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Abstract Organic mixed ionic-electronic conductors (OMIECs) are a class of materials that can transport ionic and electronic charge carriers simultaneously. They have shown broad applications in soft robotics, electrochemical transistors, and bio-electronics. The structural response of OMIECs to the mixed conduction populates from molecular conformation to devices, presenting challenges in understanding their mechanical behavior and constitutive descriptions. Furthermore, OMIECs feature strong multiphysics interactions among mechanics, electrostatics, charge conduction, mass transport, and microstructural evolution. In this review, we summarize recent progress in mechanistic understanding of OMIECs and highlight dynamics and heterogeneity underlying each element of mechanics. We introduce strain activation and breathing, mechanical properties, and degradation of OMIECs upon electrochemical doping and dedoping. Drawing on the state-of-the-art experimental and simulation insights, we highlight the critical role of multiscale dynamics in governing the functionality of OMIECs. We discuss the current understanding and limitation of constitutive relations and present computational frameworks that integrate multiphysics. We synthesize mechanics-driven strategies—spanning strain modulation, material stretchability, and interfacial stability—from molecular design to macroscopic structural engineering. We conclude with our perspective on the outstanding questions and key challenges for continued research. This review aims to organize the fundamental mechanical principles of OMIECs, offering a multidisciplinary framework for researchers to identify, analyze, and address mechanical challenges in mixed conducting polymers and their applications. 

Biocide use is essential and ubiquitous, exposing microbes to sub-inhibitory concentrations of antiseptics, disinfectants, and preservatives. This can lead to the emergence of biocide resistance, and more importantly, potential cross-resistance to antibiotics, although the degree, frequency, and mechanisms that give rise to this phenomenon are still unclear. Here, we systematically performed adaptive laboratory evolution of the gut bacteria Escherichia coli in the presence of sub-inhibitory, constant concentrations of ten widespread biocides. Our results show that 17 out of 40 evolved strains (43%) also decreased the susceptibility to medically relevant antibiotics. Through whole-genome sequencing, we identified mutations related to multidrug efflux proteins ( mdfA and acrR ), porins ( envZ and ompR ), and RNA polymerase ( rpoA and rpoBC ), as mechanisms behind the resulting (cross)resistance. We also report an association of several genes ( yeaW , pyrE , yqhC , aes , pgpA , and yeeP - isrC ) and specific mutations that induce cross-resistance, verified through mutation repairs. A greater capacity for biofilm formation with respect to the parent strain was also a common feature in 11 out of 17 (65%) cross-resistant strains. Evolution in the biocides chlorophene, benzalkonium chloride, glutaraldehyde, and chlorhexidine had the most impact in antibiotic susceptibility, while hydrogen peroxide and povidone-iodine the least. No cross-resistance to antibiotics was observed for isopropanol, ethanol, sodium hypochlorite, and peracetic acid. This work reinforces the link between exposure to biocides and the potential for cross-resistance to antibiotics, presents evidence on the underlying mechanisms of action, and provides a prioritized list of biocides that are of greater concern for public safety from the perspective of antibiotic resistance. Significance Statement Bacterial resistance and decreased susceptibility to antimicrobials is of utmost concern. There is evidence that improper biocide (antiseptic and disinfectant) use and discard may select for bacteria cross-resistant to antibiotics. Understanding the cross-resistance emergence and the risks associated with each of those chemicals is relevant for proper applications and recommendations. Our work establishes that not all biocides are equal when it comes to their risk of inducing antibiotic resistance; it provides evidence on the mechanisms of cross-resistance and a risk assessment of the biocides concerning antibiotic resistance under residual sub-inhibitory concentrations. 

Abstract How to design experiments that accelerate knowledge discovery on complex biological landscapes remains a tantalizing question. We present an optimal experimental design method (coined OPEX) to identify informative omics experiments using machine learning models for both experimental space exploration and model training. OPEX-guided exploration ofEscherichia coli’s populations exposed to biocide and antibiotic combinations lead to more accurate predictive models of gene expression with 44% less data. Analysis of the proposed experiments shows that broad exploration of the experimental space followed by fine-tuning emerges as the optimal strategy. Additionally, analysis of the experimental data reveals 29 cases of cross-stress protection and 4 cases of cross-stress vulnerability. Further validation reveals the central role of chaperones, stress response proteins and transport pumps in cross-stress exposure. This work demonstrates how active learning can be used to guide omics data collection for training predictive models, making evidence-driven decisions and accelerating knowledge discovery in life sciences.