Pivoting colloidal assemblies exhibit mechanical metamaterial behaviour

Biological machines use targeted deformations that can be actuated by Brownian fluctuations. However, although synthetic micromachines can similarly make use of targeted deformations, they are too stiff to be driven by thermal fluctuations and require strong forcing1,2,3. Furthermore, systems that are able to change their conformation by thermal fluctuations do so uncontrollably4,5 or require external control6. Here we use DNA-based sliding contacts7,8,9 to create colloidal pivots, rigid anisotropic objects that freely fluctuate around their pivot point and use a hierarchical strategy to assemble these into Brownian metamaterials with targeted deformation modes. We realize the archetypical rotating diamond and rotating triangle, or kagome, geometries and quantitatively show how thermal fluctuations drive their predicted auxetic deformations10,11,12,13,14,15. Finally, we implement magnetic particles into the colloidal pivots to achieve colloidal metamaterials that can be controlled externally as well as use Brownian fluctuations for precisely controlled shape changes. Together, our work introduces a strategy for creating Brownian mechanical metamaterials with easily actuatable deformation modes

Device Performance of Emerging Photovoltaic Materials (Version 6)

This 6th annual Emerging PV Report surveys peer-reviewed advances since August 2024 across perovskite, organic, kesterite, matildite, antimony seleno-sulfide, selenium, and tandem solar cell architectures. Updated graphs, tables, and analyses compile the best-performing devices from the emerging-pv.org database, benchmarking power conversion efficiency (PCE), flexible photovoltaic fatigue factor (F), light-utilization efficiency (LUE), and stability-test energy yield (STEY) against detailed-balance efficiency limits as functions of photovoltaic bandgap, and average visible transmittance (AVT) for (semi-)transparent devices. Beyond efficiency, operational stability is assessed via degradation rates (DR) and t95 lifetimes. Highlights include single-junction perovskite cells with efficiencies above 27%, organics surpassing 20%, and new Si/perovskite tandems exceeding 34%. Although multiple record efficiencies have been achieved this year, advances in mechanical robustness and operational stability remain inconsistent, especially in complex tandem stacks, emphasizing the urgent need for standardized protocols, improved large-area homogeneity, and database-driven benchmarks to accelerate the transition from laboratory demonstrations to scalable, real-world deployment

Timekeeping precision enhancements at constant power

We study the precision of a noisy clock comprising laser-driven coupled optical cavities sustaining limit cycles. We quantify the timekeeping precision of this system via the standard deviation of the limit-cycle period and demonstrate how it changes when varying the cavity length. We find timekeeping precision enhancements at constant power and regardless of the operation frequency. Through a phase space analysis of the limit-cycle fluctuations, we reveal how the proximity of different bifurcations determines the timekeeping precision of our system regardless of the input power and oscillation frequency. We expect our results to assist in the design of clocks that must operate in the presence of strong fluctuations, such as small clocks influenced by thermal noise. While fluctuations inevitably limit the maximum precision that can be attained, our results elucidate how that limited precision can be substantially enhanced while maintaining the energy efficiency and operation frequency of the clock

Polycarbazole–NiOOH Interfacial Engineering of BiVO4 Photoanodes for Efficient and Stable Solar Water Splitting

Photoelectrochemical (PEC) water splitting is a promising strategy for sustainable hydrogen and chemical production. Among candidate photoanodes, bismuth vanadate (BVO) offers a suitable band gap (~2.4 eV) and favorable band-edge alignment for water oxidation, yet its performance remains limited by inefficient charge separation, severe surface recombination, and sluggish interfacial kinetics. Here, these challenges are addressed through the electro-polymerization of a π-conjugated carbazole (p-CBZ) layer on BVO, forming a p–n heterojunction that enhances the built-in electric field, accelerates hole transport, and passivates surface defects. The subsequent deposition of NiOOH as an oxygen evolution co-catalyst further promotes charge transfer and catalytic activity. The resulting BVO/p-CBZ/NiOOH hybrid photoanode achieves a photocurrent density of 5.6 mA cm−2 at 1.23 V versus RHE under 1 sun illumination and maintains stable water oxidation for over 72 h. Further mechanistic insights using electrochemical impedance (EIS) and light-modulated spectroscopies (IMPS/IMVS) confirm that p-CBZ markedly improves charge separation and carrier diffusion, while NiOOH facilitates oxygen evolution. This synergistic design significantly enhances PEC performance, highlighting π-conjugated carbazole polymers as effective hole extraction and passivation layers in BVO-based photoanodes for efficient and durable solar-driven water splitting

Near-resonant nuclear spin detection with megahertz mechanical resonators

Mechanical resonators operating in the megahertz range have become a versatile platform for fundamental and applied quantum research. Their exceptional properties, such as low mass and high quality factor, make them also appealing for force sensing experiments. In this work, we propose a method for detecting, and ultimately controlling, nuclear spins by coupling them to megahertz resonators via a magnetic field gradient. Dynamical backaction between the sensor and an ensemble of N nuclear spins produces a shift in the sensor's resonance frequency. The mean frequency shift due to the Boltzmann polarization is challenging to measure in nanoscale sample volumes. Here, we show that the fluctuating polarization of the spin ensemble results in a measurable increase of the resonator's frequency variance. On the basis of analytical as well as numerical results, we predict that the variance measurement will allow single nuclear spin detection with existing resonator devices

How Crystal Size and Number Steer Asymmetric Crystallization

Chiral amplification processes during crystallization can hinge on subtle asymmetries in crystal populations, yet the underlying kinetic drivers remain elusive. Here we experimentally investigate how size and mass imbalances between two enantiomeric crystal populations translate to asymmetric growth rates that determine asymmetric crystal growth. We find that the interplay between imbalances in size and mass can yield positive, linear or even negative nonlinear chiral amplification. Consequently, though small crystals have a thermodynamically higher solubility than large ones, a minority population of small crystals can collectively outgrow and ultimately dominate a majority of larger crystals. This amplification due to size effects can be further enhanced or dampened by controlling growth rates. Our findings uncover an intricate kinetic selection mechanism driven by population-level growth rates and governed by fundamental crystallization dynamics. Together, these results provide new insights into the origin of nonlinear amplification phenomena and offer practical guidance for competitive asymmetric crystallization and self-assembly processes

Digital Holography Using Harmonic Generation from Solids for Reconstruction of Subwavelength Nanostructures

Digital holographic microscopy (DHM) is a successful technique frequently used to assess the phase in imaging experiments. Combining DHM with nonlinear generation opens the possibility of measuring phases in nonlinear processes such as high-harmonic generation and characterizing nanostructures with an increased sensitivity. In this paper, we demonstrate that the combination of DHM and harmonic generation from solids can be used to reliably perform 3D reconstructions of samples and also investigate structural parameters of subwavelength periodic structures with improved accuracy. We were able to discriminate gratings etched in silicon, with only a few tens of nanometers change in critical dimension, down to a pitch of 400 nm, which is well below the wavelength of the near-infrared (NIR) probing laser source. This technique can in principle be used with all high-harmonic-emitting materials and is expected to reach even larger gains in resolution by probing higher-order harmonics. These results pave the way for sensing of subwavelength structures via nonlinear light generation, for instance, in the semiconductor industry

Flexel ecosystem: Simulating mechanical systems made from entities with arbitrarily complex mechanical responses

Nonlinearities and instabilities in mechanical structures have shown great promise for embedding advanced functionalities. However, simulating structures subject to nonlinearities can be challenging due to the complexity of their behavior, such as large shape changes, effect of pre-tension, negative stiffness and instabilities. While traditional finite element analysis is capable of simulating a specific nonlinear structure quantitatively, it can be costly and cumbersome to use due to the high number of degrees of freedom involved. We propose a framework to facilitate the exploration of highly nonlinear structures under quasistatic conditions. In our framework, models are simplified by introducing ‘flexels’, elements capable of intrinsically representing the complex mechanical responses of compound structures. By extending the concept of nonlinear springs, flexels can be characterized by multi-valued response curves, and model various mechanical deformations, interactions and stimuli, e.g., stretching, bending, contact, pneumatic actuation, and cable-driven actuation. We demonstrate that the versatility of the formulation allows to model and simulate, with just a few elements, complex mechanical systems such as pre-stressed tensegrities, tape spring mechanisms, interaction of buckled beams and pneumatic soft gripper actuated using a metafluid. With the implementation of the framework in an easy-to-use Python library, we believe that the flexel formulation will provide a useful modeling approach for understanding and designing nonlinear mechanical structures

Ultrafast Control of Coherent Acoustic Lattice Dynamics in the Transition Metal Dichalcogenide Alloy WSSe

Coherent acoustic phonons (CAPs)—-vibrational modes prepared in a coherent state that propagate as long-wavelength strain waves—can dynamically modulate crystal structure and, in some cases, symmetry, offering unique opportunities for controlling material properties. We investigate CAP generation in exfoliated multilayer flakes of the alloy tungsten sulfide selenide (mathematical equation, hereafter WSSe). Using high-fluence 400 nm excitation together with ultrafast transient-reflection spectroscopy, we track the coupled carrier-lattice response, revealing dynamics consistent with a sequence of rapid carrier thermalization and exciton formation, phonon recycling, and photoinduced stress from prompt deformation potential and slower thermoelastic contributions that combined drive a coherent oscillation at 27 GHz. The fractional amplitude of the oscillatory component attributed to an acoustic mode in a coherent state is substantially larger in WSSe than in the parent crystals WS2 and WSe2, where the coherent contribution represents only a minor perturbation superimposed on a dominant monotonic background. This pronounced enhancement indicates that the alloy does not behave as a simple interpolation between the binary compounds, but instead exhibits an emergent optical-acoustic response linked to chalcogen mixing. Finally, by implementing a two-pulse excitation scheme, we demonstrate optical control of the CAP phase and amplitude, highlighting the potential of TMDC alloys to support dynamic modulation of optomechanical and acoustic responses for advanced device engineering

In-sensor computing with halide perovskite-based optoelectronic reservoir networks

The bigger picture Detecting and classifying data, for example, from video cameras, is a key capability for many applications using artificial intelligence, including robotics, self-driving cars, image detection, and biometrics. However, the impressive progress in the capabilities of artificial intelligence comes at the cost of rapidly increasing energy consumption. A large contributor to this energy consumption is the transfer of data from sensors to processors in order to detect or classify the input data. In this work, we demonstrate a microscale halide perovskite semiconductor device that simultaneously senses and processes information. The information can be provided as electrical or optical input, and we show that the classification accuracy is highest if the two inputs are combined. This resembles how the brain merges information from, for example, sight and touch to gain a better understanding of the world. Summary: Physical reservoir computing can provide efficient neuromorphic in- and near-sensor computing applications. Typically, reservoir networks are designed to process light or voltage inputs. Here, we demonstrate a multimodal optoelectronic reservoir network based on halide perovskite semiconductor devices capable of processing voltage and light inputs, which is also scalable for constructing high-density sensor arrays. The devices consist of micrometer-sized, asymmetric crossbars covered with a methylammonium lead iodide (MAPbI3) perovskite film. Using 4-bit inputs and linear readout layers for classification, we demonstrate multimodal networks capable of processing both voltage and light inputs. The networks reach mean accuracies up to 95.3% ± 0.1% and 87.8% ± 0.1% for image and video classification, respectively. The networks significantly outperformed linear classifier references by 3.1% for images and 14.6% for video. We show that longer retention times benefit classification accuracy for single-mode networks and give guidelines for choosing optimal experimental parameters.</p