Learning Signed Hyper Surfaces for Oriented Point Cloud Normal Estimation.

We propose a novel method called SHS-Net for point cloud normal estimation by learning signed hyper surfaces, which can accurately predict normals with global consistent orientation from various point clouds. Almost all existing methods estimate oriented normals through a two-stage pipeline, i.e., unoriented normal estimation and normal orientation, and each step is implemented by a separate algorithm. However, previous methods are sensitive to parameter settings, resulting in poor results from point clouds with noise, density variations and complex geometries. In this work, we introduce signed hyper surfaces (SHS), which are parameterized by multi-layer perceptron (MLP) layers, to learn to estimate oriented normals from point clouds in an end-to-end manner. The signed hyper surfaces are implicitly learned in a high-dimensional feature space where the local and global information is aggregated. Specifically, we introduce a patch encoding module and a shape encoding module to encode a 3D point cloud into a local latent code and a global latent code, respectively. Then, an attention-weighted normal prediction module is proposed as a decoder, which takes the local and global latent codes as input to predict oriented normals. Experimental results show that our algorithm outperforms the state-of-the-art methods in both unoriented and oriented normal estimation.

Facile preparation of covalent-organic framework composites for magnetic solid-phase extraction of naphthaleneacetic acid in food prior to HPLC-UV analysis.

Novel magnetic covalent organic frameworks (COFs) were prepared by one-pot synthetic strategy and employed as an efficient adsorbent for magnetic solid-phase extraction (MSPE) of naphthaleneacetic acid (NAA) in food samples. Depending on the predesigned the hydrogen bonding, π-π and hydrophobic interactions of magnetic COFs, the efficient and selective extraction process for NAA was achieved within 15 min. The magnetic COFs adsorbent combined with HPLC-UV was devoted to develop a novel quantitative method for NAA in complex food. The method afforded good coefficient in range of 0.002-10.0 µg mL-1 and low limit of detection was 0.0006 µg mL-1. And the newly established method afforded less adsorbent consumption, wider linearity and lower LODs than the reported analytical methods. Ultimately, the method was successfully applied to determine NAA in fresh pear, tomato and peach juice. The magnetic COFs based MSPE coupled with HPLC-UV method provided a simple, efficient and dependable alternative to monitor trace NAA in food samples.

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.

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.

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.

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.

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.

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.

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.

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.

A highly sensitive and long-term stable wearable patch for continuous analysis of biomarkers in sweat.

Although increasing efforts have been devoted to the development of non-invasive wearable or stretchable electrochemical sweat sensors for monitoring physiological and metabolic information, most of them still suffer from poor stability and specificity over time and fluctuating temperatures. This study reports the design and fabrication of a long-term stable and highly sensitive flexible electrochemical sensor based on nanocomposite-modified porous graphene by simple and facile laser treatment for detecting biomarkers such as glucose in sweat. The laser-reduced and patterned stable conductive nanocomposite on the porous graphene electrode provides the resulting glucose sensor with an excellent sensitivity of 1317.69 µAmM-1cm-2 with an ultra-low limit of detection (LOD) of 0.079 µM. The sensor can also detect pH and exhibit extraordinary stability to maintain more than 91% sensitivity over 21 days in ambient conditions. Taken together with a temperature sensor based on the same material system, the dual glucose and pH sensor integrated with a flexible microfluidic sweat sampling network further results in accurate continuous on-body glucose detection calibrated by the simultaneously measured pH and temperature. The low-cost, highly sensitive, and long-term stable platform could facilitate and pave the way for the early identification and continuous monitoring of different biomarkers for non-invasive disease diagnosis and treatment evaluation.