Electric vehicle (EV) battery packs have undergone substantial advancements in recent years, driven by engineering design improvements, material innovations, and increasingly stringent regulatory enforcement. These developments have enabled battery packs to become more energy-dense, which is essential for extending driving range and improving overall vehicle performance. However, with increased energy density comes a higher severity of thermal events, such as thermal runaway, which continues to raise concerns regarding vehicle safety, reliability, and long-term durability. This review highlights the critical role that thermal insulation materials play in mitigating the impact of such thermal events within EV battery systems. It presents an overview of commonly used thermal insulation materials, emphasizing their chemical composition, thermal resistance, and mechanical integrity under extreme conditions such as high temperatures and physical stress. The ability of these materials to maintain performance during thermal abuse is essential for protecting both the battery and surrounding vehicle components. In addition to material properties, the review compares the methodology and performance metrics of common methods for evaluating flammability, including flame retardance test such as UL 94 and torch and grit flammability tests such as one described in UL 2596. These comparisons are crucial for identifying insulation materials that can withstand severe thermal conditions without compromising safety. Beyond flammability, high temperature, smoke, and other important considerations such as thermal properties, environmental durability, corrosion resistance, and dielectric strength will be discussed with examples. These factors contribute to the overall effectiveness and reliability of insulation materials in EV applications. By understanding and optimizing these properties, engineers can better design battery packs that are not only high-performing but also safe and durable under demanding operating conditions.
Mission Engineering is maturing as a Systems Engineering discipline, yet adoption of its common frameworks remains stratified. This study examines whether selective adoption is observable across ME scholarship and develops a framework to address identified barriers. We analyze 42 self-identifying ME publications (2014-2025) using a 24-term rubric derived from Department of War guidance, constructing lexical similarity networks, coauthorship maps, and coherence baselines to test predictions from Kuhn's paradigm competition against Rogers' diffusion of innovations. Results show a binary stratification between papers engaging with Mission Engineering Guide (MEG)-coined terminology and those that do not (Cohen's d = 1.93), with no meaningful community modularity detected across 102 community detection runs (max Q = 0.212 < 0.30). Non-engaged papers are terminologically indistinguishable from random subsets, supporting Rogers' single-framework diffusion model and indicating that adoption barriers arise from implementation friction rather than competing theoretical alternatives. These findings identify process complexity, limited trialability, and weak interpersonal collaboration as likely barriers. In response, we present a formalized ten-step ME process with modular, artifact-producing steps and explicit mathematical specifications spanning operational, functional, capability, and system domains. This enables independent execution, reproducible evaluation, and standardized outputs, increasing trialability, reducing integration complexity, and supporting interoperability and broader adoption.
Relevance to human health is the importance of factors affecting the well-being and health outcomes of individuals or populations. In toxicology, it concerns whether, and how, hazardous effects seen in test animals may also affect humans. This paper illustrates how human relevance is used in classifying cancer and reproductive toxicity hazards under the EU CLP Regulation, noting that the Regulation does not clearly define the concept. Different hazard endpoints use varying terminology and evidentiary standards, and ECHA’s CLP Guidance does not always match the Regulation’s wording. Different assessors can also reach different conclusions from the same data. These factors create uncertainty about how human relevance should be interpreted and applied. To improve consistency with the CLP Regulation’s aim of protecting workers and consumers, a clear definition and more practical interpretation of human relevance are needed. Human relevance is not binary and is inherently subjective, so both qualitative and quantitative approaches should be combined. The threshold for human relevance should not be so low or vague that classification for human health would only rarely be considered unnecessary.
We develop a kinetic Monte Carlo simulation model that extends the Hoffman-Lauritzen (HL) theory for homopolymers to copolymers by including stems with short chain branches (SCBs), which adsorb onto the crystal growth surface but block growth over them until they randomly desorb at the rate specified by the HL theory. This simple extension of the HL theory predicts an exponential slowdown in crystalline growth rate with the number density of short chain branches. Utilizing established input parameters from the literature, predicted isothermal copolymer growth rates are consistent with the experimental results of Wagner and Phillips across a wide range of crystallization temperatures and SCB levels, without the need for additional adjustable parameters.
Catalysis is essential to modern chemical manufacturing and environmental sustainability. Yet, traditional catalyst discovery remains slow, resource-intensive and constrained by human-centred trial-and-error workflows. The integration of artificial intelligence (AI), robotics and high-throughput experimentation into self-driving laboratories (SDLs) presents a transformative approach for accelerating catalyst discovery and optimization. SDLs combine automated synthesis and testing platforms, data infrastructures and AI-guided decision-making to enable information-rich experimentation and the fast-tracked generation of scientific knowledge. However, in our view, realizing the full potential of SDLs requires sustained human oversight to ensure rigorous data curation, validate machine-generated hypotheses and establish benchmarks to mitigate AI-related errors. This Perspective outlines core SDL components, including hardware, computational modelling and AI-guided decision-making. We discuss challenges in data availability, integration of computational and experimental workflows and scalable platforms. Finally, we outline immediate opportunities to broaden the adoption of autonomous experimentation in catalysis. The rise of artificial intelligence together with advances in robotics is leading a surge of interest in self-driving laboratories. This Perspective discusses self-driving laboratories for catalysis while arguing that, to achieve their full potential, human oversight is required.
