Shape Template Research Articles

Hole Appearance Constraint Method in 2D Structural Topology Optimization

A 2D topology optimization algorithm is proposed, which integrates the control of hole shape, hole number, and the minimum scale between holes through the utilization of an appearance target image. The distance between the structure and the appearance target image is defined as the hole appearance constraint. The appearance constraint is organized as inequality constraints to control the performance of the structure in an iterative optimization. Specifically, hole shapes are controlled by matching adaptable equivalent shape templates, the minimum scales between holes are controlled by a hole shrinkage strategy, and the hole number is controlled by a hole number calculation and filling method. Based on the SIMP interpolation topology optimization model, the effectiveness of the proposed method is verified through numerical examples.

HM–GDM: Hybrid Measures and Graph-Dependent Modeling for Environmental Sound Classification

One of the upcoming areas of research is the automated environmental sound classification, as its application is highly utilized in criminal investigations scenarios, surveillance systems, biomedical fields, radio navigation systems, etc. The procedure of environmental sound classification deals with standardized techniques such as feature extraction and feature selection, followed by classification with machine learning or deep learning techniques. In this work, five feature extraction techniques are used initially such as the discrete wavelet transform (DWT)-based empirical mode decomposition (EMD) technique, phase space reconstruction (PSR), kernel principal component analysis (KPCA), variational mode decomposition (VMD) and tunable Q-wavelet transform (TQWT). The extracted features are then selected through seven feature selection techniques, out of which two have been proposed newly and five have already existed. The proposed feature selection techniques are stratified clustered collective technique (SCCT) and fuzzy-based template shape clustering (FTSC). The other feature selection techniques used are the generalized discriminant analysis (GDA), ReliefF, Fisher discriminant criterion (FDC), Kruskal–Wallis test and coati optimization algorithm (COA). The selected features are then classified through the proposed swarm intelligence-based hybrid Adaboost – random forest termed as SIHAR classifier and the selected features are classified with the less explored sparse representation classifier (SRC) along with eight other traditionally used machine learning classifiers. The work also proposes graph-dependent modeling for the environmental sounds where the rhythms are extracted for the similarity assessment and later the decision-making is done using hypothesis testing. The work is tested on Firat ESC-50 dataset and the best results are produced in terms of a high classification accuracy of 87.48% which is obtained for the TQWT + SCCT + SIHAR combination.

Insights from Optical Fe ii Emission in Quasars

The complexity of the energy levels in Fe ii has made it difficult to theoretically interpret its observational emission spectrum. However, addressing this challenge could provide valuable insights into the main sequence of QSOs using “eigenvector 1” and the chemical evolution of the Universe. Although the templates based on I Zw 1 are widely used, their universality remains unexplained. To address this, we utilized the CLOUDY spectral synthesis code to generate theoretical blends of Fe ii optical emission near the Hβ region. Our results indicate that the optimal cloud density and photon flux align with previous studies at gas density 1011 cm−3 and photon flux 1020.5 cm−2 s−1. We discovered a broad parameter region that fits the data well, encompassing a diverse range of densities and photon fluxes. This helps to explain the general applicability of optical templates. Additionally, we investigated the effects of different spectral energy distributions (SEDs), Eddington ratios, turbulence, and cloud column densities and found that they provided a sufficiently broad range for typical emitting clouds. We found different SEDs had a negligible impact on the template shape, further expanding the usability of empirical templates. However, they significantly affected the absolute intensity of Fe ii emission, consistent with previous research. Furthermore, we determined that a turbulence of approximately 100 km s−1 is necessary to produce sufficiently strong optical Fe ii, and either a column density greater than 1024 cm−2, which is typical for Fe ii UV emission, or an abundance higher than solar is preferred.

"Hard" Emulsion-Induced Interface Super-Assembly: A General Strategy for Two-Dimensional Hierarchically Porous Metal-Organic Framework Nanoarchitectures.

Two-dimensional (2D) hierarchically porous metal-organic framework (MOF) nanoarchitectures with tailorable meso-/macropores hold great promise for enhancing mass transfer kinetics, augmenting accessible active sites, and thereby boosting performance in heterogeneous catalysis. However, achieving the general synthesis of 2D free-standing MOF nanosheets with controllable hierarchical porosity and thickness remains a challenging task. Herein, we present an ingenious "hard" emulsion-induced interface super-assembly strategy for preparing 2D hierarchically porous UiO-66-NH2 nanosheets with highly accessible pore channels, tunable meso-/macropore sizes, and adjustable thicknesses. The methodology relies on transforming the geometric shape of oil droplet templates within appropriate oil-in-water emulsions from conventional zero-dimensional (0D) "soft" liquid spheres to 2D "hard" solid sheets below the oil's melting/freezing point. Subsequent surfactant exchange on the surface of 2D "hard" emulsions facilitates the heterogeneous nucleation and interfacial super-assembly of in situ formed mesostructured MOF nanocomposites, serving as structural units, in a loosely packed manner to produce 2D MOF nanosheets with multimodal micro/meso-/macroporous systems. Importantly, this strategy can be extended to prepare other 2D hierarchically porous MOF nanosheets by altering metal-oxo clusters and organic ligands. Benefiting from fast mass transfer and highly accessible Lewis acidic sites, the resultant 2D hierarchically porous UiO-66-NH2 nanosheets deliver a fabulous catalytic yield of approximately 96% on the CO2 cycloaddition of glycidyl-2-methylphenyl ether, far exceeding the yield of approximately 29% achieved using conventional UiO-66-NH2 microporous crystals. This "hard" emulsion-induced interface super-assembly strategy paves a new path toward the rational construction of elaborate 2D nanoarchitecture of hierarchical MOFs with tailored physicochemical properties for diverse potential applications.

What shapes template-matching performance in cryogenic electron tomography in situ?

The detection of specific biological macromolecules in cryogenic electron tomography data is frequently approached by applying cross-correlation-based 3D template matching. To reduce computational cost and noise, high binning is used to aggregate voxels before template matching. This remains a prevalent practice in both practical applications and methods development. Here, the relation between template size, shape and angular sampling is systematically evaluated to identify ribosomes in a ground-truth annotated data set. It is shown that at the commonly used binning, a detailed subtomogram average, a sphere and a heart emoji result in near-identical performance. These findings indicate that with current template-matching practices macromolecules can only be detected with high precision if their shape and size are sufficiently different from the background. Using theoretical considerations, the experimental results are rationalized and it is discussed why primarily low-frequency information remains at high binning and that template matching fails to be accurate because similarly shaped and sized macromolecules have similar low-frequency spectra. These challenges are discussed and potential enhancements for future template-matching methodologies are proposed.

Customized Self-Assembled Gold Nanoparticle-DNA Origami Composite Templates for Shape-Directed Growth of Plasmonic Structures.

The metal plasmonic nanostructure has the optical property of plasmon resonance, which holds great potential for development in nanophotonics, bioelectronics, and molecular detection. However, developing a general and straightforward method to prepare metal plasmonic nanostructures with a controllable size and morphology still poses a challenge. Herein, we proposed a synthesis strategy that utilized a customizable self-assembly template for shape-directed growth of metal structures. We employed gold nanoparticles (AuNPs) as connectors and DNA nanotubes as branches, customizing gold nanoparticle-DNA origami composite nanostructures with different branches by adjusting the assembly ratio between the connectors and branches. Subsequently, various morphologies of plasmonic metal nanostructures were created using this template shape guided strategy, which exhibited enhancement of surface-enhanced Raman scattering (SERS) signals. This strategy provides a new approach for synthesizing metallic nanostructures with multiple morphologies and opens up another possibility for the development of customizable metallic plasmonic structures with broader applications.

Automated Landmark Annotation for Morphometric Analysis of Distal Femur and Proximal Tibia.

Manual anatomical landmarking for morphometric knee bone characterization in orthopedics is highly time-consuming and shows high operator variability. Therefore, automation could be a substantial improvement for diagnostics and personalized treatments relying on landmark-based methods. Applications include implant sizing and planning, meniscal allograft sizing, and morphological risk factor assessment. For twenty MRI-based 3D bone and cartilage models, anatomical landmarks were manually applied by three experts, and morphometric measurements for 3D characterization of the distal femur and proximal tibia were calculated from all observations. One expert performed the landmark annotations three times. Intra- and inter-observer variations were assessed for landmark position and measurements. The mean of the three expert annotations served as the ground truth. Next, automated landmark annotation was performed by elastic deformation of a template shape, followed by landmark optimization at extreme positions (highest/lowest/most medial/lateral point). The results of our automated annotation method were compared with ground truth, and percentages of landmarks and measurements adhering to different tolerances were calculated. Reliability was evaluated by the intraclass correlation coefficient (ICC). For the manual annotations, the inter-observer absolute difference was 1.53 ± 1.22 mm (mean ± SD) for the landmark positions and 0.56 ± 0.55 mm (mean ± SD) for the morphometric measurements. Automated versus manual landmark extraction differed by an average of 2.05 mm. The automated measurements demonstrated an absolute difference of 0.78 ± 0.60 mm (mean ± SD) from their manual counterparts. Overall, 92% of the automated landmarks were within 4 mm of the expert mean position, and 95% of all morphometric measurements were within 2 mm of the expert mean measurements. The ICC (manual versus automated) for automated morphometric measurements was between 0.926 and 1. Manual annotations required on average 18 min of operator interaction time, while automated annotations only needed 7 min of operator-independent computing time. Considering the time consumption and variability among observers, there is a clear need for a more efficient, standardized, and operator-independent algorithm. Our automated method demonstrated excellent accuracy and reliability for landmark positioning and morphometric measurements. Above all, this automated method will lead to a faster, scalable, and operator-independent morphometric analysis of the knee.

Precise tooth design using deep learning-based templates

ObjectivesIn prosthodontic procedures, traditional computer-aided design (CAD) is often time-consuming and lacks accuracy in shape restoration. In this study, we combined implicit template and deep learning (DL) to construct a precise neural network for personalized tooth defect restoration. MethodsNinety models of right maxillary central incisor (80 for training, 10 for validation) were collected. A DL model named ToothDIT was trained to establish an implicit template and a neural network capable of predicting unique identifications. In the validation stage, teeth in validation set were processed into corner, incisive, and medium defects. The defective teeth were inputted into ToothDIT to predict the unique identification, which actuated the deformation of the implicit template to generate the highly customized template (DIT) for the target tooth. Morphological restorations were executed with templates from template shape library (TSL), average tooth template (ATT), and DIT in Exocad (GmbH, Germany). RMSestimate, width, length, aspect ratio, incisal edge curvature, incisive end retraction, and guiding inclination were introduced to assess the restorative accuracy. Statistical analysis was conducted using two-way ANOVA and paired t-test for overall and detailed differences. ResultsDIT displayed significantly smaller RMSestimate than TSL and ATT. In 2D detailed analysis, DIT exhibited significantly less deviations from the natural teeth compared to TSL and ATT. ConclusionThe proposed DL model successfully reconstructed the morphology of anterior teeth with various degrees of defects and achieved satisfactory accuracy. This approach provides a more reliable reference for prostheses design, resulting in enhanced accuracy in morphological restoration. Clinical significanceThis DL model holds promise in assisting dentists and technicians in obtaining morphology templates that closely resemble the original shape of the defective teeth. These customized templates serve as a foundation for enhancing the efficiency and precision of digital restorative design for defective teeth.

Development of a pile-up pulse recovery algorithm for the LaBr[formula omitted] detector

Precise measurement under high counting rates has been a major challenge for γ-ray spectroscopy, where signals generally pile up with each other and their characteristic values are deeply coupled. An off-line pile-up pulse recovery algorithm based on template matching is developed for LaBr3 scintillation detectors and has been applied to data from China Spallation Neutron Source (CSNS) Back-n white neutron beamline. As an improvement to the traditional Template Matching methods, the predetermined template shape is associated with pulse amplitudes, which is found crucial to achieve the best fitting results. Moreover, a universal strategy of setting the initial values of fitting allows to resolve signals that are separated in time by less than the characteristic rise time. Processing of simulated pulses demonstrates that the lower limit of interval to distinguish successive events is 10 ns, which is a significant improvement to the previous works. The algorithm performs well under a counting rate of 6×107 s−1, and is still effective up to 108 s−1. Analysis of experimental data indicates that this algorithm recovers over 98% count yield under a counting rates of no more than 4×106 s−1 in this work, providing a more reliable approach to deduce neutron-induced reaction cross sections.

Research on the forming and demolding process of shape memory self-demolding mold

The molding method is highly anticipated in engineering fields and has been preliminarily applied. However, traditional demolding methods introduce subtle deviations in demolding direction, leading to demolding defects and reduced forming accuracy. To address this, we developed an innovative Shape Memory Polymer (SMP) mold. Through finite element simulation, we extensively investigated the effects of diaminodiphenylmethane (DDM) addition, temperature, and load on the template forming and shape recovery processes. Finally, we experimentally verified the feasibility of the self-demolding of the template. The findings demonstrate that as the amount of DDM added increases, the glass transition temperature of SMP progressively elevates. This can be attributed to the enhanced crosslinking triggered by the addition of DDM. Consequently, during the stage of preserving shape in mold formation, SMP exhibits a heightened storage modulus and diminished shape recovery. Notably, when 1.5 g of DDM is added, the shape memory template exhibits the least shape recovery, with a shape retention rate of 78.2%. Conversely, employing 1.6 g of DDM generates the highest shape recovery but only achieves a shape retention rate of 59.5%. Augmenting the amount of DDM is advantageous in accomplishing template demolding. Higher temperatures expedite the initiation of the shape recovery process, facilitating template demolding during the shape recovery stage. Moreover, increasing the load can minimize template rebound and enhance the precision of mold formation. However, it also intensifies the complexity of self-demolding. Consequently, it is imperative to sensibly determine and implement suitable process parameters in applications.

Estimation of footprints of the canine stifle ligaments using deformable shape templates of bones

Knowledge regarding the ligament footprints in the canine stifle is essential for biomechanical modeling of the joint and patient-specific surgical planning for anatomical ligament reconstruction. The present study aimed to establish and evaluate deformable shape templates (DSTs) of the femur and tibia with footprints of the cruciate and collateral ligaments embedded for the noninvasive estimation of ligament footprint positions. To this end, a data set of computed tomography (CT)-derived surface models of the femur and tibia were established and used to build statistical shape models (SSMs). The contours of the stifle ligaments were obtained from CT scans of 27 hindlimb specimens with radio-opaque markings on the ligament footprints. The DST, constructed by embedding averaged footprint contours into the SSM, was used to estimate subject-specific ligament footprints in a leave-one-out cross-validation framework. The DST predictions were compared with those derived from radio-opaque-marked footprints. The results showed that the averaged Euclidean distances between the estimated and reference footprint centroids were less than 1.2 mm for the cruciate ligaments and 2.0 mm for the collateral ligaments. The DST appeared to provide a feasible alternative approach for noninvasively estimating the footprints of the stifle ligaments in vivo.

Shape embedding and retrieval in multi-flow deformation

We propose a unified 3D flow framework for joint learning of shape embedding and deformation for different categories. Our goal is to recover shapes from imperfect point clouds by fitting the best shape template in a shape repository after deformation. Accordingly, we learn a shape embedding for template retrieval and a flow-based network for robust deformation. We note that the deformation flow can be quite different for different shape categories. Therefore, we introduce a novel multi-hub module to learn multiple modes of deformation to incorporate such variation, providing a network which can handle a wide range of objects from different categories. The shape embedding is designed to retrieve the best-fit template as the nearest neighbor in a latent space. We replace the standard fully connected layer with a tiny structure in the embedding that significantly reduces network complexity and further improves deformation quality. Experiments show the superiority of our method to existing state-of-the-art methods via qualitative and quantitative comparisons. Finally, our method provides efficient and flexible deformation that can further be used for novel shape design.

Spermbots: A Promising Futuristic Innovation in Assisted Reproductive Technology

Spermbots are robotic sperms formed out of sperm cells conjugated to artificial microstructures, having potential applications ranging from biomedical processes, drug delivery systems, in situ real time imagery and assisted reproduction. The robotic sperm can act as an exploratory microdevice in biological networks. Incorporation of a biological entity like sperm into microstructures under the environment of magnetic or electric fields helps in shape templating and carrying chemotherapeutic agents to target sites. Besides its role in drug delivery systems, spermbots can potentially contribute in combating infertility, especially in oligo-zoospermia and necro-zoospermia in males. Numerous checkpoints may impede sperm cells to reach the oocyte in vivo. Spermbots bypass these sites and carry sperm to oocyte. Targeted delivery always requires interventions of natural functional aspects of living systems. Sperm flagellum, being a biological motor in nature, can be harnessed as a driving force in spermbots to ensure delivery of fertile sperm cells with impaired motile machinery to the target. Moreover, the technology has a potential to unravel sperm migration patterns and understanding the processes in vivo. After a review of documented literature on possible use of spermbot technology in assisted reproduction, we hereby discuss the application of this new and innovative technology in humans and animals. The paper also highlights certain shortfalls in the widespread application of this cargo delivery technology in assisted reproduction.

A new magnetic heterogeneous catalyst for fast degradation of rhodamine B by peroxymonosulfate oxidation: Monodisperse-porous and Fe3O4 incorporated manganese oxide microspheres

A new synthetic method, “staged shape templating decomposition” suitable for transition or rare-earth metal oxide microspheres was developed for obtaining magnetic, uniform and porous manganese oxide (MagMnOx) microspheres. The microspheres 4.7 μm in size were obtained with a specific surface area of 45.5 m2 g−1. Fe3O4, Mn5O8, MnO2 and Mn2O3 phases were shown by X-ray diffraction spectroscopy. Mn2+, Mn3+ and Mn4+ states were observed on the surface by X-ray photoelectron spectroscopy. Catalase-like and oxidase-like activities of MagMnOx microspheres were explained by coexistence of Mn and Fe crystallites in a mesoporous structure with a sufficiently large surface area and a particle interior with large mesopores facilitating substrate diffusion. The maximum substate consumption rates for catalase-like and peroxidase-like activities were determined as 15.6 mM/min and 20.3 μM/min, respectively. By considering their ROS generation ability, MagMnOx microspheres were evaluated as a heterogeneous catalyst for rhodamine B (Rh B) removal via peroxymonosulfate (PMS) activation. Rh B was removed in the periods shorter than 15 min via chemical degradation with a limited physical adsorption. Superoxide anion (O2−●) and singlet oxygen (1O2●) radicals generated by MagMnOx-PMS system were responsible for degradation of Rh B. The highest apparent first order rate constant for Rh B degradation was calculated as 0.5771 min−1. The apparent first order rate constant increased with increasing MagMnOx and PMS concentrations and decreasing initial Rh B concentration. Rh B was almost completely removed in each of five recycling runs and only lower than 1.5 % w/w of Fe and Mn contents of MagMnOx microspheres were leached over five recycling runs. The catalyst with large particle size, resistant to agglomeration exhibited high removal rate, low dye adsorption, high magnetization, and high reusability for Rh B removal via peroxymonosulfate activation.

Learning from vector data: enhancing vector-based shape encoding and shape classification for map generalization purposes

ABSTRACT Map generalization is a complex task that requires a high level of spatial cognition, and deep learning techniques have shown in numerous research fields that they could match or even outplay human cognition when knowledge is implicitly in the data. First experiments that apply deep learning techniques to map generalization tasks thereby adapt models from image processing, creating input data by rasterizing spatial vector data. Because image-based learning has major shortcomings for map generalization, this article investigates possibilities to learn directly from vector data, utilizing vector-based encoding schemes. First, we enhance preprocessing methods to match essential properties of deep learning models – namely regularity and feature description – and evaluate the performance of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Graph Convolutional Neural Networks (GCNN) in combination with a feature-based encoding scheme. The results show that feature descriptors improve the accuracy of all three neural networks, and that the overall performances of the models are quite similar for both polygon and polyline shape classification tasks. In a second step, we implement an exemplary building generalization workflow based on shape classification and template matching, and discuss the generalization results based on a case study.

Discrete element simulations of flexible ribbon-like particles

A flexible ribbon-like particle model is developed based on the Discrete Element Method (DEM) for numerical modelling of biomass materials and many other flexible materials encountered in industries. In the model, a thin, irregular-shaped particle is discretized into triangular facet elements that can deform subject to internal and external forces. A library of particle templates of various shapes is established for numerical generation of a collection of particles with a statistical distribution of particle shapes in the simulations. The developed particle model is adopted to simulate uniaxial compression of biomass materials. With the calibrated model parameters, simulation results of various biomass materials and their mixtures are in good agreement with experimental data. For a better understanding of packing behavior of the biomass materials, DEM studies are performed to reveal the effects of initial particle configuration, particle shape distribution, and particle size distribution on macroscopic and micro-structural properties.

Capacitive performance of electrode materials affected by the Cu2O template morphologies in graphene/polyaniline nanotube/ZIF-67 nanocages porous composite

Graphene/polyaniline nanotube/ZIF-67 nanocages (G/PANI-NT/Z) composites were successfully prepared in a co-precipitation manner and used as electrode material for supercapacitor application. ZIF-67 nanocages, including cube and hexapod, were designed via a simple and fast one-step Cu2O template etching route. The properties of the as-synthesized nanoparticles were perused by N2 adsorption–desorption, X-ray diffraction (XRD), field emission scanning electron microscope (FESEM), and electrochemical manners. In particular, the super-capacitive performance of the synthesized materials was perused by changing the morphology of the nanostructures. Results represent that the as-synthesized ZIF-67 nanocages maintain the shape and size of the Cu2O templates with a hollow, which depicts various capacitive demeanors. Amongst, the two morphologies, the G/PANI-NT/Z-hexapod (h) composite has a larger specific capacitance of 4400 mF g−1, while G/PANI-NT/Z-cubic (c) has a lower one of 3630 mF g−1 at 10 mA g−1 current density. This is mainly due to the presence of ZIF-67 nanocages with hexapod morphology and a larger surface area of 73.58 m2/g in the G/PANI-NT/Z-h composite, which leads to fast interfacial electron transfer and an increase in the diffusion rate of electrolyte ions for higher power density. This demonstrates G/PANI-NT/Z-h electrode is promising for applications in renewable energy storage.

Automated body composition estimation from device-agnostic 3D optical scans in pediatric populations

Excess adiposity in children is strongly correlated with obesity-related metabolic disease in adulthood, including diabetes, cardiovascular disease, and 13 types of cancer. Despite the many long-term health risks of childhood obesity, body mass index (BMI) Z-score is typically the only adiposity marker used in pediatric studies and clinical applications. The effects of regional adiposity are not captured in a single scalar measurement, and their effects on short- and long-term metabolic health are largely unknown. However, clinicians and researchers rarely deploy gold-standard methods for measuring compartmental fat such as magnetic resonance imaging (MRI) and dual X-ray absorptiometry (DXA) on children and adolescents due to cost or radiation concerns. Three-dimensional optical (3DO) scans are relatively inexpensive to obtain and use non-invasive and radiation-free imaging techniques to capture the external surface geometry of a patient's body. This 3D shape contains cues about the body composition that can be learned from a structured correlation between 3D body shape parameters and reference DXA scans obtained on a sample population. This study seeks to introduce a radiation-free, automated 3D optical imaging solution for monitoring body shape and composition in children aged 5-17. We introduce an automated, linear learning method to predict total and regional body composition of children aged 5-17 from 3DO scans. We collected 145 male and 206 female 3DO scans on children between the ages of 5 and 17 with three scanners from independent manufacturers. We used an automated shape templating method first introduced on an adult population to fit a topologically consistent 60,000 vertex (60k) mesh to 3DO scans of arbitrary scanning source and mesh topology. We constructed a parameterized body shape space using principal component analysis (PCA) and estimated a regression matrix between the shape parameters and their associated DXA measurements. We automatically fit scans of 30 male and 38 female participants from a held-out test set and predicted 12 body composition measurements. The coefficient of determination (R2) between 3DO predicted body composition and DXA measurements was at least 0.85 for all measurements with the exception of visceral fat on 3D scan predictions. Precision error was 1-4 times larger than that of DXA. No predicted variable was significantly different from DXA measurement except for male trunk lean mass. Optical imaging can quickly, safely, and inexpensively estimate regional body composition in children aged 5-17. Frequent repeat measurements can be taken to chart changes in body adiposity over time without risk of radiation overexposure.

DOVE: Learning Deformable 3D Objects by Watching Videos

Learning deformable 3D objects from 2D images is often an ill-posed problem. Existing methods rely on explicit supervision to establish multi-view correspondences, such as template shape models and keypoint annotations, which restricts their applicability on objects “in the wild”. A more natural way of establishing correspondences is by watching videos of objects moving around. In this paper, we present DOVE, a method that learns textured 3D models of deformable object categories from monocular videos available online, without keypoint, viewpoint or template shape supervision. By resolving symmetry-induced pose ambiguities and leveraging temporal correspondences in videos, the model automatically learns to factor out 3D shape, articulated pose and texture from each individual RGB frame, and is ready for single-image inference at test time. In the experiments, we show that existing methods fail to learn sensible 3D shapes without additional keypoint or template supervision, whereas our method produces temporally consistent 3D models, which can be animated and rendered from arbitrary viewpoints. Project page: https://dove3d.github.io/.

Electroless Deposition of Silver onto Silica Nanoparticles to Produce Lipophilic Core-Shell Nanoparticles

HypothesisThe colloidal stability of noble metal nanoparticles can be tuned for solvents of varying hydrophobicity by modifying the surface chemistry of the particles with different capping agent architectures. Challenges arise when attempting to separately control multiple nanoparticle properties due to the interdependence of this adsorption process on the surface chemistry and metal architecture. A surfactant-mediated, templated synthesis strategy should decouple control over size and stability to produce lipophilic nanoparticles from aqueous reagents. ExperimentsA modified electroless plating process that produces oil-dispersible core–shell silver-silica nanoparticles is presented. Amine-terminated alkanes are utilized as the capping agents to generate lipophilic surface coatings and the particles are temporarily stabilized during the synthesis by adding a Pluronic surfactant that enhances dispersibility in the aqueous reaction medium. The evolution of shell morphology, composition, and colloidal stability was analyzed against capping agent architecture and concentration. The role of particle shape was also tested by interchanging the template geometry. FindingsThe capping agents installed on the silver shell surface displayed both colloidal stability enhancements and a minimum effective capping concentration that is a function of molecular weight without influencing the shell composition. Particle geometry can be controlled by interchanging the silica template size and shape.