Nanoscale Heat Flow and Thermometry in Laser-Heated Resonant Silicon Mie Nanospheres Probed with Spatially Resolved Cathodoluminescence Spectroscopy

Many nanoscale technologies depend critically on precise knowledge and control of local temperature and heat flow, making robust nanothermometry essential for designing, optimizing, and ensuring the reliability of next-generation devices. In this work, we introduce a correlative method that combines laser excitation with scanning electron microscopy-based cathodoluminescence (SEM-CL) to probe photothermal effects in situ with nanoscale spatial resolution. We analyze the spatially resolved CL (30 keV) of resonant Mie modes in single silicon nanoparticles under continuous-wave laser irradiation (λ = 442 nm). The 235–250-nm-diameter crystalline nanospheres, placed on a Si3N4 membrane, show a strong electric quadrupole CL resonance of which the peak wavelength reversibly red-shifts upon laser-induced heating. A temperature of up to 585 ± 12 °C is derived from the spectral shifts for the highest laser power used (9.6 mW, ∼1 × 106 W/cm2 at the substrate). Numerical heat flow simulations show that the measured steady-state temperatures are consistent with a geometry in which heat flow occurs through a contact area of up to 100 nm2, depending on laser power, between the Si nanosphere and the Si3N4 membrane. We postulate that this contact forms by reshaping of the particle–membrane geometry as it heats up in the initial phase of the laser irradiation, leading to an equilibrium geometry that results in the measured steady-state temperature. This work shows that CL of resonant nanostructures in combination with simulations can serve as sensitive probes of temperature and thermal conductivity. Spatially resolved CL nanothermometry in a SEM enables studies of nanoscale thermal properties of a wide range of device geometries such as electronic integrated circuits, surface catalysts, photovoltaic devices, and more

Proteome-wide determinants of co-translational chaperone binding in bacteria

Chaperones are essential to the co-translational folding of most proteins. However, the principles of co-translational chaperone interaction throughout the proteome are poorly understood, as current methods are restricted to few substrates and cannot capture nascent protein folding or chaperone binding sites, precluding a comprehensive understanding of productive and erroneous protein biosynthesis. Here, by integrating genome-wide selective ribosome profiling, single-molecule tools, and computational predictions using AlphaFold we show that the binding of the main E. coli chaperones involved in co-translational folding, Trigger Factor (TF) and DnaK correlates with "unsatisfied residues" exposed on nascent partial folds - residues that have begun to form tertiary structure but cannot yet form all native contacts due to ongoing translation. This general principle allows us to predict their co-translational binding across the proteome based on sequence only, which we verify experimentally. The results show that TF and DnaK stably bind partially folded rather than unfolded conformers. They also indicate a synergistic action of TF guiding intra-domain folding and DnaK preventing premature inter-domain contacts, and reveal robustness in the larger chaperone network (TF, DnaK, GroEL). Given the complexity of translation, folding, and chaperone functions, our predictions based on general chaperone binding rules indicate an unexpected underlying simplicity

Interferon-responsive intestinal BEST4/CA7+ cells are targets of bacterial diarrheal toxins

BEST4/CA7+ cells of the human intestine were recently identified by single-cell RNA sequencing. While their gene expression profile predicts a role in electrolyte balance, BEST4/CA7+ cell function has not been explored experimentally owing to the absence of BEST4/CA7+ cells in mice and the paucity of human in vitro models. Here, we establish a protocol that allows the emergence of BEST4/CA7+ cells in human intestinal organoids. Differentiation of BEST4/CA7+ cells requires activation of Notch signaling and the transcription factor SPIB. BEST4/CA7+ cell numbers strongly increase in response to the cytokine interferon-γ, supporting a role in immunity. Indeed, we demonstrate that BEST4/CA7+ cells generate robust CFTR-mediated fluid efflux when stimulated with bacterial diarrhea-causing toxins and find the norepinephrine-ADRA2A axis as a potential mechanism in blocking BEST4/CA7+ cell-mediated fluid secretion. Our observations identify a central role of BEST4/CA7+ cells in fluid homeostasis in response to bacterial infections

Template-Assisted Growth of CsxFA1-xPbI3 with Pulsed Laser Deposition for Single Junction Perovskite Solar Cells

Cesium-formamidinium lead iodide (CsxFA1-xPbI3) perovskites are a promising methylammonium-free alternative for efficient single-junction solar cells. However, they have not been fully explored by vapor-phase deposition techniques. Herein, a template-assisted approach is demonstrated for the growth of CsxFA1-xPbI3 perovskite films using pulsed laser deposition (PLD) from a single-source target of mixed precursors. Implementing a lead iodide (PbI2) + CsxFA1-xPbI3 tailored template, phase-pure CsxFA1-xPbI3 films with uniform coverage on both planar and textured substrates are achieved. Compositional analysis via X-ray fluorescence confirms near-stoichiometric transfer of the inorganic cations (Cs/Pb), with identical Cs0.2FA0.8PbI3 composition and a bandgap of 1.58 eV achieved in templated and non-templated films. However, the presence of the template proves essential for attaining phase-pure films in the photoactive cubic (α-) phase. Proof-of-concept solar cells fabricated with templated-PLD α-CsxFA1-xPbI3 achieve an efficiency exceeding 12.9% on 0.1 cm2 area devices without the employment of passivation approaches. Additionally, increasing deposition rates does not alter the phase, morphology, or optoelectronic properties of the templated films on textured substrates, indicating the robustness of this methodology. The compositional control of PLD for Cs-FA-based perovskites is showcased, and template-assisted growth is demonstrated as a reliable pathway to high-quality reproducible perovskite films

Transition graphs of interacting hysterons: structure, design, organization and statistics

Transition graphs capture the memory and sequential response of multistable media, by specifying their evolution under external driving. Microscopically, collections of bistable elements, or hysterons, provide a powerful model for these materials, with recent work highlighting the crucial role of hysteron interactions. Here, we introduce a general framework that links transition graphs and the microscopic parameters of interacting hysterons. We first introduce a systematic framework, based on so-called scaffolds, which structures the space of transition graphs and provides tools to deal with their combinatorial explosion. We then connect the topology of transition graphs to partial orders of the microscopic parameters. This allows us to understand the statistical properties of transition graphs, as well as determine whether a given graph is realizable, i.e. compatible with the hysteron framework. Our approach paves the way for a deeper theoretical understanding of memory effects in complex media and opens a route to rationally design pathways and memory effects in materials

Enantiopurity by Directed Evolution of Crystal Stabilities and Nonequilibrium Crystallization

Crystallization is a powerful method to isolate enantiopure molecules from racemates if enantiomers self-sort into separate enantiopure crystals. Unfortunately, this behavior is unpredictable and rare (5–10%), as both enantiomers predominantly crystallize together to form racemic crystals, hindering any such chiral sorting. These unfavorable statistics might be overcome using nonequilibrium conditions. Therefore, we systematically characterize energy differences (ΔGΦ) between racemic and enantiopure crystal phases for libraries of target molecules (phenylglycine, praziquantel) with different chemical modifications. Surprisingly, these libraries reveal wide but similar continuous distributions of ΔGΦ, wherein similar chemical modifications group together. This grouping allows a directed evolution strategy to discover racemic crystals with low ΔGΦ for isolating desired enantiomers by crystallization under nonequilibrium conditions. Comparison with over a hundred previously reported compounds suggests that as many as half of all chiral molecules may kinetically form enantiopure crystals (∼50%). These insights open new previously unconsidered possibilities for isolating enantiopure molecules

Quantifying the nuclear localization of fluorescently tagged proteins

Motivation Cells are dynamic, continually responding to intra- and extracellular signals. Measuring the response to these signals in individual cells is challenging. Signal transduction is fast, but reporters for downstream gene expression are slow: fluorescent proteins must be expressed and mature. An alternative is to fluorescently tag and monitor the intracellular locations of transcription factors and other effectors. These proteins enter or exit the nucleus in minutes, after upstream signalling modifies their phosphorylation state. Although such approaches are increasingly popular, there is no consensus on how to quantify nuclear localization. Results Using budding yeast, we developed a convolutional neural network that determines nuclear localization from fluorescence and, optionally, bright-field images. Focusing on changing extracellular glucose, we generated ground-truth data using strains with a transcription factor and a nuclear protein tagged with fluorescent markers. We showed that the neural network-based approach outperformed seven published methods, particularly when predicting single-cell time series, which are key to determining how cells respond. Collectively, our results are conclusive - using machine learning to automatically determine the appropriate image processing consistently outperforms ad hoc approaches. Adopting such methods promises to both improve the accuracy and, with transfer learning, the consistency of single-cell analyses

Prospecting for pluripotency in metamaterial design

From self-assembly and protein folding to combinatorial metamaterials, a key challenge in material design is finding the right combination of interacting building blocks that yield targeted properties. Such structures are fiendishly difficult to find—not only are they rare, but often the design space is so rough that gradients are useless and direct optimization is hopeless. Here, we design ultrarare combinatorial metamaterials, capable of multiple desired deformations, by introducing a twofold strategy that avoids the drawbacks of direct optimization. We first combine convolutional neural networks with genetic algorithms to prospect for metamaterial designs with a potential for high performance; in our case, these metamaterials have a high number of spatially extended modes—they are pluripotent. Second, we exploit this library of pluripotent designs to generate metamaterials with multiple target deformations, which we finally refine by strategically placing defects. Our multishape metamaterials would be impossible to design through trial-and-error or standard optimization. Instead, our data-driven approach is systematic and ideally suited to tackling the large and intractable combinatorial problems that are pervasive in material science

Microscopic Imprints of Learned Solutions in Tunable Networks

In physical networks trained using supervised learning, physical parameters are adjusted to produce desired responses to inputs. An example is an electrical contrastive local learning network of nodes connected by edges that adjust their conductances during training. When an edge conductance changes, it upsets the current balance of every node. In response, physics adjusts the node voltages to minimize the dissipated power. Learning in these systems is therefore a coupled double-optimization process, in which the network descends both a cost landscape in the high-dimensional space of edge conductances and a physical landscape—the power dissipation—in the high-dimensional space of node voltages. Because of this coupling, the physical landscape of a trained network contains information about the learned task. Here, we derive a structure-function relation for trained tunable networks and demonstrate that all the physical information relevant to the trained input-output relation can be captured by a tuning susceptibility, an experimentally measurable quantity. We supplement our theoretical results with simulations to show that the tuning susceptibility is correlated with functional importance and that we can extract physical insight into how the system performs the task from the conductances of highly susceptible edges. Our analysis is general and can be applied directly to mechanical networks, such as networks trained for protein-inspired function such as allostery

Interview with 2025 ACS Energy Lectureship Outstanding Mid-Career Award Winner Dr. Bruno Ehrler

Dr. Bruno Ehrler, the recipient of the 2025 ACS Energy Lectureship Outstanding Mid-Career Award, has established himself as a leading researcher in the field of solar energy materials. Dr. Ehrler has made significant contributions to the understanding of ion migration effects in perovskite solar cells, which is crucial in advancing the efficiency and stability of PSC devices. In this interview, Dr. Ehrler shares insights into his academic path, current research, and perspectives on the future of energy materials scienc