Joost J. Vlassak

While methods exist to indirectly quantify the metabolism of biological systems, directly measuring metabolic rates in living samples remains challenging. Here, we describe a calorimetric sensor with a sensitivity of ~100 pW at 23.1 mHz, suitable for measurements on living organisms, surpassing previously reported sensitivities. The sensor measures minute temperature differences between a capillary that contains the sample and two reference capillaries, directly relating this temperature difference to the heat produced by the sample. The sensor provides high responsivity (23 to 100 nV/nW), a fast thermal response time (~7.9 s), and supports real-time, long-term monitoring of biological processes, such as the proliferation and growth of small numbers of bacteria. These capabilities offer opportunities to advance our understanding of complex biological phenomena. We demonstrate the utility of the sensor by measuring the growth rate of Escherichia coli . The technique enables estimation of the oxygen consumption rate per cell, the heat production per cell, and the associated contributions from respiration and fermentation. We further show how the growth rate changes in response to different concentrations of chloramphenicol, rifampicin, and ampicillin, three antibiotics with distinct mechanisms of action. The sensor shows significant potential for determining the minimum inhibitory concentrations of antibiotics, performing antibiotic susceptibility testing of pathogens, and enabling fundamental studies of microorganism metabolism.

As Electrification and Renewable Energy adoption accelerate, the electric grid faces growing challenges in delivering clean and reliable power to meet surging demand from sectors such as transportation, industry, and artificial intelligence (AI)-driven computing. However, expanding grid infrastructure remains constrained by regulatory, environmental, and economic barriers. This article explores how AI, advanced conductor materials, energy-aware computing, and energy storage can effectively enhance transmission capacity without new construction. AI enables real-time grid optimization and stability management, while advanced materials like composite-core conductors reduce losses and increase throughput. Additionally, energy-flexible computing dynamically aligns computational workloads with grid conditions, alleviating peak demand. The future integration of energy storage as a transmission asset offers new pathways for congestion management. Together, these innovations form a scalable blueprint for an efficient and sustainable power system that supports sustainable electrification goals.

Electronics for neural signal recording must be robust across multiple and deep brain regions while preserving tissue-level flexibility to ensure stable tracking over months or years. However, existing electronics cannot simultaneously achieve robustness and tissue-level flexibility, limiting their potential for customizable and scalable neuroscience research and clinical applications. Here, we introduce FlexiSoft, an electronic platform based on a plastic-elastomer heterostructure that uniquely integrates mechanical robustness and tissue-level flexibility. Compared to conventional flexible electronics of similar thickness, the FlexiSoft platform demonstrates an order-of- magnitude improvement in both mechanical robustness (critical energy release rate) and flexibility (flexural rigidity). Leveraging these mechanical advantages, we developed FlexiSoft probe for robust implantation, demonstrated by its ability to withstand repeated insertion and removal, as well as to reach centimeter-scale depths comparable to those in the human brain. The platform enables long-term recording from the same neurons across the hippocampus (HPC) and primary motor cortex (M1) during a months-long motor learning task, thereby revealing long-term dynamic changes in neuronal firing patterns. Additionally, FlexiSoft's unique robustness and flexibility enable curved implantation routes, opening new directions of customizable implantation pathways. In summary, we present FlexiSoft as a novel, robust, and tissue-level flexible heterostructure electronics platform that advances flexible brain-computer interfaces (BCIs) with strong translational potential for neuroscience and clinical applications.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

Barocaloric effects─solid-state thermal changes induced by the application and removal of hydrostatic pressure─offer the potential for energy-efficient heating and cooling without relying on volatile refrigerants. Here, we report that dialkylammonium halides─organic salts featuring bilayers of alkyl chains templated through hydrogen bonds to halide anions─display large, reversible, and tunable barocaloric effects near ambient temperature. The conformational flexibility and soft nature of the weakly confined hydrocarbons give rise to order–disorder phase transitions in the solid state that are associated with substantial entropy changes (>200 J kg –1 K –1 ) and high sensitivity to pressure (>24 K kbar –1 ), the combination of which drives strong barocaloric effects at relatively low pressures. Through high-pressure calorimetry, X-ray diffraction, and Raman spectroscopy, we investigate the structural factors that influence pressure-induced phase transitions of select dialkylammonium halides and evaluate the magnitude and reversibility of their barocaloric effects. Furthermore, we characterize the cyclability of thin-film samples under aggressive conditions (heating rate of 3500 K s –1 and over 11,000 cycles) using nanocalorimetry. Taken together, these results establish dialkylammonium halides as a promising class of pressure-responsive thermal materials.

Nickel titanium (NiTi) is a protypical shape-memory alloy used in a range of biomedical and engineering devices, but direct molecular dynamics simulations of the martensitic B19' -> B2 phase transition driving its shape-memory behavior are rare and have relied on classical force fields with limited accuracy. Here, we train four machine-learned force fields for equiatomic NiTi based on the LDA, PBE, PBEsol, and SCAN DFT functionals. The models are trained on the fly during NPT molecular dynamics, with DFT calculations and model updates performed automatically whenever the uncertainty of a local energy prediction exceeds a chosen threshold. The models achieve accuracies of 1-2 meV/atom during training and are shown to closely track DFT predictions of B2 and B19' elastic constants and phonon frequencies. Surprisingly, in large-scale molecular dynamics simulations, only the SCAN model predicts a reversible B19' -> B2 phase transition, with the LDA, PBE, and PBEsol models predicting a reversible transition to a previously uncharacterized low-volume phase, which we hypothesize to be a new stable high-pressure phase. We examine the structure of the new phase and estimate its stability on the temperature-pressure phase diagram. This work establishes an automated active learning protocol for studying displacive transformations, reveals important differences between DFT functionals that can only be detected in large-scale simulations, provides an accurate force field for NiTi, and identifies a new phase.

Living systems are intrinsically nonequilibrium: They use metabolically derived chemical energy to power their emergent dynamics and self-organization. A crucial driver of these dynamics is the cellular cytoskeleton, a defining example of an active material where the energy injected by molecular motors cascades across length scales, allowing the material to break the constraints of thermodynamic equilibrium and display emergent nonequilibrium dynamics only possible due to the constant influx of energy. Notwithstanding recent experimental advances in the use of local probes to quantify entropy production and the breaking of detailed balance, little is known about the energetics of active materials or how energy propagates from the molecular to emergent length scales. Here, we use a recently developed picowatt calorimeter to experimentally measure the energetics of an active microtubule gel that displays emergent large-scale flows. We find that only approximately one-billionth of the system's total energy consumption contributes to these emergent flows. We develop a chemical kinetics model that quantitatively captures how the system's total thermal dissipation varies with ATP and microtubule concentrations but that breaks down at high motor concentration, signaling an interference between motors. Finally, we estimate how energy losses accumulate across scales. Taken together, these results highlight energetic efficiency as a key consideration for the engineering of active materials and are a powerful step toward developing a nonequilibrium thermodynamics of living systems.

We introduce a class of ultra-light and ultra-stiff sandwich panels designed for use in photophoretic levitation applications and investigate their mechanical behavior using both computational analyses and micro-mechanical testing. The sandwich panels consist of two face sheets connected with a core that consists of hollow cylindrical ligaments arranged in a honeycomb-based hexagonal pattern. Computational modeling shows that the panels have superior bending stiffness and buckling resistance compared to similar panels with a basketweave core, and that their behavior is well described by Uflyand-Mindlin plate theory. By optimizing the ratio of the face sheet thickness to the ligament wall thickness, panels maybe obtained that have a bending stiffness that is more than five orders of magnitude larger than that of a solid plate with the same area density. Using a scalable microfabrication process, we demonstrate that panels as large as 3x3 cm^2 with a density of 20 kg/m^3 can be made in a few hours. Micro-mechanical testing of the panels is performed by deflecting microfabricated cantilevered panels using a nanoindenter. The experimentally measured bending stiffness of the cantilevered panels is in very good agreement with the computational results, demonstrating exquisite control over the dimensions, form, and properties of the microfabricated panels.

Ion beam plays a pivotal role in ion implantations and the fabrication of nanostructures. However, there lacks a quantitative model to describe the residual stresses associated with the ion-beam radiation. Radiation-induced residual stress/transformation strain have been mostly recognized in the hydrostatic sub strain space. Here, we use molecular dynamics (MD) simulations to show that the response of a material to irradiation is generally anisotropic that depends on the ion-beam direction, and should be described using tensorial quantities. We demonstrate that accelerator-based ion beam irradiation, combined with the intrinsic lattice anisotropy and externally induced anisotropy (such as anisotropic mechanical loadings), causes radiation-actuated shear transformation strains in addition to hydrostatic expansion. We map out these complex correlations for several materials. Radiation-induced defects are shown to be responsible for residual shear stresses in the manner of Eshelby inclusion transformation. We propose such tensorial response model should be considered for accurate nanoscale fabrication using ion-beam irradiation.