Visible-ultraviolet dual-band photodetectors based on an all-inorganic CsPbCl3/p-GaN heterostructure.

All-inorganic metal halide perovskites (MHPs) have attracted increasing attention because of their high thermal stability and band gap tunability. Among them, CsPbCl3 is considered a promising semiconductor material for visible-ultraviolet dual-band photodetectors because of its excellent photoelectric properties and suitable band gap value. In this work, we fabricated a visible-ultraviolet dual-band photodetector based on a CsPbCl3/p-GaN heterojunction using the spin coating method. The formation of the heterojunction enables the device to exhibit obvious dual-band response behavior at positive and negative bias voltages. At the same time, the dark current of the device can be as low as 2.42 × 10-9 A, and the corresponding detection rate can reach 5.82 × 1010 Jones. In addition, through simulation calculations, it was found that the heterojunction has a type II energy band arrangement, and the heterojunction response band light absorption is significantly enhanced. The type II energy band arrangement will separate electron-hole pairs more effectively, which will help improve device performance. The successful implementation of visible-ultraviolet dual-band photodetectors based on a CsPbCl3/p-GaN heterojunction provides guidance for the application of all-inorganic MHPs in the field of multi-band photodetectors.

Fast Learning of Signed Distance Functions From Noisy Point Clouds Via Noise to Noise Mapping.

Learning signed distance functions (SDFs) from point clouds is an important task in 3D computer vision. However, without ground truth signed distances, point normals or clean point clouds, current methods still struggle from learning SDFs from noisy point clouds. To overcome this challenge, we propose to learn SDFs via a noise to noise mapping, which does not require any clean point cloud or ground truth supervision. Our novelty lies in the noise to noise mapping which can infer a highly accurate SDF of a single object or scene from its multiple or even single noisy observations. We achieve this by a novel loss which enables statistical reasoning on point clouds and maintains geometric consistency although point clouds are irregular, unordered and have no point correspondence among noisy observations. To accelerate training, we use multi-resolution hash encodings implemented in CUDA in our framework, which reduces our training time by a factor of ten, achieving convergence within one minute. We further introduce a novel schema to improve multi-view reconstruction by estimating SDFs as a prior. Our evaluations under widely-used benchmarks demonstrate our superiority over the state-of-the-art methods in surface reconstruction from point clouds or multi-view images, point cloud denoising and upsampling.

Propagation constant-based diameter measurement technique for a submicrometer-scale optical fiber.

Diameter is a critical parameter for determining the physical properties of a submicrometer optical fiber and requires an accurate measurement. In this study, we proposed, to our knowledge, a novel diameter measurement technique derived from the waveguide theory, utilizing the pitch of a standing-wave near-field light generated by two counter-propagating lights within the submicrometer optical fiber. In a submicrometer optical fiber, the propagating light extends into the surrounding air as near-field light, existing within a range approximately equivalent to one wavelength from the surface of the fiber. By generating the standing-wave near-field light with the incident lights from both ends of the fiber, the pitch of the standing-wave near-field light can be measured by scanning along the fiber's central axis with a scanning near-field optical microscopy probe. The fiber diameter is subsequently acquired by solving the optical fiber eigenvalue equation. Based on the feasibility verification experiment, a high-precision measurement of approximately 0.50 µm was realized for the diameter of the optical fiber.

Advanced Optical Information Encryption Enabled by Polychromatic and Stimuli-Responsive Luminescence of Sb-Doped Double Perovskites.

The smart materials with multi-color and stimuli-responsive luminescence are very promising for next generation of optical information encryption and anti-counterfeiting, but these materials are still scarce. Herein, a multi-level information encryption strategy is developed based on the polychromatic emission of Sb-doped double perovskite powders (SDPPs). Cs2NaInCl6:Sb, Cs2KInCl6:Sb, and Cs2AgInCl6:Sb synthesized through coprecipitation methods exhibit broadband emissions with bright blue, cyan, and orange colors, respectively. The information transmitted by specific SDPP is encrypted when different SDPPs are mixed. The confidential information can be decrypted by selecting the corresponding narrowband filter. Then, an encrypted quick response (QR) code with improved security is demonstrated based on this multi-channel selection strategy. Moreover, the three types of SDPPs exhibit three different water-triggered luminescence switching behaviors. The confidential information represented by Cs2NaInCl6:Sb can be erased/recovered through a simple water spray/drying. Whereas, the information collected from the green channel is permanently erased by moisture, which fundamentally avoids information leakage. Therefore, different encryption schemes can be designed to meet a variety of encryption requirements. The multicolor and stimuli-responsive luminescence greatly enrich the flexibility of optical information encryption, which leaps the level of security and confidentiality.

CAP-UDF: Learning Unsigned Distance Functions Progressively from Raw Point Clouds with Consistency-Aware Field Optimization.

Surface reconstruction for point clouds is an important task in 3D computer vision. Most of the latest methods resolve this problem by learning signed distance functions from point clouds, which are limited to reconstructing closed surfaces. Some other methods tried to represent open surfaces using unsigned distance functions (UDF) which are learned from ground truth distances. However, the learned UDF is hard to provide smooth distance fields due to the discontinuous character of point clouds. In this paper, we propose CAP-UDF, a novel method to learn consistency-aware UDF from raw point clouds. We achieve this by learning to move queries onto the surface with a field consistency constraint, where we also enable to progressively estimate a more accurate surface. Specifically, we train a neural network to gradually infer the relationship between queries and the approximated surface by searching for the moving target of queries in a dynamic way. Meanwhile, we introduce a polygonization algorithm to extract surfaces using the gradients of the learned UDF. We conduct comprehensive experiments in surface reconstruction for point clouds, real scans or depth maps, and further explore our performance in unsupervised point normal estimation, which demonstrate non-trivial improvements of CAP-UDF over the state-of-the-art methods.

Exquisite visualization of mitophagy and monitoring the increase of lysosomal micro-viscosity in mitophagy with an unusual pH-independent lysosomal rotor.

BACKGROUND: Mitophagy plays indispensable roles in maintaining intracellular homeostasis in most eukaryotic cells by selectively eliminating superfluous components or damaged organelles. Thus, the co-operation of mitochondrial probes and lysosomal probes was presented to directly monitor mitophagy in dual colors. Nowadays, most of the lysosomal probes are composed of groups sensitive to pH, such as morpholine, amine and other weak bases. However, the pH in lysosomes would fluctuate in the process of mitophagy, leading to the optical interference. Thus, it is crucial to develop a pH-insensitive probe to overcome this tough problem to achieve exquisite visualization of mitophagy. RESULTS: In this study, we rationally prepared a pH-independent lysosome probe to reduce the optical interference in mitophagy, and thus the process of mitophagy could be directly monitored in dual color through cooperation between IVDI and MTR, depending on Förster resonance energy transfer mechanism. IVDI shows remarkable fluorescence enhancement toward the increase of viscosity, and the fluorescence barely changes when pH varies. Due to the sensitivity to viscosity, the probe can visualize micro-viscosity alterations in lysosomes without washing procedures, and it showed better imaging properties than LTR. Thanks to the inertia of IVDI to pH, IVDI can exquisitely monitor mitophagy with MTR by FRET mechanism despite the changes of lysosomal pH in mitophagy, and the reduced fluorescence intensity ratio of green and red channels can indicate the occurrence of mitophagy. Based on the properties mentioned above, the real-time increase of micro-viscosity in lysosomes during mitophagy was exquisitely monitored through employing IVDI. SIGNIFICANCE AND NOVELTY: Compared with the lysosomal fluorescent probes sensitive to pH, the pH-inert probe could reduce the influence of pH variation during mitophagy to achieve exquisite visualization of mitophagy in real-time. Besides, the probe could monitor the increase of lysosomal micro-viscosity in mitophagy. So, the probe possesses tremendous potential in the visualization of dynamic changes related to lysosomes in various physiological processes.

A Sulfur-Doped Copper Catalyst with Efficient Electrocatalytic Formate Generation during the Electrochemical Carbon Dioxide Reduction Reaction.

Catalysts involving post-transition metals have shown almost invincible performance on generating formate in electrochemical CO2 reduction reaction (CO2 RR). Conversely, Cu without post-transition metals has struggled to achieve comparable activity. In this study, a sulfur (S)-doped-copper (Cu)-based catalyst is developed, exhibiting excellent performance in formate generation with a maximum Faradaic efficiency of 92 % and a partial current density of 321 mA cm-2 . Ex situ structural elucidations reveal the presence of abundant grain boundaries and high retention of S-S bonds from the covellite phase during CO2 RR. Furthermore, thermodynamic calculations demonstrate that S-S bonds can moderate the binding energies with various intermediates, further improving the activity of the formate pathway. This work is significant in modifying a low-cost catalyst (Cu) with a non-metallic element (S) to achieve comparable performance to mainstream catalysts for formate generation in industrial grade.

A multichromatic colorimetric detection method for Vibrio parahaemolyticus based on Fe3O4-Zn-Mn nanoenzyme and dual substrates.

IMPORTANCE: The Fe3O4-Zn-Mn nanomimetic enzyme demonstrates significant importance in dual-substrate colorimetric detection for V. parahaemolyticus, owing to its enhanced sensitivity, selectivity, and rapid detection capabilities. Additionally, it offers cost-effectiveness, portability, and the potential for multiplex detection. This innovative approach holds promise for improving the monitoring and control of V. parahaemolyticus infections, thereby contributing to advancements in public health and food safety.

Hypergraph-Based Multi-Modal Representation for Open-Set 3D Object Retrieval.

The traditional 3D object retrieval (3DOR) task is under the close-set setting, which assumes the categories of objects in the retrieval stage are all seen in the training stage. Existing methods under this setting may tend to only lazily discriminate their categories, while not learning a generalized 3D object embedding. Under such circumstances, it is still a challenging and open problem in real-world applications due to the existence of various unseen categories. In this paper, we first introduce the open-set 3DOR task to expand the applications of the traditional 3DOR task. Then, we propose the Hypergraph-Based Multi-Modal Representation (HGM 2 R) framework to learn 3D object embeddings from multi-modal representations under the open-set setting. The proposed framework is composed of two modules, i.e., the Multi-Modal 3D Object Embedding (MM3DOE) module and the Structure-Aware and Invariant Knowledge Learning (SAIKL) module. By utilizing the collaborative information of modalities derived from the same 3D object, the MM3DOE module is able to overcome the distinction across different modality representations and generate unified 3D object embeddings. Then, the SAIKL module utilizes the constructed hypergraph structure to model the high-order correlation among 3D objects from both seen and unseen categories. The SAIKL module also includes a memory bank that stores typical representations of 3D objects. By aligning with those memory anchors in the memory bank, the aligned embeddings can integrate the invariant knowledge to exhibit a powerful generalized capacity toward unseen categories. We formally prove that hypergraph modeling has better representative capability on data correlation than graph modeling. We generate four multi-modal datasets for the open-set 3DOR task, i.e., OS-ESB-core, OS-NTU-core, OS-MN40-core, and OS-ABO-core, in which each 3D object contains three modality representations: multi-view, point clouds, and voxel. Experiments on these four datasets show that the proposed method can significantly outperform existing methods. In particular, the proposed method outperforms the state-of-the-art by 12.12%/12.88% in terms of mAP on the OS-MN40-core/OS-ABO-core dataset, respectively. Results and visualizations demonstrate that the proposed method can effectively extract the generalized 3D object embeddings on the open-set 3DOR task and achieve satisfactory performance.

Magnetic covalent organic frameworks for extraction and determination of endocrine-disrupting chemicals in beverage and water samples.

BACKGROUND: Phenolic endocrine-disrupting chemicals (EDCs) are widespread and easily ingested through the food chain. They pose a serious threat to human health. Magnetic solid-phase extraction (MSPE) is an effective sample pre-treatment technology to determine traces of phenolic EDCs. RESULTS: Magnetic covalent organic framework (COF) (Fe3 O4 @COF) nanospheres were prepared and characterized. The efficient and selective extraction of phenolic EDCs relies on a large specific surface and the inherent porosity of COFs and hydrogen bonding, π-π, and hydrophobic interactions between COF shells and phenolic EDCs. Under optimal conditions, the proposed magnetic solid-phase extraction-high-performance liquid chromatography-ultra violet (MSPE-HPLC-UV) based on the metallic covalent organic framework method for phenolic EDCs shows good linearities (0.002-6 µg mL-1 ), with R2 of 0.995 or higher, and low limits of detection (6-1.200 ng mL-1 ). CONCLUSION: Magnetic covalent organic frameworks (Fe3 O4 @COFs) with good MSPE performance for phenolic EDCs were synthesized by the solvothermal method. The magnetic covalent organic framework-based MSPE-HPLC-UV method was applied successfully to determine phenolic EDCs in beverage and water samples with satisfactory recoveries (90.200%-123%) and relative standard deviations (2.100%-12.100%). © 2023 Society of Chemical Industry.

Asymmetric Cu-N-La Species Enabling Atomic-Level Donor-Acceptor Structure and Favored Reaction Thermodynamics for Selective CO2 Photoreduction to CH4.

Photocatalytic CO2 reduction into ideal hydrocarbon fuels, such as CH4 , is a sluggish kinetic process involving adsorption of multiple intermediates and multi-electron steps. Achieving high CH4 activity and selectivity therefore remains a great challenge, which largely depends on the efficiency of photogenerated charge separation and transfer as well as the intermediate energy levels in CO2 reduction. Herein, we construct La and Cu dual-atom anchored carbon nitride (LaCu/CN), with La-N4 and Cu-N3 coordination bonds connected by Cu-N-La bridges. The asymmetric Cu-N-La species enables the establishment of an atomic-level donor-acceptor structure, which allows the migration of electrons from La atoms to the reactive Cu atom sites. Simultaneously, intermediates during CO2 reduction on LaCu/CN demonstrate thermodynamically more favorable process for CH4 formation based on theoretical calculations. Eventually, LaCu/CN exhibits a high selectivity (91.6 %) for CH4 formation with a yield of 125.8 µmol g-1 , over ten times of that for pristine CN. This work presents a strategy for designing multi-functional dual-atom based photocatalysts.

Broadband Photodetector for Ultraviolet to Visible Wavelengths Based on the BA2PbI4/GaN Heterostructure.

Two-dimensional (2D) organic-inorganic hybrid perovskites (OIPs) have exhibited ideal prospects for perovskite photodetectors (PDs) owing to their remarkable environmental stability, tunable band gap, and structural diversity. However, most perovskites face the great challenge of a narrow spectral response. Integrating 2D OIPs with a suitable wide band gap semiconductor gives opportunities to broaden the response spectra. Here, a photodetector based on the BA2PbI4/GaN heterostructure with a broadband photoresponse covering from the ultraviolet (UV) to visible band is designed. We demonstrate that the device is capable of detecting in the UV region by p-GaN being integrated with BA2PbI4. The morphology and material optical properties of BA2PbI4 are characterized by transmission electron microscopy (TEM) and photoluminescence (PL). Additionally, the current-voltage (I-V) characteristics and photoresponses of the BA2PbI4/GaN heterojunction photodetector are investigated. The response spectrum of the photodetector is broadened from the visible to UV region, exhibiting good rectifying behavior in the dark conditions and a broadband photoresponse from the UV to the visible region. Additionally, the energy band is used to analyze the current mechanism of the BA2PbI4/GaN heterojunction PD. This study is expected to provide a new insight of optoelectronic devices by integrating 2D OIPs such as BA2PbI4 and wide-band-gap semiconductors such as GaN to broaden the response spectra.

Metal-semiconductor-metal solar-blind ultraviolet photodetector based on Al0.55Ga0.45N/Al0.4Ga0.6N/Al0.65Ga0.35N heterostructures.

We have designed a metal-semiconductor-metal (MSM) solar-blind ultraviolet (UV) photodetector (PD) by utilizing Al0.55Ga0.45N/Al0.4Ga0.6N/Al0.65Ga0.35N heterostructures. The interdigital Ni/Au metal stack is deposited on the Al0.55Ga0.45N layer to form Schottky contacts. The AlGaN hetero-epilayers with varying Al content contribute to the formation of a two-dimensional electron gas (2DEG) conduction channel and the enhancement of the built-in electric field in the Al0.4Ga0.6N absorption layer. This strong electric field facilitates the efficient separation of photogenerated electron-hole pairs. Consequently, the fabricated PD exhibits an ultra-low dark current of 1.6 × 10-11 A and a broad spectral response ranging from 220 to 280 nm, with a peak responsivity of 14.08 A/W at -20 V. Besides, the PD demonstrates an ultrahigh detectivity of 2.28 × 1013 Jones at -5 V. Furthermore, to investigate the underlying physical mechanism of the designed solar-blind UV PD, we have conducted comprehensive two-dimensional device simulations.

A droplet friction/solar-thermal hybrid power generation device for energy harvesting in both rainy and sunny weathers.

Photovoltaic device is highly dependent on the weather, which is completely ineffective on rainy days. Therefore, it is very significant to design an all-weather power generation system that can utilize a variety of natural energy. This work develops a water droplet friction power generation (WDFG)/solar-thermal power generation (STG) hybrid system. The WDFG consists of two metal electrodes and a candle soot/polymer composite film, which also can be regarded as a capacitor. Thus, the capacitor coupled power generation (C-WDFG) device can achieve a sustainable and stable direct-current (DC) output under continuous dripping without external conversion circuits. A single device can produce an open-circuit voltage of ca.0.52 V and a short-circuit current of ca.0.06 mA, which can be further scaled up through series or parallel connection to drive commercial electronics. Moreover, we demonstrate that the C-WDFG is highly compatible with the thermoelectric device. The excellent photothermal performance of soot/polymer composite film can efficiently convert solar into heat, which is then converted to electricity by the thermoelectric device. Therefore, this C-WDFG/STG hybrid system can work in both rainy and sunny days.

Rapid and sensitive determination of ascorbic acid based on label-free silver triangular nanoplates.

In this study, a new method for the detection of ascorbic acid (AA) was proposed. It was based on the protective effect of AA on silver triangular nanoplates (Ag TNPs) against Cl- induced etching reactions. Cl- can attack the corners of Ag TNPs and etch them, causing a morphological shift from triangular nanoplates to nanodiscs. As a result, the solution changes color from blue to yellow. However, in the presence of AA, the corners of Ag TNPs can be protected from Cl- etching, and the blue color of the solution remains unchanged. Using this effect, a selective sensor was designed to detect AA in the range of 0-40.00 µM with a detection limit of 2.17 µM. As the concentration of AA varies in this range, color changes from yellow to blue can be easily observed, so the designed sensor can be used for colorimetric detection. This method can be used to analyze fruit juice samples.

Co-multiplexing spectral and temporal dimensions based on luminescent materials.

Optical multiplexing is a pivotal technique for augmenting the capacity of optical data storage (ODS) and increasing the security of anti-counterfeiting. However, due to the dearth of appropriate storage media, optical multiplexing is generally restricted to a single dimension, thus curtailing the encoding capacity. Herein, the co-multiplexing spectral and temporal dimensions are proposed for optical encoding based on photoluminescence (PL) and persistent-luminescence (PersL) at four different wavelengths. Each emission color comprises four luminescence modes. The further multiplexing of four wavelengths leads to the maximum encoding capacity of 8 bits at each pixel. The wavelength difference between adjacent peaks is larger than 50 nm. The well-separated emission wavelengths significantly lower the requirements for high-resolution spectrometers. Moreover, the information is unable to be decoded until both PL and PersL spectra are collected, suggesting a substantial improvement in information security and the security level of anti-counterfeiting.

Significant effects of mixed cations on the morphology and photochemical activities of alkali-metal-antimony (Na,K)Sb3O7.

Oxide semiconductors with mixed-valence states generally exhibit excellent optoelectronic and photochemical properties due to facile charge transfer in redox reactions. In this work, we investigate the effects of mixed alkali on the optical absorption, luminescence spectra and photocatalytic abilities of (Na1-xKx)Sb3O7 nanoparticles. All the samples are fabricated using a simple one-step hydrothermal method. The structural studies show that the largest substitution of K+ ions in (Na1-xKx)Sb3O7 is at x = 0.3. In hydrothermal synthesis, the mixed arrangement of K+ and Na+ in (Na1-xKx)Sb3O7 has an influence on the crystal shape of particles. NaSb3O7 develops into a regular cube shape. With the increase of K+ ions in (Na1-xKx)Sb3O7, the edges and corners of the cube are further ground off, resulting in irregularly spherical particles. This mixed-alkali antimonite belongs to a p-type indirect allowed transition semiconductor, and the optical band gap is 2.71 eV (x = 0.3). The intrinsic luminescence of NaSb3O7 is detected at 540 nm, which is nearly quenched in Na0.7K0.3Sb3O7. It is demonstrated that the substitution of K+ in NaSb3O7 significantly increases the photodegradation of RhB solutions. There are two types of Sb cations, i.e., Sb5+ and Sb3+ mixed in the structure. The improved photocatalysis is attributed to the charge mediators between Sb5+/Sb3+ couples. The experiment shows that co-doping cations in antimonite oxides may be one of the strategies to improve photochemical properties.

Intrinsic- and Rare Earth Ion-Activated Luminescence in NbAlO4 with Wide Structure Channels.

Compounds with ordered and interconnected channels have versatile multifunctional applications in technological fields. In this work, we report the intrinsic- and Eu3+-activated luminescence in NbAlO4 with a wide channel structure. NbAlO4 is an n-type semiconductor with an indirect allowed transition and a band-gap energy of 3.26 eV. The conduction band and valence band are composed of Nb 3d and O 2p states, respectively. Unlike the common niobate oxide Nb2O5, NbAlO4 exhibits efficient self-activated luminescence with good thermal stability even at room temperature. The AlO4 tetrahedron effectively blocks the transfer/dispersion of excitation energy between NbO6 chains in NbAlO4, allowing for effective self-activated luminescence from NbO6 activation centers. Moreover, Eu3+-doped NbAlO4 displayed a bright red luminescence of 5D0 → 7F2 transition at 610 nm. The site-selective excitation and luminescence of Eu3+ ions in a spectroscopic probe were utilized to investigate the doping mechanism. It is evidenced that Eu3+ is doped in the structure channel in NbAlO4 lattices, not in the normal cation sites of Nb5+ or Al3+. The experimental findings are valuable in developing new luminescent materials and improving the understanding of the material's channel structure.

Sustainable and High-Rate Electrosynthesis of Nitrogen Fertilizer.

The conventional industrial production of nitrogen-containing fertilizers, such as urea and ammonia, relies heavily on energy-intensive processes, accounting for approximately 3 % of global annual CO2 emissions. Herein, we report a sustainable electrocatalytic approach that realizes direct and selective synthesis of urea and ammonia from co-reduction of CO2 and nitrates under ambient conditions. With the assistance of a copper (Cu)-based salphen organic catalyst, outstanding urea (3.64 mg h-1 mgcat -1 ) and ammonia (9.73 mg h-1 mgcat -1 ) yield rates are achieved, in addition to a remarkable Faradaic efficiency of 57.9±3 % for the former. This work proposes an appealing sustainable route to converting greenhouse gas and waste nitrates by renewable energies into value-added fertilizers.

Fast Learning Radiance Fields by Shooting Much Fewer Rays.

Learning radiance fields has shown remarkable results for novel view synthesis. The learning procedure usually costs lots of time, which motivates the latest methods to speed up the learning procedure by learning without neural networks or using more efficient data structures. However, these specially designed approaches do not work for most of radiance fields based methods. To resolve this issue, we introduce a general strategy to speed up the learning procedure for almost all radiance fields based methods. Our key idea is to reduce the redundancy by shooting much fewer rays in the multi-view volume rendering procedure which is the base for almost all radiance fields based methods. We find that shooting rays at pixels with dramatic color change not only significantly reduces the training burden but also barely affects the accuracy of the learned radiance fields. In addition, we also adaptively subdivide each view into a quadtree according to the average rendering error in each node in the tree, which makes us dynamically shoot more rays in more complex regions with larger rendering error. We evaluate our method with different radiance fields based methods under the widely used benchmarks. Experimental results show that our method achieves comparable accuracy to the state-of-the-art with much faster training.