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Deracemization by coupling electrochemically assisted racemization and asymmetric crystallization
Full text linkAmino acid derivatives of tert-leucine and phenyl glycine along with plant growth retardant and the precursor of fungicide paclobutrazol are deracemized by combining in situ electrochemical base generation to induce racemization and crystallization-induced chiral amplification in a one-pot, two-step deracemization procedure. Full enantioselective conversion (e.e. > 99%) of a mixture of enantiomers towards the desired handedness is achieved
Characterization of Mobile Ions in Perovskite Solar Cells with Capacitance and Current Measurements by Approximating Drift-Diffusion Simulations
Full text linkThe migration of mobile ions is one of the leading causes of the degradation of perovskite solar cells. However, quantifying mobile ions in complete perovskite solar cells is challenging due to the complex device stacks and the impact of charge transport layers on the measurement techniques. Here we develop a simple and openly accessible step model that approximates drift-diffusion simulations. The step model is based on the expression of the charge density in the ionic and electronic accumulation and depletion layers as step functions. We can then accurately determine the impact of mobile ions on the dc potential distribution of perovskite solar cells. Furthermore, we can simulate electrical measurement techniques commonly used to quantify mobile ions: capacitance transient, current transient, and capacitance-frequency measurements. By validating the step model with drift-diffusion simulations, we show that an accurate extraction of ion density, diffusion coefficient, and activation energy is possible in an accessible range. We finally apply the step model developed to estimate the ionic conductivity and activation energy of perovskite solar cells
Exploiting bias in optimal finite-time copying protocols
Full text linkWe study a finite-time cyclic copy protocol that creates persisting correlations between a memory and a data bit. The average work to copy the two states of the data bit consists of the mutual information created between the memory and data bit after copying, a cost due to the difference between the initial and final states of the memory bit, and a finite-time cost. At low copy speeds, the optimal initial distribution of the memory bit matches the bias in the expected outcome, set by the bias in the data bit and the copying accuracies. However, if both states of the data are copied with the same accuracy, then in the high-speed regime copying the unlikely data bit state becomes prohibitively costly with a biased memory; the optimal initial distribution is then pushed towards 50:50. Copying with unequal accuracies, at fixed copy-generated mutual information, yields an opposite yet more effective strategy. Here, the initial memory distribution becomes increasingly biased as the copy speed increases, drastically lowering the work and raising the maximum speed. This strategy is so effective that it induces a symmetry breaking transition for an unbiased data bit
A travelling-wave strategy for plant–fungal trade
Full text linkFor nearly 450 million years, mycorrhizal fungi have constructed networks to collect and trade nutrient resources with plant roots1,2. Owing to their dependence on host-derived carbon, these fungi face conflicting trade-offs in building networks that balance construction costs against geographical coverage and long-distance resource transport to and from roots3. How they navigate these design challenges is unclear4. Here, to monitor the construction of living trade networks, we built a custom-designed robot for high-throughput time-lapse imaging that could track over 500,000 fungal nodes simultaneously. We then measured around 100,000 cytoplasmic flow trajectories inside the networks. We found that mycorrhizal fungi build networks as self-regulating travelling waves—pulses of growing tips pull an expanding wave of nutrient-absorbing mycelium, the density of which is self-regulated by fusion. This design offers a solution to conflicting trade demands because relatively small carbon investments fuel fungal range expansions beyond nutrient-depletion zones, fostering exploration for plant partners and nutrients. Over time, networks maintained highly constant transport efficiencies back to roots, while simultaneously adding loops that shorten paths to potential new trade partners. Fungi further enhance transport flux by both widening hyphal tubes and driving faster flows along ‘trunk routes’ of the network5. Our findings provide evidence that symbiotic fungi control network-level structure and flows to meet trade demands, and illuminate the design principles of a symbiotic supply-chain network shaped by millions of years of natural selection
Behind the scenes of cellular organization: Quantifying spatial phenotypes of puncta structures with statistical models including random fields
Full text linkThe cellular interior is a spatially complex environment shaped by nontrivial stochastic and biophysical processes. Within this complexity, spatial organizational principles—also called spatial phenotypes—often emerge with functional implications. However, identifying and quantifying these phenotypes in the stochastic intracellular environment is challenging. To overcome this challenge for puncta, we discuss the use of inference of point-process models that link the density of points to other imaged structures and a random field that captures hidden processes. We apply these methods to simulated data and multiplexed immunofluorescence images of Vero E6 cells. Our analysis suggests that peroxisomes are likely to be found near the perinuclear region, overlapping with the endoplasmic reticulum, and located within a distance of 1 μm to mitochondria. Moreover, the random field captures a hidden variation of the mean density in the order of 15 μm. This length scale could provide critical information for further developing mechanistic hypotheses and models. By using spatial statistical models including random fields, we add a valuable perspective to cell biology
Template-Assisted Growth of CsxFA1-xPbI3 with Pulsed Laser Deposition for Single Junction Perovskite Solar Cells
Full text linkCesium-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
Enhanced near infrared light trapping in Si solar cells with metal nanowire grid front electrodes
No full textThis work focuses on the optical performance of silver nanowire grids as front solar cell electrodes in a realistic dielectric environment. To do so, we first demonstrate the successful integration of the metallic grids on an ITO-free c-Si solar cell with arbitrarily high nanowires by light-induced electroplating in nano-imprinted polymer masks. The bottom-up approach enables the fabrication of high-aspect ratio grids with estimated very low sheet resistance (95%). We use tunnel oxide passivating contact (TOPCon2) silicon cells as a platform to probe the grid's transparency. External quantum efficiency maps together with optical simulations reveal that the grids’ transparency in the visible spectral range is greater than expected from geometrical shading due to the sub-wavelength cross-section of the metal nanowires. On top of that, the nanowire grid even enhances the photocurrent at the near infrared, as a result of the increased optical path length from the grid's diffraction. Lastly, we demonstrate that the cell's photocurrent is unaffected by the angle of illumination up to about 40°, which is the relevant range for encapsulated cells in solar panels. Our findings highlight the dual role of metal nanowire grids as both electrical conductors and optical enhancers, offering substantial potential for future photovoltaic technologies
Microscopic Imprints of Learned Solutions in Tunable Networks
Full text linkIn 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
Beyond Spectral Resolution in Nanophotonic Sensing: Picometer-Level Precision with Multispectral Readout
Full text linkNanophotonic sensors offer precision, remote read-out, and immunity to electromagnetic interference but face adoption challenges due to complex, costly readout instrumentation, typically based on high resolution. This article challenges the notion that high spectral resolution is necessary for high-performance optical sensing. We propose co-optimizing the line widths of the sensor and readout to achieve picometer-level precision using low-resolution multispectral detector arrays and incoherent light sources. This approach is validated in temperature sensing, fiber-tip refractive index sensing, and biosensing with nanophotonic transducers, achieving superior precision to high-resolution spectrometers. This paradigm change in readout will enable optical sensing systems with costs and dimensions comparable to electronic sensors
Programmable synthetic magnetism and chiral edge states in nano-optomechanical quantum Hall networks
Full text linkArtificial magnetic fields break time-reversal symmetry in engineered materials—also known as metamaterials, enabling robust, topological transport of neutral excitations, much like edge channels facilitate electronic conduction in the integer quantum Hall effect. We experimentally demonstrate the emergence of quantum-Hall-like chiral edge states in optomechanical resonator networks. Synthetic magnetic fields for phononic excitations are induced through laser drives, while cavity optomechanical control allows full reconfigurability of the effective metamaterial response of the networks, including programming of magnetic fluxes in multiple resonator plaquettes. By tuning the interplay between network connectivity and magnetic fields, we demonstrate both flux-sensitive and flux-insensitive localized mechanical states. Scaling up the system creates spectral features that are precursors to Hofstadter butterfly spectra. Site-resolved spectroscopy reveals edge-bulk separation, with stationary phononic distributions signaling chiral edge modes. We directly probe those edge modes in transport measurements to demonstrate a unidirectional acoustic channel. This work unlocks new ways of controlling topological phononic phases at the nanoscale with applications in noise management and information processing