A gated integrating laser-combined scanning tunneling microscope has been developed for high-fidelity investigation of photophysical processes at the atomic scale. This instrument integrates a low-repetition-rate, high-pulse-energy laser with a synchronized gated-integration scheme, enabling selective extraction of the laser-induced tunneling current. To demonstrate its performance, atomically resolved topography and surface photovoltage mapping were simultaneously obtained on the Si(111)-(7 × 7) surface. The surface photovoltage distribution is found to be strongly correlated with the underlying atomic structure. In addition, a highly localized enhancement of the laser-induced tunneling current is observed at a specific single-atom defect site. Detailed analysis, supported by local density of states measurements, reveals that this defect possesses an unusually high density of deep valence-band states. This distinctive electronic structure likely promotes Auger recombination between photogenerated holes accumulated under positive sample bias and electrons tunneling from the tip, thereby giving rise to the observed increase in the local tunneling current.
Mimicking the sophisticated oxidative machinery of natural monooxygenases for high-performance biosensing remains a grand challenge, primarily due to the elusive control over positive charge carriers (holes) required for efficient signal amplification. Here, we report an atomically precise Fe-Cu heteronuclear diatomic catalyst (Fe-Cu-HDC) that unlocks unprecedented analytical sensitivity through a novel asymmetric hole-confinement mechanism. Unlike traditional single-atom catalysts where charge carriers are delocalized, the Fe-Cu interface acts as a decisive "hole trap" spatially concentrating reactive charge carriers at the active center. This localized accumulation triggers the formation of potent high-valent iron species and facilitates barrierless oxygen activation, providing the rapid mineralization of >80% tetracycline within 80 min with a superior reaction rate (Vmax = 0.27 mM min-1). Leveraging this hole-driven enzymatic proficiency, we engineered an integrated 3D-printed platform comprising a continuous-flow filtration reactor and a smartphone-based colorimetric sensor array. This portable device enables the simultaneous rapid mineralization and fingerprint-like discrimination of tetracycline antibiotics in complex real-world matrices (raw milk and livestock wastewater) with a detection limit as low as 30 nM. The platform maintains >90% activity after 18 d of storage and demonstrates laboratory-grade accuracy in raw milk and livestock wastewater. Our findings not only demystify the electronic origin of bio-inspired oxidation but also establish hole-carrier engineering as a transformative dimension for designing the next generation of intelligent, cofactor-free biosensors for on-site diagnostics and environmental surveillance.
Self-assembly, a key approach in material science, enables modulation of nanostructures to achieve distinct materials properties. Atomically precise metal nanoclusters (NCs), consisting of a few metal atoms, exhibit distinctive optical and electronic properties due to their "molecule-like" discrete energy levels. Such NCs offer advantages over conventional metal nanoparticles by avoiding dispersity in size and uncontrolled aggregation. Here we demonstrate a study on crystalline aggregates of Au6 NCs, formed under varying conditions. Notably, the controlled aggregation of these Au6 NCs, synchronising by protonation or employing specific hydrogen bonding, yields self-assembled nanoribbons and percolated networks of nanofibers that further produce extended 3D fibrillar architectures. X-ray scattering and electron microscopy reveal two distinct packing modes: a monoclinic 2D oblique lattice with sparse NC arrangement, and a nematic 2D hexagonal packing, resembling liquid-crystalline rod-like assemblies. The resulting superstructures exhibit enhanced optical responses, retaining the photoluminescence of their constituents, and manifesting third-harmonic generation, slow photoluminescence decay driven by charge-migration effects, and polarization effects influenced by the ordered structure with the periodicity of the NCs. Overall, this work emphasizes the potential of NCs as versatile building blocks for tunable and responsive optoelectronic materials, providing insights into the mechanisms of self-assembly.
We present an all-atom model mimicking superparamagnetic iron oxide nanoparticles functionalized by alkyl phosphonic acids and demonstrate the prediction of grafting density, X-ray diffraction profiles, and solvent-dependent particle radii. The inherent complexity of this nanosystem calls for careful model preparation to avoid bias from human intuition. We suggest a cascade of simulated annealing steps for (i) providing reasonable starting points for the iron oxide particle and (ii) grafting by self-assembled monolayers. The latter was performed as a function of the number of phosphonic acid molecules deposited, thus offering an unbiased assessment of the grafting density and structural alignment of the tail groups that define the outer shell of the nanoparticle. The overall protocol is widely transferable and implies moderate computational costs as compared to the explicit modeling of phosphonic acid association from solution. In turn, solvent effects on the surface structure of the nanoparticle model initially prepared in the gas phase were considered using hexane, propanol, and water, respectively. Voronoi analyses clearly demonstrated the solvent-dependent bundling of the terminal alkyl groups of the phosphonic acids grafted onto iron oxide nanoparticles. To this end, the nanoparticle-solvent interface gives rise to nanometer-scale patterns of differently oriented monolayer structures.
Precisely introducing single-atom sites into metal oxide semiconductors (MOS) is essential for achieving ultrasensitive and selective detection of trace gases; however, the structure-dependent role of these atomically dispersed sites remains insufficiently understood. Here, we report a defect-assisted strategy to construct Ni-modified SnO2 with tunable surface Ni dispersion, enabling a direct correlation between Ni coordination structure and NO2 sensing behavior. Hydrogen treatment of SnO2 (H-SnO2) produces abundant surface defects that stabilize isolated Ni atoms through Ni-O-Sn coordination. Aberration-corrected scanning transmission electron microscopy and X-ray absorption spectroscopy unambiguously confirm the formation of atomically dispersed Ni sites and their aggregation into NiOx clusters at higher loading. Among them, the single-atom-dominated Ni/H-SnO2 sensor exhibits an ultrahigh response of 13,152 toward 1 ppm NO2 at 175 °C, together with excellent selectivity, good long-term stability, and a detection limit down to 10 ppb. Experimental results combined with density functional theory calculations reveal that the superior sensing performance originates from the synergy of strong and selective NO2 adsorption and efficient charge transfer via the single-atom Ni-O-Sn bonding structure. In contrast, the formation of NiOx clusters weakens NO2 adsorption and deteriorates charge-transfer efficiency, leading to reduced sensing performance. This work identifies the single-atom Ni-O-Sn bonding as the decisive active structure for ultrasensitive NO2 detection and provides an atomic-level bonding engineering strategy for high-performance MOS-based gas sensors.
A heterojunction photocatalyst composed of single-atom, atomically dispersed Ni sites on g-C3N4/TiO2 was developed for sacrificial-agent-free CO2-to-CO reduction under simulated solar irradiation. The optimized catalyst, containing 0.78 wt% Ni and 46 wt% g-C3N4, delivered 70% selectivity to CO over H2 and exhibited 16-fold enhancement in activity compared with bare g-C3N4/TiO2 and Ni-single-atom catalysts supported on either g-C3N4 or TiO2 alone. High-resolution transmission electron microscopy (HRTEM) revealed intimate interfacial coupling within the g-C3N4/TiO2 heterojunction, while atomically dispersed Ni species were predominantly anchored on g-C3N4 nanosheets coating the TiO2 surface. X-ray photoelectron spectroscopy revealed pronounced interfacial electronic redistribution following heterojunction formation and Ni incorporation, indicating strong electronic communication between the semiconductor components. Electrochemical impedance spectroscopy (EIS) and steady-state photoluminescence measurements showed significantly suppressed charge recombination, whereas transient absorption spectroscopy revealed that isolated Ni sites act as efficient electron traps, extracting photogenerated electrons and directing them toward catalytic reduction centers. Combined with the comparative photocatalytic performance of individual components, these findings identify the Ni-Nx moieties on g-C3N4 as active sites for CO2-to-CO conversion and support the S-scheme charge-transfer pathway, in which TiO2 preferentially consumes holes while highly reducing electrons accumulate on the g-C3N4-supported Ni single-atom sites to drive selective CO2 reduction in water.
Colloidal nanocrystals are generally regarded as rigid solid entities, rarely exhibiting the structural adaptability observed in molecular cages, such as fullerenes, which can undergo carbon framework reduction and encapsulate guest cations without structural reorganization. Here, by creating two enantiomeric pairs of high-nuclearity copper sulfide nanoclusters with a mixed-valence Cu(II)/Cu(I) configuration, we endow these nanoscale assemblies with an intrinsic capacity for electron uptake under mild reducing conditions. The resulting charge imbalance provides an effective thermodynamic driving force that realizes a positively charged metal ion migrating inward through multiple atomic layers and occupying the cluster core. This system thus represents a rare example of a nanocluster platform that simultaneously combines reduction tolerance and structural robustness, preserving its atomic framework despite the incorporation of a single atom effectively modifying the electronic structure, particularly the local chirality. In situ absorption and circular dichroism spectroscopies establish that the transformation proceeds through a continuous, single-particle process rather than a fragmentation-reconstruction pathway, while ex situ pair distribution function analysis resolves key local steps in the structural evolution, offering mechanistic insights into this unique migration behavior.
The structural prediction of metal nanoclusters is hindered by the extremely complex potential energy surface and the prohibitive cost of first-principles calculations. Here, we develop an efficient structure-prediction framework that tightly integrates machine-learning interatomic potentials with global optimization. Neural network atomic potentials are iteratively trained to achieve density-functional-theory accuracy and coupled with a genetic algorithm to enable reliable exploration of complex energy landscapes. As a stringent benchmark, the framework is applied to neutral Aun clusters (n = 30-45), where it robustly identifies low-energy structures at an affordable computational cost and reveals a non-monotonic structural evolution from hollow cage-like motifs to multi-core-cage building blocks over a critical size range. Notably, this transition exhibits pronounced differences from that of the corresponding anionic clusters, highlighting the potential of the proposed active-learning workflow as an extensible strategy for investigating metal clusters with complex electronic structures.
Toxic trace metals such as lead (Pb), cadmium (Cd), and arsenic (As) represent persistent environmental contaminants associated with significant human health risks. Reliable and rapid analytical strategies are therefore essential for toxicological surveillance of consumer and environmental matrices. In this study, a green and process-intensified ultrasonic extraction method was developed for the efficient recovery and determination of Pb, Cd, and As from complex matrices including herbal materials, cosmetic products, beverages, and wastewater, using Atomic Absorption Spectrometry. The approach utilizes ultrasonic cavitation to accelerate metal desorption and mass transfer, enabling rapid extraction under mild conditions with reduced reagent consumption. Galangal was selected as a representative plant matrix to establish and validate the analytical framework. A rotatable central composite design based on response surface methodology was applied to evaluate the influence of nitric acid concentration (0.40-1.20 M), ultrasonic time (5-15 min), and acid volume (20-40 mL) on metal recovery. The statistical model demonstrated strong predictive capability and identified acid concentration and solvent volume as significant factors influencing extraction performance (p < 0.05). Ultrasonic irradiation significantly enhanced metal release from the matrix through cavitation-induced disruption and accelerated diffusion processes. The optimized extraction conditions (0.68 M HNO3, 11 min ultrasonic time, and 32 mL acid volume) yielded predicted recoveries of 97.13%, 102.09%, and 103.26% for Pb, Cd, and As, respectively, with corresponding experimental recoveries of 95.17 ± 2.37%, 97.50 ± 5.13%, and 98.63 ± 0.25%. The developed method showed a very low matrix effect. The relative expanded uncertainty of metal extraction under optimized conditions ranged from 5.60 to 10.36%, which is within the acceptable criteria (≤15%). Based on the evaluation of trueness through comparison with results obtained from homogeneous proficiency testing (PT) materials, the developed method exhibited acceptable trueness for the quantitative determination of all trace metals, with z-scores less than 2. Application of the method to real samples demonstrated consistent analytical performance across seven galangal varieties and multiple complex matrices, with recoveries ranging from 87.00 to 109.00% (% RSD = 0.08-6.82). These findings demonstrate that ultrasonic processing can provide a rapid, low-reagent, and environmentally responsible extraction strategy for toxic trace metal determination. The developed protocol offers a practical analytical platform for toxicological monitoring, contaminant surveillance, and safety assessment of natural and industrial products.
Frostbite is a serious clinical condition in which delayed treatment usually results in irreversible tissue necrosis. Due to lack of effective therapeutic interventions, we have developed minoxidil-loaded chitosan (CH)-polyethylene glycol (PEG) hydrogel (MH) to treat frostbite injury. RSM-CCD was used to optimize the independent variables in order to maximize the gel fraction, swelling index and in vitro drug permeation studies. The MH formulation was characterized by field emission scanning electron microscopy (FESEM) and atomic force microscopy (AFM). The healing activity was evaluated in L929 cells. The MH formulation was studied in experimentally induced frostbite in a rat model. Further, the efficacy was evaluated for wound closure rate, histopathological analysis, immunohistochemical expression of CD34, and levels of IL-6, IL-10, VEGF, and NO. MH showed a gel fraction (63.21 ± 3.28%), swelling index (479.79 ± 6.12%), and in vitro drug permeation (91.78 ± 10.17%). FESEM and AFM analyses validated its macroscopic properties. In L929 cell lines, MH showed significant cell proliferation, improvement of cell migration from the scratch assay and negligible toxicity. MH is also shown to be hemocompatible and non-irritant. Further, an in vivo study revealed significant improvement in the wound area with re-epithelization, angiogenesis, reduction of inflammation, and significant collagen depositions without the formation of scars. Based on these preclinical observations, it can be concluded that MH has the potential to be a promising therapeutic option for frostbite-induced tissue injury. Further studies, such as long-term stability and detailed molecular mechanisms, will be required for its clinical applicability.
Solid-binding peptides (SBPs) are versatile molecules that can control a range of atomic-scale interfacial processes, but they remain challenging to discover. Current approaches for discovery rely on directed evolution, which samples only a small fraction of possible sequences. Data-driven methods for therapeutic peptides are also not applicable as they rely on crystal structures whereas peptides adopt varied conformations at solid surfaces. To address this challenge, we recently combined biophysical modeling and machine learning to design plastic-binding peptides that were predicted to have strong adsorption enthalpies. Here, we evaluate these designs using steered molecular dynamics and single-molecule force measurements and identify de novo designed peptides that bind strongly to polyethylene. Experimental adhesion forces exceed those previously reported for SBPs, and adsorption free energies from metadynamics simulations support strong binding. Analysis of the designed peptides reveals blocks of non-polar and charged residues, which enables them to adopt conformations that segregate non-polar amino acids to the plastic surfaces (generating hydrophobic interactions) and charged amino acids away from the surfaces. The contact patterns within the non-polar blocks depend on sequence context and polyolefin type. Overall, we validate a general approach for de novo SBP discovery that has broad scientific and engineering applications.
Balancing stability, activity, and selectivity in single-atom alloy (SAA) catalysts for CO2 electroreduction remains a fundamental challenge, constrained by the vast host-dopant-surface design space and the prohibitive cost of exhaustive first-principles exploration. Here, we establish a unified computational framework for co-optimizing activity, selectivity, and stability in SAA catalysts. By integrating literature-informed design-space construction with physically constrained, data-efficient exploration, the framework rapidly identifies high-performance regions under a tightly controlled number of first-principles evaluations. Pt1/Cu(100) is identified as an optimal catalyst that simultaneously delivers high activity toward HCOOH formation, effective suppression of CO production, and intrinsic thermodynamic stability of the single-atom configuration. Mechanistic analysis reveals that dopant-host electronic coupling selectively stabilizes OCHO* intermediates while destabilizing the competing COOH* pathway, thereby governing product selectivity at the atomic scale. This work establishes a unified framework for stability-activity-selectivity co-optimization in SAA catalysts and provides transferable design principles for selective CO2 electroreduction.
The unique qualities of carbon dots (CDs) have made them appealing for applications like bioimaging, ion sensors, anticounterfeiting, and many more. The heteroatom doping strategy is one of the best techniques used to improve the optical properties of carbon dots and overcome their low quantum yield percentage. Boron-doped carbon atoms enhance the fluorescence intensity of CDs and raise the quantum yield (QY) due to the unique characteristic of boron, which has the same atomic radius as carbon and structural properties. Codoping boron with other elements has been shown in recent research to have synergistic effects that improve optical qualities compared to single-doped CDs. This paper not only highlights the synthesis, structure, and applications of boron-doped CDs but also emphasizes computational approaches to determine the properties and applications of these materials. The electronic properties and application possibilities of boron-doped carbon nanostructures were partly estimated by mathematical calculations. Density functional theory (DFT) and time-dependent DFT (TD-DFT) are two computational techniques widely used in investigating the structure-activity relationships of boron-doped CDs and predicting their electronic and optical properties. Furthermore, the role of boron attribution plays very well in improving the performance of carbon-based materials for energy storage and electrocatalysis.
Bottom-up micro/nanofabrication complements photolithography in multilayer, 3D, and cost-effective manufacturing, but lacks resist patterning technology with photoresist-level reproducibility, processability, and universality. Here, we explore a surface-selective nucleation (SSN) effect to derive polymeric resist patterns with nanoscale resolution, inherent 3D compatibility, low defect density, clean lift-off, and broad applicability across fabrication platforms. The SSN phenomenon is achieved through solution-phase polymerization of dual-ended acrylic monomers on prepatterned substrates and surface-mediated creation of area-dependent nucleation barriers using adsorption inhibitors and chain terminators, which synergistically modulate surface and bulk free energy of nucleation, respectively. The resulting resist forms a coherent resin film physically adhered to the substrate, while featuring tunable thickness (13-150 nm) and minimal surface roughness (0.91 nm). This method delivers 15 nm linewidth patterning with 99.97% coverage across wafer-scale arrays within 10 s and demonstrates high compatibility with mainstream thin-film deposition techniques (e.g., e-beam evaporation, sputtering, and atomic layer deposition).
Metastable ε-Ga2O3 holds promise for integrated neuromorphic memory and photosensing owing to its spontaneous polarization and low-temperature deposition compatibility. However, realizing its optoelectronic perception function is hindered by poor control over crystalline allotropes and complex defect-mediated carrier trapping. Here, we address these issues by achieving low-temperature (350 °C) deposition of polycrystalline ε-Ga2O3 with tailored photocarrier dynamics using reactive oxygen plasma-enhanced atomic layer deposition. This low-temperature strategy suppresses undesired phase transformation and enables defect engineering. Microstructure analyses confirm (002)-oriented polycrystalline ε-Ga2O3 with a triple domain twinning architecture that yields macroscopic pseudohexagonal symmetry and reveal an orientation relationship of ε-Ga2O3(002)//α-Al2O3(006) on c-plane sapphire. The ε-Ga2O3 deep ultraviolet photodetectors exhibit a rapid recovery time of 0.03 s and a high detectivity of 8 × 1011 Jones under a low bias of 1 V. At biases exceeding 10 V, persistent photoconductivity emerges, attributed to bias-addressed carrier trapping at oxygen vacancy defects of different energy depths. In neuromorphic mode, key synaptic behaviors─paired-pulse facilitation, excitatory postsynaptic current, and spike rate-dependent plasticity─are emulated, and high-accuracy image recognition is achieved. This work establishes a low-temperature growth strategy for ε-Ga2O3 that integrates photodetection and neuromorphic visual functionality in a single material system.
This study employed flame atomic absorption spectrophotometry to determine the levels of selected heavy metals in Swiss chard. Samples were collected from three vicinities of Addis Ababa: Akaki, Sebeta, and Kotebe. A 0.5-g dried and powdered sample was analyzed using the wet digestion method with 69%-72% HNO3 and 70% HClO4, with optimized digestion. The calibration curves and coefficient (r) value were between 0.996 and 0.999, showing very good linearity. The accuracy of the optimized procedure was tested using samples that had a known amount of the substance added. The recovery percentages ranged from 95.89% to 100%, which is a good range. The mean concentrations (mg/kg) of nickel (0.017) and zinc (0.088) in the Swiss chard were determined . The mean concentrations of metals in Swiss chard from the three areas indicated a higher concentration of zinc in Kotebe compared to Akaki and Sebeta. A higher concentration of nickel (Ni) was found in Akaki's Swiss chard compared to Sebeta and Kotebe. The Pearson correlation coefficients of metals from the Swiss chard between nickel and zinc showed a very strong correlation. The best approach combines immediate risk reduction such as cleaner irrigation and consumer warnings, with long-term remediation such as soil treatment, pollution control, and policy enforcement to protect both farmers and consumers.
Colorectal cancer is a major cause of cancer-related mortality worldwide. Nanotechnology has introduced nanoparticles as a promising therapeutic approach in cancer treatment. Gold nanoparticles (AuNPs) can influence critical cellular processes in cancer cells. This study evaluates the effects of chitosan-coated gold nanoparticles (CH-AuNPs) on cell viability, mitochondrial membrane potential, reactive oxygen species (ROS) production, and apoptosis-related gene expression in the human colorectal adenocarcinoma HT-29 cell line. CH-AuNPs were synthesized and characterized using dynamic light scattering (DLS), UV-Vis spectroscopy, and atomic force microscopy (AFM). Cell viability was assessed using the MTT assay. Apoptosis, mitochondrial membrane potential (Δψm), and ROS production were analyzed via flow cytometry using Annexin V-FITC/PI, Rhodamine 123, and DCFH-DA, respectively. A clonogenic assay was used to measure colony-forming ability, and real-time qPCR was performed for gene expression analysis. The nanoparticles had an average size of 12.3 nm. The IC50 was 33 µM (approximately 6.5 µg/mL). They significantly reduced colony formation, decreased mitochondrial membrane potential, and induced apoptosis in HT-29 cells. ROS levels remained unchanged. BAX, Caspase-3, and CYT C gene expression increased, while anti-apoptotic BCL2 expression decreased. Chitosan-coated gold nanoparticles exhibit cytotoxic and apoptotic effects on HT-29 cells under in vitro conditions. These preliminary findings suggest the need for further investigation, including validation in additional colorectal cancer cell lines, assessment of selectivity, and in vivo studies, before any therapeutic application can be considered.
Hypertrophic cardiomyopathy (HCM) variants in genes encoding the myosin heavy chain (MHC) (MYH7), myosin light chains (MYL2 and MYL3), and cardiac myosin binding protein-C (cMyBP-C, MYBPC3) lead to cardiac hypertrophy, with abnormal contractility, relaxation, and energy consumption. Here, we defined the structural consequences of pathogenic and benign missense variants in these genes by mapping 233 variants (MYH7, n = 175; MYBPC3, n = 41; MYL2, n = 12; MYL3, n = 5) onto a cryo-EM-based atomic model of the human cardiac thick filament. We identified HCM variants residing in 30 molecular interfaces of the complex thick filament interactome, including the two main interfaces of the myosin interacting-heads motif (IHM), and interfaces involving the MHC, essential and regulatory light chains, and cMyBP-C. None of the 21 variants classified as benign were within interfaces. We demonstrated earlier disease onset and adverse outcomes in HCM patients with pathogenic variants within vs. outside of molecular interfaces, emphasizing their importance in normal thick filament function and improving risk stratification of patients.
Phase transformation is a fundamental phenomenon in nature, vital for both the scientific understanding and industrial applications of materials. The emergence of two-dimensional (2D) materials introduces new physical attributes that challenge traditional phase transformation theories due to their reduced dimensionality. In monolayer transition metal dichalcogenides (TMDCs), phase transformation is typically described as a martensitic process characterized by concerted atomic displacements. Nevertheless, the large energy barrier in 2D TMDCs makes such transformations difficult to realize, posing a substantial challenge to the experimental research on the microscopic mechanism, and hindering the precise regulation of material properties. To address this, we investigate the phase transformation in monolayer MoTe2 through advanced molecular dynamics simulations accelerated by deep learning potential. Our results uncover that the phase transformation proceeds in a one-dimensional (1D), domino-like manner, exhibiting features of both martensitic and reconstructive transformations. This unique mechanism provides tunability over the process, enabling remarkably enhanced nonlinear optical responses and rapid electrical switching. This work advances current phase transformation understanding and provides perspectives for the phase engineering in other 2D materials.
This study presents a novel approach for fabricating photoelectrochemically homogeneous photocathodes composed of nickel oxide nanocrystals (NiO NCs) using supercritical hydrothermal synthesis and push-coating (PC) methods. NiO NCs were synthesized in supercritical water using oleic acid (OA) as a surface modifier. The NiO NCs exhibited high crystallinity, small particle size, and narrow size distribution. Compared with the Langmuir-Schaefer method, the PC method provided NiO-NC films with relatively low roughness and high photoelectrochemical performance. In the PC method, NiO-NC films with thicknesses of ∼100-500 nm were obtained by varying the NiO-NC and OA concentrations in the dispersions. The NiO-NC films can be regarded as photoelectrochemically homogeneous photocathodes, as their impedances are proportional to their thicknesses. This homogeneity, in turn, increases the design freedom of NiO photocathodes. In addition, atomic layer deposition allowed the filling of interparticle voids with NiO, improving the hole migration capability of the NiO-NC films. These findings demonstrate the potential of combining supercritical synthesis and PC methods as a green and economically advantageous process for the high-throughput manufacturing of NiO photocathodes, minimizing material loss and limiting the use of hazardous solvents.