Elaboration and characterization of nanoplate structured alpha-Fe2O3 films by Ag3PO4

A new strategy for surface treatment of hematite nanoplates for efficient photoelectrochemical (PEC) performances is proposed. Silver orthophosphate (Ag₃PO₄) has been adopted to mediate the formation of α-Fe₂O₃ films. Phosphate ions in Ag₃PO₄ is found to cause a significant morphology change during annealing process, from β-FeOOH nanorod arrays to hematite nanoplates. Meanwhile, Ag ions is doped into α-Fe₂O₃ film. The obtained nanoplate structured Fe₂O₃ –Ag–P films demonstrate much higher photoelectrochemical performance as photoanodes than the bare Fe₂O₃ nanorod thin films. The effects of phosphate and silver ions on the morphology, surface characteristics and the PEC properties of the photoanodes are investigated

NOPE-SAC: Neural One-Plane RANSAC for Sparse-View Planar 3D Reconstruction

This paper studies the challenging two-view 3D reconstruction in a rigorous sparse-view configuration, which is suffering from insufficient correspondences in the input image pairs for camera pose estimation. We present a novel Neural One-PlanE RANSAC framework (termed NOPE-SAC in short) that exerts excellent capability to learn one-plane pose hypotheses from 3D plane correspondences. Building on the top of a siamese plane detection network, our NOPE-SAC first generates putative plane correspondences with a coarse initial pose. It then feeds the learned 3D plane parameters of correspondences into shared MLPs to estimate the one-plane camera pose hypotheses, which are subsequently reweighed in a RANSAC manner to obtain the final camera pose. Because the neural one-plane pose minimizes the number of plane correspondences for adaptive pose hypotheses generation, it enables stable pose voting and reliable pose refinement in a few plane correspondences for the sparse-view inputs. In the experiments, we demonstrate that our NOPE-SAC significantly improves the camera pose estimation for the two-view inputs with severe viewpoint changes, setting several new state-of-the-art performances on two challenging benchmarks, i.e., MatterPort3D and ScanNet, for sparse-view 3D reconstruction. The source code is released at https://github.com/IceTTTb/NopeSAC for reproducible research.Comment: Accepted to IEEE TPAMI; Code is available at https://github.com/IceTTTb/NopeSA

Level-S2^2fM: Structure from Motion on Neural Level Set of Implicit Surfaces

This paper presents a neural incremental Structure-from-Motion (SfM) approach, Level-S$^2$fM. In our formulation, we aim at simultaneously learning coordinate MLPs for the implicit surfaces and the radiance fields, and estimating the camera poses and scene geometry, which is mainly sourced from the established keypoint correspondences by SIFT. Our formulation would face some new challenges due to inevitable two-view and few-view configurations at the beginning of incremental SfM pipeline for the optimization of coordinate MLPs, but we found that the strong inductive biases conveying in the 2D correspondences are feasible and promising to avoid those challenges by exploiting the relationship between the ray sampling schemes used in volumetric rendering and the sphere tracing of finding the zero-level set of implicit surfaces. Based on this, we revisit the pipeline of incremental SfM and renew the key components of two-view geometry initialization, the camera pose registration, and the 3D points triangulation, as well as the Bundle Adjustment in a novel perspective of neural implicit surfaces. Because the coordinate MLPs unified the scene geometry in small MLP networks, our Level-S$^2$fM treats the zero-level set of the implicit surface as an informative top-down regularization to manage the reconstructed 3D points, reject the outlier of correspondences by querying SDF, adjust the estimated geometries by NBA (Neural BA), finally yielding promising results of 3D reconstruction. Furthermore, our Level-S$^2$fM alleviated the requirement of camera poses for neural 3D reconstruction.Comment: under revie

Crystal structure of ethanol-bis(N-((5-(ethoxycarbonyl)-3,4-dimethyl-1H-pyrrol-2-yl)methylene)benzohydrazonato-κ2N,O)copper(II), C36H42N6O7Cu

Abstract C20H29N3O5, triclinic, P1̄ (no. 2), a = 8.482(6) Å, b = 15.272(12) Å, c = 15.285(12) Å, a = 112.024(11)°, β = 95.325(9)°, γ = 98.958(9)°, V = 1788(2) Å3, Z = 2, R gt(F) = 0.0570, wR ref(F 2) = 0.1687, T = 296(2) K

Graph neural network based on brain inspired forward-forward mechanism for motor imagery classification in brain-computer interfaces

IntroductionWithin the development of brain-computer interface (BCI) systems, it is crucial to consider the impact of brain network dynamics and neural signal transmission mechanisms on electroencephalogram-based motor imagery (MI-EEG) tasks. However, conventional deep learning (DL) methods cannot reflect the topological relationship among electrodes, thereby hindering the effective decoding of brain activity.MethodsInspired by the concept of brain neuronal forward-forward (F-F) mechanism, a novel DL framework based on Graph Neural Network combined forward-forward mechanism (F-FGCN) is presented. F-FGCN framework aims to enhance EEG signal decoding performance by applying functional topological relationships and signal propagation mechanism. The fusion process involves converting the multi-channel EEG into a sequence of signals and constructing a network grounded on the Pearson correlation coeffcient, effectively representing the associations between channels. Our model initially pre-trains the Graph Convolutional Network (GCN), and fine-tunes the output layer to obtain the feature vector. Moreover, the F-F model is used for advanced feature extraction and classification.Results and discussionAchievement of F-FGCN is assessed on the PhysioNet dataset for a four-class categorization, compared with various classical and state-of-the-art models. The learned features of the F-FGCN substantially amplify the performance of downstream classifiers, achieving the highest accuracy of 96.11% and 82.37% at the subject and group levels, respectively. Experimental results affirm the potency of FFGCN in enhancing EEG decoding performance, thus paving the way for BCI applications

Bis(μ-di-2-pyridyl disulfide-κ3 N,S:N′)di-μ3-iodido-di-μ2-iodido-tetra­copper(I)

In the centrosymmetric tetra­nuclear title compound, [Cu4I4(C10H8N2S2)2], there are two different CuI atoms with tetra­hedral coordination geometries. One is chelated by a pyridine N atom and an S-donor from one di-2-pyridyl disulfide ligand and coordinated by two I atoms, while the second CuI atom is coordinated by a pyridine-N and three I atoms. Iodine bridges between the CuI atoms form a tetra­nuclear structure