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Undergraduate research transforms student’s conceptions of themselves as scientists and encourages participation and retention in science, technology, engineering, and mathematics (STEM) fields. Many barriers exist to carrying out scientifically impactful undergraduate research in nanomaterials at primarily undergraduate institutions (PUIs). Here, we share several practices and design principles that demonstrate pathways to overcome these barriers. Design of modular research projects with low entry barriers is essential. Postsynthetic transformation of nanoparticles is a field that enables such design and has been used successfully to advance nanoscience research while being achievable within undergraduate laboratories. Relatively large, inclusive research communities can be supported through the creation of opportunities with peer- and near-peer mentoring. We also share emerging strategies for enabling routine undergraduate access to transmission electron microscopy, which is one of the most mainstream characterization techniques in nanoscience yet is frequently absent from the infrastructure at undergraduate-focused institutions. 

Multilayer ceramic capacitors (MLCC) play a vital role in electronic systems, and their reliability is of critical importance. The ongoing advancement in MLCC manufacturing has improved capacitive volumetric density for both low and high voltage devices; however, concerns about long-term stability under higher fields and temperatures are always a concern, which impact their reliability and lifespan. Consequently, predicting the mean time to failure (MTTF) for MLCCs remains a challenge due to the limitations of existing models. In this study, we develop a physics-based machine learning approach using the eXtreme Gradient Boosting method to predict the MTTF of X7R MLCCs under various temperature and voltage conditions. We employ a transfer learning framework to improve prediction accuracy for test conditions with limited data and to provide predictions for test conditions where no experimental data exists. We compare our model with the conventional Eyring model (EM) and, more recently, the tipping point model (TPM) in terms of accuracy and performance. Our results show that the machine learning model consistently outperforms both the EM and TPM, demonstrating superior accuracy and stability across different conditions. Our model also exhibits a reliable performance for untested voltage and temperature conditions, making it a promising approach for predicting MTTF in MLCCs. 

Abstract Intercalation forms heterostructures, and over 25 elements and compounds are intercalated into graphene, but the mechanism for this process is not well understood. Here, the de‐intercalation of 2D Ag and Ga metals sandwiched between bilayer graphene and SiC are followed using photoemission electron microscopy (PEEM) and atomistic‐scale reactive molecular dynamics simulations. By PEEM, de‐intercalation “windows” (or defects) are observed in both systems, but the processes follow distinctly different dynamics. Reversible de‐ and re‐intercalation of Ag is observed through a circular defect where the intercalation velocity front is 0.5 nm s⁻¹± 0.2 nm s.⁻¹In contrast, the de‐intercalation of Ga is irreversible with faster kinetics that are influenced by the non‐circular shape of the defect. Molecular dynamics simulations support these pronounced differences and complexities between the two Ag and Ga systems. In the de‐intercalating Ga model, Ga atoms first pile up between graphene layers until ultimately moving to the graphene surface. The simulations, supported by density functional theory, indicate that the Ga atoms exhibit larger binding strength to graphene, which agrees with the faster and irreversible diffusion kinetics observed. Thus, both the thermophysical properties of the metal intercalant and its interaction with defective graphene play a key role in intercalation. 

Transition metal dichalcogenides (TMDCs) are potential materials for future optoelectronic devices. Grain boundaries (GBs) can significantly influence the optoelectronic properties of TMDC materials. Here, we have investigated the mechanical characteristics of tungsten diselenide (WSe 2 ) monolayers and failure process with symmetric tilt GBs using ReaxFF molecular dynamics simulations. In particular, the effects of topological defects, loading rates, and temperatures are investigated. We considered nine different grain boundary structures of monolayer WSe 2 , of which six are armchair (AC) tilt structures, and the remaining three are zigzag (ZZ) tilt structures. Our results indicate that both tensile strength and fracture strain of WSe 2 with symmetric tilt GBs decrease as the temperature increases. We revealed an interfacial phase transition for high-angle GBs reduces the elastic strain energy within the interface at finite temperatures. Furthermore, brittle cracking is the dominant failure mode in the WSe 2 monolayer with tilted GBs. WSe 2 GB structures showed more strain rate sensitivity at high temperatures than at low temperatures.