for each material to classify base powder, parts, streaking, short spreading, and bumps from recoater friction with testing categorical accuracies greater than 90%. Lower model performance accompanied the more subtle spreading features present in the white alumina compared to the darker steel. Applications of models to new builds demonstrated repeatability with the resulting models, and trends in classified pixels reflected corrections made to processing parameters. Through the development of robust analysis techniques and feedback for new materials, parameters can be corrected as builds progress.

Abstract Binder jet additive manufacturing is well suited for fabricating large (order of cm) and geometrically complex ceramic preforms. However, the main challenge in producing ceramic oxide parts via binder jetting is the high‐temperature postprocess tasked with eliminating internal porosity to achieve full densities. In this work, we demonstrate the ability to produce oxide ceramic parts with desirable densities by sintering binder jetted preforms. We investigate the sintering behavior of binder jetted preforms composed of three oxide powders with distinct morphologies: ball‐milled alumina, gas‐atomized silica, and sintered‐agglomerated zirconia. We fabricate the preform samples using a commercial binder jetting system and a conventional die‐pressing technique to understand the effect of starting densities. Furthermore, we parametrize the heating profiles to understand the effect of sintering temperature, sintering duration, and heating rate on each powder's densification behavior, microstructure, and phase composition. Results show the relatively low starting densities within the binder jetted preforms caused the onset sintering temperature to be higher than what is documented in conventional sintering studies. As expected, we observed sintered densities increase with respect to sintering temperature and duration. These findings were utilized to downselect sintering parameters capable of achieving high densities (>96%). Herein, this study validates the sintering of binder jetted preforms as a suitable way to manufacture ceramic parts, regardless of powder morphologies, thereby increasing the robustness of the supply chain involved in additive manufacturing of ceramic oxides.

The densification and sintering of ceramics using microwaves is first reported in the mid‐1960s. Today, the reduced carbon footprint of this process has renewed interest as it uses less energy overall compared to conventional process heating/furnaces. However, scaling up and commercializing the microwave sintering process of ceramics remains a formidable challenge. As a contactless method, microwave sintering offers geometric flexibility over other field‐assisted sintering processes. Yet, the inability to address multiscale, multiphysics‐driven heterogeneities arising during microwave coupling limits discussions about a future scale‐up process. Herein, the case is made that unlike 60 years ago, new advances in multiscale computational modeling, materials characterization, control systems, and software open up new avenues for addressing these challenges. More importantly, the rise of additive manufacturing techniques demands the innovation of sintering processes in the ceramics community for realizing near‐net‐shaped and complex parts for applications ranging from medical implants to automotive and aerospace parts.

) at 10C and a 500% improvement in cycling stability tested at 5C within the voltage range of 3.0-4.5 V (capacity fading rate is reduced from 1.92%/cycle to 0.32%/cycle).

Abstract This study binder jets a tungsten carbide‐nickel (WC‐Ni) sintered‐agglomerated composite powder, and postprocesses the preforms using an initial sintering step followed by a hot isostatic pressing (HIP) step. The effects of sintering temperatures, sintering durations, and HIP temperatures on notable properties (e.g., porosity, microstructure, hardness, and oxidation behavior) are quantified. The highest average relative density produced in this study was 96.8%, and volumetric shrinkage of these coupons was about 64%. Microstructural characterization shows that the WC grains are homogenously distributed throughout the nickel matrix and grow to an average diameter of 1.6 (a 60% increase) during processing. X‐ray diffraction patterns indicate that no unwanted products were formed. Processed coupons achieved a maximum hardness of 54 Rockwell C, limited by their internal porosity. Oxidation tests result in the production of WO 3 and NiWO 4 at temperatures above 600°C. Methodologies and results from this study can be leveraged to additively manufacture highly dense, geometrically complex WC‐Ni parts with small carbide grains, low nickel content, desirable microstructure, and suitable functional properties.

Over the years, efforts in bioinspired soft robotics have led to mobile systems that emulate features of natural animal locomotion. This includes combining mechanisms from multiple organisms to further improve movement. In this work, we seek to improve locomotion in soft, amphibious robots by combining two independent mechanisms: sea star locomotion gait and gecko adhesion. Specifically, we present a sea star-inspired robot with a gecko-inspired adhesive surface that is able to crawl on a variety of surfaces. It is composed of soft and stretchable elastomer and has five limbs that are powered with pneumatic actuation. The gecko-inspired adhesion provides additional grip on wet and dry surfaces, thus enabling the robot to climb on 25° slopes and hold on statically to 51° slopes.

Abstract Real‐time onboard state monitoring and estimation of a battery over its lifetime is indispensable for the safe and durable operation of battery‐powered devices. In this study, a methodology to predict the entire constant‐current cycling curve with limited input information that can be collected in a short period of time is developed. A total of 10 066 charge curves of LiNiO 2 ‐based batteries at a constant C‐rate are collected. With the combination of a feature extraction step and a multiple linear regression step, the method can accurately predict an entire battery charge curve with an error of < 2% using only 10% of the charge curve as the input information. The method is further validated across other battery chemistries (LiCoO 2 ‐based) using open‐access datasets. The prediction error of the charge curves for the LiCoO 2 ‐based battery is around 2% with only 5% of the charge curve as the input information, indicating the generalization of the developed methodology for predicting battery cycling curves. The developed method paves the way for fast onboard health status monitoring and estimation for batteries during practical applications.

Feature extraction and a neural network model are applied to predict defect types and concentrations in experimental anatase TiO2 samples. A dataset of TiO2 structures with vacancies and interstitials of oxygen and titanium is built, and the structures are relaxed using energy minimization. The features of the calculated pair distribution functions (PDFs) of these defected structures are extracted using linear methods (principal component analysis and non-negative matrix factorization) and non-linear methods (autoencoder and convolutional neural network). The extracted features are used as inputs to a neural network that maps feature weights to the concentration of each defect type. The performance of this machine learning pipeline is validated by predicting defect concentrations based on experimentally measured TiO2 PDFs and comparing the results to brute-force predictions. A physics-based initialization of the autoencoder has the highest accuracy in predicting defect concentrations. This model incorporates physical interpretability and predictability of material structures, enabling a more efficient characterization process with scattering data.