Elucidating the auxetic behavior of cementitious cellular composites using finite element analysis and interpretable machine learning

With the advent of 3D printing, auxetic cellular cementitious composites (ACCCs) have recently garnered significant attention owing to their unique mechanical performance. To enable seamless performance prediction of the ACCCs, interpretable machine learning (ML)-based approaches can provide efficient means. However, the prediction of Poisson’s ratio using such ML approaches requires large and consistent datasets which is not readily available for ACCCs. To address this challenge, this paper synergistically integrates a finite element analysis (FEA)-based framework with ML to predict the Poisson’s ratios. In particular, the FEA-based approach is used to generate a dataset containing 850 combinations of different mesoscale architectural void features. The dataset is leveraged to develop an ML-based prediction tool using a feed-forward multilayer perceptron-based neural network (NN) approach which shows excellent prediction efficacy. To shed light on the relative influence of the design parameters on the auxetic behavior of the ACCCs, Shapley additive explanations (SHAP) is employed, which establishes the volume fraction of voids as the most influential parameter in inducing auxetic behavior. Overall, this paper develops an efficient approach to evaluate geometry-dependent auxetic behaviors for cementitious materials which can be used as a starting point toward the design and development of auxetic behavior in cementitious composites

Dynamic compressive behavior of metallic particulate-reinforced cementitious composites: SHPB experiments and numerical simulations

An experimental and numerical evaluation on the dynamic compressive response of mortars containing up to 20% waste iron powder as sand replacement is presented in this paper. The dynamic response is evaluated using split Hopkinson pressure bar (SHPB) apparatus under high strain rates (up to 250/s). The elongated iron particulates present in the iron powder-incorporated mortars warrant significantly improved compressive strength and energy absorption capacity at high strain rates. Multiscale numerical simulations are performed with a view to develop a tool that facilitates microstructure-guided design of these particulate-reinforced mortars for efficient dynamic performance. The dynamic compressive response of particulate-reinforced mortars is simulated adopting a numerical approach that incorporates strain rate-dependent damage in a continuum micromechanics framework. The simulated dynamic compressive strengths and energy absorption capacities for mortars with various iron powder content exhibit good correlation with the experimental observations thereby validating the efficacy of the simulation approach

LDIP cooperates with SEIPIN and LDAP to facilitate lipid droplet biogenesis in Arabidopsis

Abstract Cytoplasmic lipid droplets (LDs) are evolutionarily conserved organelles that store neutral lipids and play critical roles in plant growth, development, and stress responses. However, the molecular mechanisms underlying their biogenesis at the endoplasmic reticulum (ER) remain obscure. Here we show that a recently identified protein termed LD-associated protein [LDAP]-interacting protein (LDIP) works together with both endoplasmic reticulum-localized SEIPIN and the LD-coat protein LDAP to facilitate LD formation in Arabidopsis thaliana. Heterologous expression in insect cells demonstrated that LDAP is required for the targeting of LDIP to the LD surface, and both proteins are required for the production of normal numbers and sizes of LDs in plant cells. LDIP also interacts with SEIPIN via a conserved hydrophobic helix in SEIPIN and LDIP functions together with SEIPIN to modulate LD numbers and sizes in plants. Further, the co-expression of both proteins is required to restore normal LD production in SEIPIN-deficient yeast cells. These data, combined with the analogous function of LDIP to a mammalian protein called LD Assembly Factor 1, are discussed in the context of a new model for LD biogenesis in plant cells with evolutionary connections to LD biogenesis in other eukaryotes