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.

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.

Half-metallic properties, single-spin negative differential resistance, and large single-spin Seebeck effects induced by chemical doping in zigzag-edged graphene nanoribbons.

Ab initio calculations combining density-functional theory and nonequilibrium Green's function are performed to investigate the effects of either single B atom or single N atom dopant in zigzag-edged graphene nanoribbons (ZGNRs) with the ferromagnetic state on the spin-dependent transport properties and thermospin performances. A spin-up (spin-down) localized state near the Fermi level can be induced by these dopants, resulting in a half-metallic property with 100% negative (positive) spin polarization at the Fermi level due to the destructive quantum interference effects. In addition, the highly spin-polarized electric current in the low bias-voltage regime and single-spin negative differential resistance in the high bias-voltage regime are also observed in these doped ZGNRs. Moreover, the large spin-up (spin-down) Seebeck coefficient and the very weak spin-down (spin-up) Seebeck effect of the B(N)-doped ZGNRs near the Fermi level are simultaneously achieved, indicating that the spin Seebeck effect is comparable to the corresponding charge Seebeck effect.

Spin-dependent ballistic transport properties and electronic structures of pristine and edge-doped zigzag silicene nanoribbons: large magnetoresistance.

The electronic structure and conductance of substitutionally edge-doped zigzag silicene nanoribbons (ZSiNRs) are investigated using the nonequilibrium Green's function method combined with the density functional theory. Two-probe systems of ZSiNRs in both ferromagnetic and antiferromagnetic states are considered. Doping effects of elements from groups III and V, in a parallel or antiparallel magnetic configuration of the two electrodes, are discussed. By switching on and off the external magnetic field, we may convert the metallic ferromagnetic ZSiNRs into insulating antiferromagnetic ZSiNRs. In the ferromagnetic state, even- or odd-width ZSiNRs exhibit a drastically different magnetoresistance. In an odd-width edge-doped ZSiNR a large magnetoresistance occurs compared to that in a pristine ZSiNR. The situation is reversed in even-width ZSiNRs. These phenomena result from the drastic change in the conductance in the antiparallel configuration.

Tunable electronic and magnetic properties in germanene by alkali, alkaline-earth, group III and 3d transition metal atom adsorption.

We performed first-principles calculations to study the adsorption characteristics of alkali, alkali-earth, group III, and 3d transition-metal (TM) adatoms on germanene. We find that the adsorption of alkali or alkali-earth adatoms on germanene has minimal effects on geometry of germanene. The significant charge transfer from alkali adatoms to germanene leads to metallization of germanene, whereas alkali-earth adatom adsorption, whose interaction is a mixture of ionic and covalent, results in semiconducting behavior with an energy gap of 17-29 meV. For group III adatoms, they also bind germanene with mixed covalent and ionic bonding character. Adsorption characteristics of the transition metals (TMs) are rather complicated, though all TM adsorptions on germanene exhibit strong covalent bonding with germanene. The main contributions to the strong bonding are from the hybridization between the TM 3d and Ge pz orbitals. Depending on the induced-TM type, the adsorbed systems can exhibit metallic, half-metallic, or semiconducting behavior. Also, the variation trends of the dipole moment and work function with the adsorption energy across the different adatoms are discussed. These findings may provide a potential avenue to design new germanene-based devices in nanoelectronics.

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.

NeuralTPS: Learning Signed Distance Functions without Priors from Single Sparse Point Clouds.

Surface reconstruction for point clouds is one of the important tasks in 3D computer vision. The latest methods rely on generalizing the priors learned from large scale supervision. However, the learned priors usually do not generalize well to various geometric variations that are unseen during training, especially for extremely sparse point clouds. To resolve this issue, we present a neural network to directly infer SDFs from single sparse point clouds without using signed distance supervision, learned priors or even normals. Our insight here is to learn surface parameterization and SDFs inference in an end-to-end manner. To make up the sparsity, we leverage parameterized surfaces as a coarse surface sampler to provide many coarse surface estimations in training iterations, according to which we mine supervision for our thin plate splines (TPS) based network to infer smooth SDFs in a statistical way. Our method significantly improves the generalization ability and accuracy on unseen point clouds. Our experimental results show our advantages over the state-of-the-art methods in surface reconstruction for sparse point clouds under synthetic datasets and real scans.

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.

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.

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.

Self-assembled Fe3O4-COOH @ hydrogen-bonded organic framework composites for magnetic solid-phase extraction of tetracycline in food samples coupled with HPLC determination.

Magnetic solid-phase extraction (MSPE) technology for tetracycline (TCC) was developed by employing the novel and pre-designed Fe3O4-COOH@hydrogen-bonded organic frameworks (HOFs) adsorbents in complex food samples. The HOF shell was grown onto the Fe3O4-COOH core by in-situ self-assembled method. The excellent MSPE performances with less solvent, less adsorbent and time consumption were derived from the hydrogen bonding, π-π and hydrophobic interactions between HOF shell and TCC. Combined with HPLC analysis, Fe3O4@ HOFs adsorbent reduced matrix effects and the established MSPE-HPLC method for TCC gave the linearity of 0.001-6 µg mL-1 with the limit of detection 0.0003 µg mL-1. The recoveries in pure milk, canned yellow peach and carrot were 82.4-103.7 %. The method provided a simple, efficient and dependable alternative to monitor trace TCC antibiotics in food or environmental samples.

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 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.

3DMolNavi: a web-based retrieval and navigation tool for flexible molecular shape comparison.

BACKGROUND: Many molecules of interest are flexible and undergo significant shape deformation as part of their function, but most existing methods of molecular shape comparison treat them as rigid shapes, which may lead to incorrect measure of the shape similarity of flexible molecules. Currently, there still is a limited effort in retrieval and navigation for flexible molecular shape comparison, which would improve data retrieval by helping users locate the desirable molecule in a convenient way. RESULTS: To address this issue, we develop a web-based retrieval and navigation tool, named 3DMolNavi, for flexible molecular shape comparison. This tool is based on the histogram of Inner Distance Shape Signature (IDSS) for fast retrieving molecules that are similar to a query molecule, and uses dimensionality reduction to navigate the retrieved results in 2D and 3D spaces. We tested 3DMolNavi in the Database of Macromolecular Movements (MolMovDB) and CATH. Compared to other shape descriptors, it achieves good performance and retrieval results for different classes of flexible molecules. CONCLUSIONS: The advantages of 3DMolNavi, over other existing softwares, are to integrate retrieval for flexible molecular shape comparison and enhance navigation for user's interaction. 3DMolNavi can be accessed via https://engineering.purdue.edu/PRECISE/3dmolnavi/index.html.

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.

The role of Coulomb interaction in thermoelectric effects of an Aharonov-Bohm interferometer.

We investigate the thermoelectric effects of an Aharonov-Bohm (AB) interferometer with a quantum dot (QD) embedded in each of its arms, where the intra-dot Coulomb interaction between electrons in each QD is taken into account. Using Green's function methods and the equation of motion (EOM) technique, we find that the Seebeck coefficient and Lorenz number can be strongly enhanced when the chemical potential sweeps the molecular states associated with the Fano line-shapes in the transmission spectra, due to quantum interference effects between the bonding and antibonding molecular states. It is found that enhancement of the thermoelectric effects occurs between the two groups of conductance peaks in the presence of strong intra-dot Coulomb interaction-the reason being that a transmission node is developed in the Coulomb blockade regime. In this case, the maximum value of the Lorenz number approaches 10π(2)k(B)(2)/(3e(2)). Its thermoelectric conversion efficiency in the absence of phonon thermal conductance, described by the figure of merit ZT, approaches 2 at room temperature. Therefore, it may be used as a high-efficiency solid-state thermoelectric conversion device under certain circumstances.