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.

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.

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.

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.

PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-Step Point Moving Paths.

Point cloud completion concerns to predict missing part for incomplete 3D shapes. A common strategy is to generate complete shape according to incomplete input. However, unordered nature of point clouds will degrade generation of high-quality 3D shapes, as detailed topology and structure of unordered points are hard to be captured during the generative process using an extracted latent code. We address this problem by formulating completion as point cloud deformation process. Specifically, we design a novel neural network, named PMP-Net++, to mimic behavior of an earth mover. It moves each point of incomplete input to obtain a complete point cloud, where total distance of point moving paths (PMPs) should be the shortest. Therefore, PMP-Net++ predicts unique PMP for each point according to constraint of point moving distances. The network learns a strict and unique correspondence on point-level, and thus improves quality of predicted complete shape. Moreover, since moving points heavily relies on per-point features learned by network, we further introduce a transformer-enhanced representation learning network, which significantly improves completion performance of PMP-Net++. We conduct comprehensive experiments in shape completion, and further explore application on point cloud up-sampling, which demonstrate non-trivial improvement of PMP-Net++ over state-of-the-art point cloud completion/up-sampling methods.

Snowflake Point Deconvolution for Point Cloud Completion and Generation With Skip-Transformer.

Most existing point cloud completion methods suffer from the discrete nature of point clouds and the unstructured prediction of points in local regions, which makes it difficult to reveal fine local geometric details. To resolve this issue, we propose SnowflakeNet with snowflake point deconvolution (SPD) to generate complete point clouds. SPD models the generation of point clouds as the snowflake-like growth of points, where child points are generated progressively by splitting their parent points after each SPD. Our insight into the detailed geometry is to introduce a skip-transformer in the SPD to learn the point splitting patterns that can best fit the local regions. The skip-transformer leverages attention mechanism to summarize the splitting patterns used in the previous SPD layer to produce the splitting in the current layer. The locally compact and structured point clouds generated by SPD precisely reveal the structural characteristics of the 3D shape in local patches, which enables us to predict highly detailed geometries. Moreover, since SPD is a general operation that is not limited to completion, we explore its applications in other generative tasks, including point cloud auto-encoding, generation, single image reconstruction, and upsampling. Our experimental results outperform state-of-the-art methods under widely used benchmarks.

Core-shell-structured magnetic covalent organic frameworks for effective extraction of parabens prior to their determination by HPLC.

Covalent organic framework (COF)-decorated magnetic nanoparticles (Fe3O4@DhaTab) with core-shell structure have been synthesized by one-pot method. The prepared Fe3O4@DhaTab was well characterized, and parameters of magnetic solid-phase extraction (MSPE) for parabens were also investigated in detail. Under optimized conditions, the adsorbent dosage was only 3 mg and extraction time was 10 min. The developed Fe3O4@DhaTab-based MSPE-HPLC analysis method offered good linearity (0.01-20 µg mL-1) with R2 (0.999) and low limits of detection (3.3-6.5 µg L-1) using UV detector at 254 nm. The proposed method was applied to determine four parabens in environmental water samples with recoveries in the range 64.0-105% and relative standard deviations of 0.16-7.8%. The adsorption mechanism was explored and indicated that porous DhaTab shell provided π-π, hydrophobic, and hydrogen bonding interactions in the MSPE process. The results revealed the potential of magnetic-functionalized COFs in determination of environmental contaminants.

SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction With Self-Projection Optimization.

The task of point cloud upsampling aims to acquire dense and uniform point sets from sparse and irregular point sets. Although significant progress has been made with deep learning models, state-of-the-art methods require ground-truth dense point sets as the supervision, which makes them limited to be trained under synthetic paired training data and not suitable to be under real-scanned sparse data. However, it is expensive and tedious to obtain large numbers of paired sparse-dense point sets as supervision from real-scanned sparse data. To address this problem, we propose a self-supervised point cloud upsampling network, named SPU-Net, to capture the inherent upsampling patterns of points lying on the underlying object surface. Specifically, we propose a coarse-to-fine reconstruction framework, which contains two main components: point feature extraction and point feature expansion, respectively. In the point feature extraction, we integrate the self-attention module with the graph convolution network (GCN) to capture context information inside and among local regions simultaneously. In the point feature expansion, we introduce a hierarchically learnable folding strategy to generate upsampled point sets with learnable 2D grids. Moreover, to further optimize the noisy points in the generated point sets, we propose a novel self-projection optimization associated with uniform and reconstruction terms as a joint loss to facilitate the self-supervised point cloud upsampling. We conduct various experiments on both synthetic and real-scanned datasets, and the results demonstrate that we achieve comparable performances to state-of-the-art supervised methods.

Supramolecular polymer/peptide hybrid hydrogels with tunable stiffness mediated by interchain acid-amide hydrogen bonds.

Hydrogels are a class of biomaterials used in the field of tissue engineering and drug delivery. Many tissue engineering applications depend on the material properties of hydrogel scaffolds, such as mechanical stiffness, pore size, and interconnectivity. In this work, we describe the synthesis of peptide/polymer hybrid double-network (DN) hydrogels composed of supramolecular and covalent polymers. The DN hydrogels were prepared by combining the self-assembled pentafluorobenzyl diphenylalanyl aspartic acid (PFB-FFD) tripeptide for the first network and the polymeric PNIPAM-PEGDA copolymer for the second network. During this process, self-assembled peptide nanostructures are cross-linked to the polyacrylamide group in the polymer network through non-covalent interactions. The PNIPAM-PEGDA:PFB-FFD hydrogel exhibited higher mechanical stiffness (G' ∼2 kPa) than the PNIPAM-PEGDA copolymer. Moreover, PNIPAM-PEGDA:PFB-FFD hydrogel shows a decrease in pore size (∼1.2 µm) compared to the original copolymer (∼5.2 µm), with the structural framework of highly interconnected fibrous peptide network. The mechanical stiffness of hydrogels was systematically investigated by rheological analysis in response to various variables, including UV exposure time, concentration of peptides, and amino acid functionalization. Modulating the time of UV irradiation resulted in PNIPAM-PEGDA:PFB-FFD hydrogels with a four-fold increase in stiffness. The influence of amino acid side chains and terminal charge of peptides on the strength of DN hydrogels was also investigated using pentafluorobenzyl diphenylalanyl lysine (PFB-FFK). Interestingly, PFB-FFK, which has an amine group on the side chain, does not exhibit the DN structures. The mechanical properties and pore sizes of PNIPAM-PEGDA:PFB-FFK hydrogel were very similar to those of the PNIPAM-PEGDA copolymer due to poor cross-linking. The biocompatibility of the hydrogel materials was tested with the hMSC cell line using the MTT method, and the results indicate that the materials are non-toxic and potentially useful for biological applications.

Fabrication of magnetic covalent organic framework for effective and selective solid-phase extraction of propylparaben from food samples.

Efficient magnetic solid phase extraction using crystalline porous polymers can find important applications in food safety. Herein, the core-shell Fe3O4@COFs nanospheres were synthesized by one-pot method and characterized in detail. The porous COF shell with large surface area had fast and selective adsorption for propylparaben via π-π, hydrogen bonding and hydrophobic interactions. The extraction and desorption parameters were evaluated in detail. Under the optimized conditions, the extraction equilibrium was reached only in 5 min, the maximum adsorption capacity for propylparaben was 500 mg g-1 and the proposed Fe3O4@DhaTab-based-MSPE-HPLC-UV method afforded good linearity (4-20000 µg mL-1) with R2 (0.997), low limits of detection (0.55 µg L-1) and limits of quantification (1.5 µg L-1). Furthermore, the developed method was applied to determine propylparaben in soft drinks with the recoveries (97.0-98.3%) and relative standard deviations (0.61 to 3.75%). These results revealed the potential of Fe3O4@DhaTab as efficient adsorbents for parabens in food samples.

Fine-Grained 3D Shape Classification With Hierarchical Part-View Attention.

Fine-grained 3D shape classification is important for shape understanding and analysis, which poses a challenging research problem. However, the studies on the fine-grained 3D shape classification have rarely been explored, due to the lack of fine-grained 3D shape benchmarks. To address this issue, we first introduce a new 3D shape dataset (named FG3D dataset) with fine-grained class labels, which consists of three categories including airplane, car and chair. Each category consists of several subcategories at a fine-grained level. According to our experiments under this fine-grained dataset, we find that state-of-the-art methods are significantly limited by the small variance among subcategories in the same category. To resolve this problem, we further propose a novel fine-grained 3D shape classification method named FG3D-Net to capture the fine-grained local details of 3D shapes from multiple rendered views. Specifically, we first train a Region Proposal Network (RPN) to detect the generally semantic parts inside multiple views under the benchmark of generally semantic part detection. Then, we design a hierarchical part-view attention aggregation module to learn a global shape representation by aggregating generally semantic part features, which preserves the local details of 3D shapes. The part-view attention module hierarchically leverages part-level and view-level attention to increase the discriminability of our features. The part-level attention highlights the important parts in each view while the view-level attention highlights the discriminative views among all the views of the same object. In addition, we integrate a Recurrent Neural Network (RNN) to capture the spatial relationships among sequential views from different viewpoints. Our results under the fine-grained 3D shape dataset show that our method outperforms other state-of-the-art methods. The FG3D dataset is available at https://github.com/liuxinhai/FG3D-Net.

3D fluorescence confocal microscopy of InGaN/GaN multiple quantum well nanorods from a light absorption perspective.

A nanostructure of In0.18Ga0.82N/GaN multiple quantum well (MQW) nanorods (NRs) was fabricated using top-down etching with self-organized nickel (Ni) nanoparticles as masks on the wafer. The optical properties of In0.18Ga0.82N/GaN MQW NRs were discussed by experiment and theory from a light absorption perspective. Three-dimensional (3D) optical images of NRs were successfully obtained by confocal laser scanning microscopy (CLSM) for physical observation of the optical phenomenon of InGaN/GaN MQW NRs. Moreover, optical simulations were performed by COMSOL Multiphysics via the three-dimensional finite-element method to explore the influences of NR geometrical parameters on optical absorption. The simulated results demonstrate that the absorption of NRs is higher than that of the film due to the waveguide properties of NRs resulting from their higher refractive index than embedding medium and higher aspect ratio than bulk. In addition, an increase in the diameter results in a red-shift of the absorption peak position of In0.18Ga0.82N/GaN MQW NRs. The smaller pitch enhances the near-field coupling of the nanorods and broadens the absorption peak. These results clearly illustrate the optical properties of In0.18Ga0.82N/GaN MQW NRs from the perspective of 3D confocal laser scanning microscopy. This work is promising for the applications of III-V optoelectronic devices.

Reconstructing 3D Shapes from Multiple Sketches using Direct Shape Optimization.

3D shape reconstruction from multiple hand-drawn sketches is an intriguing way to 3D shape modeling. Currently, state-of-the-art methods employ neural networks to learn a mapping from multiple sketches from arbitrary view angles to a 3D voxel grid. Because of the cubic complexity of 3D voxel grids, however, neural networks are hard to train and limited to low resolution reconstructions, which leads to a lack of geometric detail and low accuracy. To resolve this issue, we propose to reconstruct 3D shapes from multiple sketches using direct shape optimization (DSO), which does not involve deep learning models for direct voxel-based 3D shape generation. Specifically, we first leverage a conditional generative adversarial network (CGAN) to translate each sketch into an attenuance image that captures the predicted geometry from a given viewpoint. Then, DSO minimizes a project-and-compare loss to reconstruct the 3D shape such that it matches the predicted attenuance images from the view angles of all input sketches. Based on this, we further propose a progressive update approach to handle inconsistencies among a few hand-drawn sketches for the same 3D shape. Our experimental results show that our method significantly outperforms the state-of-the-art methods under widely used benchmarks and produces intuitive results in an interactive application.

Point2SpatialCapsule: Aggregating Features and Spatial Relationships of Local Regions on Point Clouds using Spatial-aware Capsules.

Learning discriminative shape representation directly on point clouds is still challenging in 3D shape analysis and understanding. Recent studies usually involve three steps: first splitting a point cloud into some local regions, then extracting the corresponding feature of each local region, and finally aggregating all individual local region features into a global feature as shape representation using simple max-pooling. However, such pooling-based feature aggregation methods do not adequately take the spatial relationships (e.g. the relative locations to other regions) between local regions into account, which greatly limits the ability to learn discriminative shape representation. To address this issue, we propose a novel deep learning network, named Point2SpatialCapsule, for aggregating features and spatial relationships of local regions on point clouds, which aims to learn more discriminative shape representation. Compared with the traditional max-pooling based feature aggregation networks, Point2SpatialCapsule can explicitly learn not only geometric features of local regions but also the spatial relationships among them. Point2SpatialCapsule consists of two main modules. To resolve the disorder problem of local regions, the first module, named geometric feature aggregation, is designed to aggregate the local region features into the learnable cluster centers, which explicitly encodes the spatial locations from the original 3D space. The second module, named spatial relationship aggregation, is proposed for further aggregating the clustered features and the spatial relationships among them in the feature space using the spatial-aware capsules developed in this paper. Compared to the previous capsule network based methods, the feature routing on the spatial-aware capsules can learn more discriminative spatial relationships among local regions for point clouds, which establishes a direct mapping between log priors and the spatial locations through feature clusters. Experimental results demonstrate that Point2SpatialCapsule outperforms the state-of-the-art methods in the 3D shape classification, retrieval and segmentation tasks under the well-known ModelNet and ShapeNet datasets.

Electronic Properties of Armchair Black Phosphorene Nanoribbons Edge-Modified by Transition Elements V, Cr, and Mn.

The structural, electrical, and magnetic properties of armchair black phosphorene nanoribbons (APNRs) edge-functionalized by transitional metal (TM) elements V, Cr, and Mn were studied by the density functional theory combined with the non-equilibrium Green's function. Spin-polarized edge states introduce great varieties to the electronic structures of TM-APNRs. For APNRs with Mn-stitched edge, their band structures exhibit half-semiconductor electrical properties in the ferromagnetic state. A transverse electric field can then make the Mn-APNRs metallic by shifting the conduction bands of edge states via the Stark effect. The Mn/Cr-APNR heterojunction may be used to fabricate spin p-n diode where strong rectification acts only on one spin.

3D2SeqViews: Aggregating Sequential Views for 3D Global Feature Learning by CNN With Hierarchical Attention Aggregation.

Learning 3D global features by aggregating multiple views is important. Pooling is widely used to aggregate views in deep learning models. However, pooling disregards a lot of content information within views and the spatial relationship among the views, which limits the discriminability of learned features. To resolve this issue, 3D to Sequential Views (3D2SeqViews) is proposed to more effectively aggregate the sequential views using convolutional neural networks with a novel hierarchical attention aggregation. Specifically, the content information within each view is first encoded. Then, the encoded view content information and the sequential spatiality among the views are simultaneously aggregated by the hierarchical attention aggregation, where view-level attention and class-level attention are proposed to hierarchically weight sequential views and shape classes. View-level attention is learned to indicate how much attention is paid to each view by each shape class, which subsequently weights sequential views through a novel recursive view integration. Recursive view integration learns the semantic meaning of view sequence, which is robust to the first view position. Furthermore, class-level attention is introduced to describe how much attention is paid to each shape class, which innovatively employs the discriminative ability of the fine-tuned network. 3D2SeqViews learns more discriminative features than the state-of-the-art, which leads to the outperforming results in shape classification and retrieval under three large-scale benchmarks.

Dynamically loading IFC models on a web browser based on spatial semantic partitioning.

Industry foundation classes (IFC) is an open and neutral data format specification for building information modeling (BIM) that plays a crucial role in facilitating interoperability. With increases in web-based BIM applications, there is an urgent need for fast loading large IFC models on a web browser. However, the task of fully loading large IFC models typically consumes a large amount of memory of a web browser or even crashes the browser, and this significantly limits further BIM applications. In order to address the issue, a method is proposed for dynamically loading IFC models based on spatial semantic partitioning (SSP). First, the spatial semantic structure of an input IFC model is partitioned via the extraction of story information and establishing a component space index table on the server. Subsequently, based on user interaction, only the model data that a user is interested in is transmitted, loaded, and displayed on the client. The presented method is implemented via Web Graphics Library, and this enables large IFC models to be fast loaded on the web browser without requiring any plug-ins. When compared with conventional methods that load all IFC model data for display purposes, the proposed method significantly reduces memory consumption in a web browser, thereby allowing the loading of large IFC models. When compared with the existing method of spatial partitioning for 3D data, the proposed SSP entirely uses semantic information in the IFC file itself, and thereby provides a better interactive experience for users.

SeqViews2SeqLabels: Learning 3D Global Features via Aggregating Sequential Views by RNN With Attention.

Learning 3D global features by aggregating multiple views has been introduced as a successful strategy for 3D shape analysis. In recent deep learning models with end-to-end training, pooling is a widely adopted procedure for view aggregation. However, pooling merely retains the max or mean value over all views, which disregards the content information of almost all views and also the spatial information among the views. To resolve these issues, we propose Sequential Views To Sequential Labels (SeqViews2SeqLabels) as a novel deep learning model with an encoder-decoder structure based on recurrent neural networks (RNNs) with attention. SeqViews2SeqLabels consists of two connected parts, an encoder-RNN followed by a decoder-RNN, that aim to learn the global features by aggregating sequential views and then performing shape classification from the learned global features, respectively. Specifically, the encoder-RNN learns the global features by simultaneously encoding the spatial and content information of sequential views, which captures the semantics of the view sequence. With the proposed prediction of sequential labels, the decoder-RNN performs more accurate classification using the learned global features by predicting sequential labels step by step. Learning to predict sequential labels provides more and finer discriminative information among shape classes to learn, which alleviates the overfitting problem inherent in training using a limited number of 3D shapes. Moreover, we introduce an attention mechanism to further improve the discriminative ability of SeqViews2SeqLabels. This mechanism increases the weight of views that are distinctive to each shape class, and it dramatically reduces the effect of selecting the first view position. Shape classification and retrieval results under three large-scale benchmarks verify that SeqViews2SeqLabels learns more discriminative global features by more effectively aggregating sequential views than state-of-the-art methods.

Electronic Structure and I-V Characteristics of InSe Nanoribbons.

We have studied the electronic structure and the current-voltage (I-V) characteristics of one-dimensional InSe nanoribbons using the density functional theory combined with the nonequilibrium Green's function method. Nanoribbons having bare or H-passivated edges of types zigzag (Z), Klein (K), and armchair (A) are taken into account. Edge states are found to play an important role in determining their electronic properties. Edges Z and K are usually metallic in wide nanoribbons as well as their hydrogenated counterparts. Transition from semiconductor to metal is observed in hydrogenated nanoribbons HZZH as their width increases, due to the strong width dependence of energy difference between left and right edge states. Nevertheless, electronic structures of other nanoribbons vary with the width in a very limited scale. The I-V characteristics of bare nanoribbons ZZ and KK show strong negative differential resistance, due to spatial mismatch of wave functions in energy bands around the Fermi energy. Spin polarization in these nanoribbons is also predicted. In contrast, bare nanoribbons AA and their hydrogenated counterparts HAAH are semiconductors. The band gaps of nanoribbons AA (HAAH) are narrower (wider) than that of two-dimensional InSe monolayer and increase (decrease) with the nanoribbon width.