Solar fuels offer a promising approach to provide sustainable fuels by harnessing sunlight^1,2. Following a decade of advancement, Cu₂O photocathodes are capable of delivering a performance comparable to that of photoelectrodes with established photovoltaic materials^3–5. However, considerable bulk charge carrier recombination that is poorly understood still limits further advances in performance⁶. Here we demonstrate performance of Cu₂O photocathodes beyond the state-of-the-art by exploiting a new conceptual understanding of carrier recombination and transport in single-crystal Cu₂O thin films. Using ambient liquid-phase epitaxy, we present a new method to grow single-crystal Cu₂O samples with three crystal orientations. Broadband femtosecond transient reflection spectroscopy measurements were used to quantify anisotropic optoelectronic properties, through which the carrier mobility along the [111] direction was found to be an order of magnitude higher than those along other orientations. Driven by these findings, we developed a polycrystalline Cu₂O photocathode with an extraordinarily pure (111) orientation and (111) terminating facets using a simple and low-cost method, which delivers 7 mA cm⁻² current density (more than 70% improvement compared to that of state-of-the-art electrodeposited devices) at 0.5 V versus a reversible hydrogen electrode under air mass 1.5 G illumination, and stable operation over at least 120 h.

All-electronicultrafast control of terahertz radiation is demonstrated in integrated metamaterial/graphene devices. By electrostatic gating the graphene conductivity, the overall optical response of the incident terahertz $E$ -field is modified. Depending on the configuration, amplitude, phase, and polarization of terahertz radiation could be modulated with GHz range of reconfiguration speed. An extinction ratio of $>$ 7.6 dB in amplitude is achieved at the resonant frequency of 0.75 THz. Additionally, a relative phase shift of $>\!\!17.4^\circ$ is observed around a frequency of 0.68 THz. When operating as a polarization modulator, the device has reported an ellipticity change of $\sim 40\%$ at a frequency of 0.68 THz and a dynamic rotation of the polarization plane by $>\!\!9^\circ$ at resonance. The switching capability of the modulators has been investigated all electronically reporting a speed exceeding 3 GHz, only limited by the available instrumentation. Consequently, GHz-speed of modulation can be achieved for frequencies around 0.75 THz. These results represent a breakthrough for all applications where a fast, versatile, and efficient modulation of THz radiation is required, such as in next-generation wireless communication, quantum electronics, and ultrafast imaging.

Reliable, clean transfer and interfacing of 2D material layers are technologically as important as their growth. Bringing both together remains a challenge due to the vast, interconnected parameter space. We introduce a fast-screening descriptor approach to demonstrate holistic data-driven optimization across the entirety of process steps for the graphene-Cu model system. We map the crystallographic dependences of graphene chemical vapor deposition, interfacial Cu oxidation to decouple graphene, and its dry delamination across inverse pole figures. Their overlay enables us to identify hitherto unexplored (168) higher index Cu orientations as overall optimal orientations. We show the effective preparation of such Cu orientations via epitaxial close-space sublimation and achieve mechanical transfer with a very high yield (>95%) and quality of graphene domains, with room-temperature electron mobilities in the range of 40000 cm²/(V s). Our approach is readily adaptable to other descriptors and 2D material systems, and we discuss the opportunities of such a holistic optimization.

We present a high-throughput method for identifying and characterizing individual nanowires and for automatically designing electrode patterns with high alignment accuracy. Central to our method is an optimized machine-readable, lithographically processable, and multi-scale fiducial marker system─dubbed LithoTag─which provides nanostructure position determination at the nanometer scale. A grid of uniquely defined LithoTag markers patterned across a substrate enables image alignment and mapping in 100% of a set of >9000 scanning electron microscopy (SEM) images (>7 gigapixels). Combining this automated SEM imaging with a computer vision algorithm yields location and property data for individual nanowires. Starting with a random arrangement of individual InAs nanowires with diameters of 30 ± 5 nm on a single chip, we automatically design and fabricate >200 single-nanowire devices. For >75% of devices, the positioning accuracy of the fabricated electrodes is within 2 pixels of the original microscopy image resolution. The presented LithoTag method enables automation of nanodevice processing and is agnostic to microscopy modality and nanostructure type. Such high-throughput experimental methodology coupled with data-extensive science can help overcome the characterization bottleneck and improve the yield of nanodevice fabrication, driving the development and applications of nanostructured materials.

Twisted bilayer graphene provides an ideal solid-state model to explore correlated material properties and opportunities for a variety of optoelectronic applications, but reliable, fast characterization of the twist angle remains a challenge. Here we introduce spectroscopic ellipsometric contrast microscopy (SECM) as a tool for mapping twist angle disorder in optically resonant twisted bilayer graphene. We optimize the ellipsometric angles to enhance the image contrast based on measured and calculated reflection coefficients of incident light. The optical resonances associated with van Hove singularities correlate well to Raman and angle-resolved photoelectron emission spectroscopy, confirming the accuracy of SECM. The results highlight the advantages of SECM, which proves to be a fast, nondestructive method for characterization of twisted bilayer graphene over large areas, unlocking process, material, and device screening and cross-correlative measurement potential for bilayer and multilayer materials.

Simultaneously recording network activity and ultrastructural changes of the synapse is essential for advancing understanding of the basis of neuronal functions. However, the rapid millisecond‐scale fluctuations in neuronal activity and the subtle sub‐diffraction resolution changes of synaptic morphology pose significant challenges to this endeavor. Here, specially designed graphene microelectrode arrays (G‐MEAs) are used, which are compatible with high spatial resolution imaging across various scales as well as permit high temporal resolution electrophysiological recordings to address these challenges. Furthermore, alongside G‐MEAs, an easy‐to‐implement machine learning algorithm is developed to efficiently process the large datasets collected from MEA recordings. It is demonstrated that the combined use of G‐MEAs, machine learning (ML) spike analysis, and 4D structured illumination microscopy (SIM) enables monitoring the impact of disease progression on hippocampal neurons which are treated with an intracellular cholesterol transport inhibitor mimicking Niemann–Pick disease type C (NPC), and show that synaptic boutons, compared to untreated controls, significantly increase in size, leading to a loss in neuronal signaling capacity.