Big-data approaches to chemistry falter when experimental data remain scarce, expensive, and noisy. Here we demonstrate that physics-informed descriptors overcome this limitation and enable accurate predictions from severely limited datasets. Taking capped diisocyanate deblocking as a test case, we introduce descriptors derived from the Bond Energies from Bond Orders and Populations (BEBOP) method that capture electronic structure and bonding at low computational cost. Trained on only 19 experimental compounds, BEBOP-informed LASSO regression predicts deblocking temperatures within an RMSE of ~11 °C over a 227–323 °C range, while conventional molecular descriptors fail to produce any meaningful correlation. We validate the resulting physically interpretable model on two new compounds, one within the training range and one outside of it. Embedding physical insight into descriptors rather than relying on algorithmic complexity reduces data requirements by an order of magnitude and offers a generalizable strategy for molecular design problems wherever large datasets are impractical.
Developmental neurotoxicity (DNT) potential of (agro)chemicals is assessed in rodents according to OECD Test Guidelines (TG) 426 (DNT) or 443 (Extended One-Generation Reproductive Toxicity; EOGRTS) incorporating a DNT cohort. While the EOGRTS evaluates reproductive and neurotoxicity endpoints across multiple life stages, key differences in study design, dose selection, and exposure duration between OECD 426 and OECD 443 can complicate data interpretation and impact hazard identification and risk assessment. This manuscript evaluates current DNT testing strategies and examines the feasibility of a combined DNT-EOGRTS approach aimed at generating data for regulatory decision making. This combined approach requires substantial modifications to the EOGRTS design, and its implementation should be approached with caution. Given the increased complexity and logistical challenges, additional cognitive assessments such as learning and memory (L M) should only be included when scientifically justified on a substance specific basis. Retrospective analyses indicate that L M is among the least sensitive DNT endpoints, and its inclusion in an EOGRTS does not yield a functional equivalent of a standalone DNT. The review also examines regulatory triggers and recent regulatory decisions relevant to inclusion of cognitive assessments. Finally, the status and limitations of alternative approaches, including the in vitro DNT battery and zebrafish embryo models, are discussed. Overall, OECD 426 remains the scientifically preferred approach for DNT assessment. While a combined design may be feasible, it is inherently suboptimal for DNT evaluation. Collectively, this comprehensive review provides recommendations for generating robust DNT data while supporting animal welfare-conscious, future-oriented regulatory testing strategies.
Molecular dipole moments are required for empirical correlations used to estimate thermophysical properties of zinc dialkyldithiophosphates (ZDDPs), an important class of anti-wear additives. We calculated dipole moments for three ZDDPs and their precursor dialkyldithiophosphates using density functional theory and explicitly accounted for their dependence on molecular conformation. We unexpectedly found that dipole ∗Corresponding author. Email: karlj@pitt.edu moments are highly sensitive to conformational degrees of freedom. The dipole moments for each of these six molecules range from almost zero to almost 5 D across different conformers. Surprisingly, this variation appears to be dominated by the conformations of the alkyl chains rather than the polar inorganic core. Despite this wide range, the arithmetic mean and Boltzmann weighted average dipole moments fall within a narrow range of 2.2–2.4 D for all six molecules examined. This consistency arises because the dipole moment shows no correlation with the conformational energy. The robust average value significantly simplifies property estimation by eliminating the need for conformation-specific dipole calculations. We recommend that thermophysical property correlations use a constant dipole moment of 2.3 D for ZDDPs and their precursors. We predict that experimental measurements will yield values close to this average, since they inherently sample an ensemble of conformations.
As lithium-ion batteries (LIBs) are increasingly used in electric vehicles and grid-scale energy storage, thermal runaway (TR) remains a critical safety concern. TR releases intense heat, flammable vapors, toxic gases, and particulates, posing risks of fire, explosion, and environmental harm. Immersion cooling using dielectric battery thermal management fluids (BTMFs) has emerged as a promising mitigation strategy, yet its effects on TR and gas evolution remain unclear. In this study, 18650 cylindrical NCA cells were driven to TR either in air or fully submerged in non-aqueous BTMFs inside a 600 L environmental chamber equipped with heating, imaging, thermocouples, and in situ FTIR gas analysis. Results show that immersion substantially increases the energy required to initiate TR but does not alter intrinsic venting (115 degrees C-135 degrees C) or TR (170 degrees C-195 degrees C) temperature thresholds. Under immersion, only a brief (similar to 0.5 s) localized fire occurred and particulates largely remained in the fluid and later settled. Whereas air-cooled cells eject sustained burning electrolyte and generate widespread particulates. Headspace FTIR measurements indicate that immersion suppresses hydrocarbon release during venting, followed by a single burst upon TR. Overall, immersion cooling increases the TR energy barrier and mitigates hazardous emissions, though burst of volatiles upon runaway must be considered. Immersion cooling raises the energy needed to trigger battery runaway.Runaway temperatures remain unchanged despite liquid immersion.BTMF traps vent gases during early stage of cell failure, mitigating hazards.When TR occurs, liquid cooling rapidly quenches flames and limits casing damage.BTMF captures particulates and soot during TR, minimizing environmental harm.
Molecular modeling calculations for the design and improvement of next-generation additives for motor oils have reached a level that can support and improve experimental results. The regulation of insoluble sludge nanoparticle aggregations within oil and on engine pistons is a critical performance metric for lubricant oil additives. There is a general agreement regarding the mechanism of deposit formation which is attributed to the self-aggregation of nano-sized carbon rich insoluble structures. Dispersants are a primary category of additives employed to inhibit aggregation in lubricant formulations. Along with the base oil, they are crucial in dispersing and stabilizing insoluble particles to manage the formation of deposits. In this study, multiscale modeling methods were used to elucidate molecular mechanism of deposit control via polyisobutylene-bis-succinimide (PIBSI) dispersants by using density functional theory (DFT), molecular dynamics (MD) simulations of cells constructed by statistical sampling of molecular configurations, and coarse-grained (CG) simulations. The aim of this study was to understand the role of different groups such as succinimide, amine center, and two polyisobutylene (PIB) tails in PIBSI dispersants. It was demonstrated that the mechanism of deposit control by the polymer-based PIBSI dispersant can be elucidated through the interactions among various constituents, including hydrogen bonding and hydrophilic–hydrophobic interactions. We showed that sludge type nanoparticle aggregation is mitigated by intercalation of polar amine central groups of dispersant between the nanoparticles followed by the extension of two hydrophobic PIB chains into the oil phase that decreases coalesce further by forming a hydrophobic repulsive layer.
