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Light‐Induced Precipitation of an Inorganic Phosphate for Direct Writing of Thin Films and Templating Complex Mineral Morphologies
Full text linkLight-induced direct patterning allows intricate spatiotemporal control over microscopic structures and has even been extended to functional inorganic materials. However, while sol–gel-based materials such as silica maintain structural continuity, photoinduced precipitation of salts such as carbonates and phosphates typically suffers from a granular nature and produces loose particle assemblies. In this study, UV-induced release of phosphoric acid from an organic precursor is exploited for locally modulating supersaturation levels. This allows for controlled interplay between photogeneration and precursor supply, for the precipitation of structurally continuous, non-granular barium phosphate from solution. Based on these insights, nanoscopic thin films with controllable thickness are deposited in an illuminated spot. By moving the light beam, this approach is extended to direct writing based on user-defined patterns. Moreover, by triggering photoinduced mineralization within organic templates, complex morphologies can be replicated with high fidelity. This versatility and precision will open new opportunities for the design of functional, biologically relevant inorganic materials
A Rational Framework to Estimate the Chiroptical Activity of [6]Helicene Derivatives
Full text linkHelicenes are a class of molecules potentially suitable in several technological applications with intrinsic structural chirality, and they are known for their exceptional chiroptical properties, with CD signals being notably more intense than those of other small organic molecules. Accurately estimating the chiroptical properties of helicenes is relevant for the application of these molecules in many diverse fields, yet still challenging. In this paper, we combine experimental optical characterization and ab initio calculations to study how different substituents influence the chiroptical properties of [6]helicene. By systematically varying the size and chemical nature of the substituents, we find that both electron withdrawing and electron-donating substituents red-shift and dwindle the optical activity of the molecule. We hypothesize that the observed dumping in transition energy and intensity is connected to the strength of the perturbation induced by the substituent on the π-conjugation of the aromatic rings. The comparison between experiments and computations allows rationalization of the trends and suggestion of how the substituents influence properties. This work provides a framework for the fine-tuning of helicenes’ chiroptical properties via chemical modification of the substituents, enabling the design of helicene-based systems with tailored optical properties
The Escherichia coli replication initiator DnaA is titrated on the chromosome
Full text linkDNA replication initiation is orchestrated in bacteria by the replication initiator DnaA. Two models for regulation of DnaA activity in Escherichia coli have been proposed: the switch between an active and inactive form, and the titration of DnaA on the chromosome. Although proposed decades ago, experimental evidence of a titration-based control mechanism is still lacking. Here, we first identified a conserved high-density region of binding motifs near the origin of replication, an advantageous trait for titration of DnaA. We then investigated the mobility of DnaA by visualising single proteins inside single cells of wild-type and deletion mutants E. coli strains, while monitoring cellular size and DNA content. Our results indicate that the chromosome of E. coli controls the free amount of DnaA in a growth rate-dependent fashion. Moreover, they address long-standing questions on the relevance of DnaA titration in stabilising DNA replication by preventing re-initiation events during slow growth
Colloidal Crack Sintering Lithography for Light‐Induced Patterning of Particle Assemblies
Full text linkPhotoinduced patterning can generate intricate topographic structures, but it relies heavily on specialized materials, which limits its general usability. Here, colloidal crack sintering lithography (CCSL) is introduced, a patterning method based simply on a commodity polymer and water that interact with focused near-infrared light. Photothermal heating causes nanoscale sintering in polymer colloidal assemblies, which in turn induces controlled formation of cracks on the micron scale. Because light provides spatiotemporal control, individual cracks can be guided along precisely defined trajectories. Furthermore, this photo-driven sintering mechanism can be applied to subsequently convert such cracks into an open channel morphology. The result is reminiscent of positive resists in photolithographic patterning, as the precursor material is selectively removed along the irradiated path. Consequently, CCSL provides analogous functionality to photolithography in templating techniques such as shadow masks in physical vapor deposition and templated colloidal self-assembly
Embodying mechano-fluidic memory in soft machines to program behaviors upon interactions
No full textAnimals show richness in adopted behaviors, which are programmed based on interactions with the external world. For instance, the nematode Caenorhabditis elegans switches between fundamental locomotion modes, such as moving straight and steering, based on the history of external cues and the current surroundings. Soft machines already show feats of passive deformation to external cues. Could a soft machine also remember past interactions using its body, without a processor? We harness the bistability of elastic shells to embody mechano-fluidic memory in soft machines. By storing information of past cues from the external world in their physical body, the machines program new stable locomotion behaviors. When provided with antennae where soft tubes kink and unkink, the machines with embodied memory can detect and avoid obstacles in unknown environments by switching between moving straight and steering, all without software or processors. Embodied memory opens the door to robust, autonomous behaviors that are fully embedded in the nonlinear mechanical structure the machines are made of, for applications ranging from responsive microrobots to reliable space exploration in harsh weather
Quantifying the nuclear localization of fluorescently tagged proteins
Full text linkMotivation 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
Intuitive dissection of the Gaussian information bottleneck method with an application to optimal prediction
Full text linkEfficient signal representation is essential for the functioning of living and artificial systems operating under resource constraints. A widely recognized framework for deriving such representations is the information bottleneck method, which yields the optimal strategy for encoding a random variable, such as the signal, in a way that preserves maximal information about a functionally relevant variable, subject to an explicit constraint on the amount of information encoded. While in its general formulation the information bottleneck method is a numerical scheme, it admits an analytical solution in an important special case where the variables involved are jointly Gaussian. In this setting, the solution predicts discrete transitions in the dimensionality of the optimal representation as the encoding capacity is increased. Although these signature transitions, along with other features of the optimal strategy, can be derived from a constrained optimization problem, a clear and intuitive understanding of their emergence is still lacking. In our work, we advance our understanding of the Gaussian information bottleneck method through multiple mutually enriching perspectives, including geometric and information-theoretic ones. These perspectives offer intuition about the set of optimal encoding directions, the nature of the critical points where the optimal number of encoding components changes, and the way the optimal strategy navigates between these critical points. We then apply our treatment of the information bottleneck to a previously studied signal prediction problem, obtaining insights into how different features of the signal are encoded across multiple components to enable optimal prediction of future signals. Altogether, our work deepens the foundational understanding of the information bottleneck method in the Gaussian setting, motivating the exploration of analogous perspectives in broader, non-Gaussian contexts
Team Science
Full text linkExcellent and impactful science is best accomplished in teams, as the significant challenges of our time must be addressed through interdisciplinary cooperation. But what makes an effective and impactful team? Often, creating a large-scale impact in science requires a large team of, for example, 50–100 people. At the same time, studies indicate that an ideal group consists of only 10–15 members to optimize its impact per person. (1) In much larger teams, it becomes more challenging for the Principal Investigator (PI) to supervise and interact with all team members and guide their research effectively. As a result, very large groups, while impactful because of their size, see diminishing returns in the output per researcher
Vibrational Strong Coupling of Thin Water Layers Using Plasmonic Cavities
Full text linkStrong coupling of molecular vibrations with resonant optical cavities creates vibro-polaritonic states, which can alter chemical reaction rates and product distributions without altering molecular structure. However, so far vibrational strong coupling has only been demonstrated in films of material so thick that the effects have been limited to bulk chemical reactions. Demonstrating vibrational strong coupling in nanometer-scale surface layers of molecules facilitates applying the results of vibro-polaritonic chemistry to the vast majority of industrial chemistry: catalytic reactions occurring at surfaces. Here, highly confined plasmonic cavities are designed and fabricated that are tunable over the entire mid-infrared region. Vibrational strong coupling of water layers that are only 44 nm thick, in both the O─H stretching and bending modes is demonstrated, with Rabi splitting energy of 468 and 282 cm−1, respectively. Detuning experiments show the dispersive behavior of the polaritonic states, and changing the oscillator strength of water molecules by diluting with D2O follows the theoretically predicted change in Rabi splitting energy, assuming a strongly bound layer of water at the surface. We confirm that dry samples annealed in vacuum and measured under nitrogen purging still display a substantial Rabi splitting of 202 cm−1 coming from surface-bound water with an estimated thickness from literature of a few monolayers to a few nanometers, closely approaching the 242 cm−1 threshold for vibrational strong coupling. Such strongly perturbed water layers directly at the surface of metal electrodes broaden the relevant range of reactions where vibro-polaritonic chemistry can be applied and point toward the exciting possibility of a totally new way of altering aqueous electrocatalytic activity for important reactions such as water-splitting and CO2 reduction
Influence of Driving Pulse Properties on Third-Harmonic Diffraction from Quasi-BIC Metasurfaces
Full text linkQuasi-bound states in the continuum in dielectric metasurfaces support sharp Fano resonances that emerge from the interference between bright and dark modes. We exploit this modal interplay to demonstrate tunable third-harmonic emission, controlled through the driving pulse’s wavelength and intensity. Our experiments show imbalances in third-harmonic diffraction patterns and non-Gaussian third-harmonic spectral features that exhibit strong variations near the Fano resonance. We explain the observations via a coupled-oscillator model that captures the interplay between the driving field and the nonlinear response of the modes, explaining our observations and providing a predictive framework for optimizing the third-harmonic diffraction efficiency. These results establish pulse-engineered metasurfaces as a powerful platform for nonlinear wavefront shaping and frequency conversion applications while simultaneously serving as a warning that pulse properties play a vital role in metasurface function design