arXiv subjects

Changyu Hu

Publications and source records attributed to Changyu Hu.

7 recordsLinked to original sources

arXiv2025

TopoGaussian: Inferring Internal Topology Structures from Visual Clues

We present TopoGaussian, a holistic, particle-based pipeline for inferring the interior structure of an opaque object from easily accessible photos and videos as input. Traditional mesh-based approaches require tedious and error-prone mesh filling and fixing process, while typically output rough boundary surface. Our pipeline combines Gaussian Splatting with a novel, versatile particle-based differentiable simulator that simultaneously accommodates constitutive model, actuator, and collision, without interference with mesh. Based on the gradients from this simulator, we provide flexible choice of topology representation for optimization, including particle, neural implicit surface, and quadratic surface. The resultant pipeline takes easily accessible photos and videos as input and outputs the topology that matches the physical characteristics of the input. We demonstrate the efficacy of our pipeline on a synthetic dataset and four real-world tasks with 3D-printed prototypes. Compared with existing mesh-based method, our pipeline is 5.26x faster on average with improved shape quality. These results highlight the potential of our pipeline in 3D vision, soft robotics, and manufacturing applications.

cs.CV↗

arXiv2019

Semiconducting Metal Oxide Photonic Crystal Plasmonic Photocatalysts

Plasmonic photocatalysis has facilitated rapid progress in enhancing photocatalytic efficiency under visible light irradiation. Poor visible-light-responsive photocatalytic materials and low photocatalytic efficiency remain major challenges. Plasmonic metal-semiconductor heterostructures where both the metal and semiconductor are photosensitive are promising for light harvesting catalysis, as both components can absorb solar light. Efficiency of photon capture can be further improved by structuring the catalyst as a photonic crystal. Here we report the synthesis of photonic crystal plasmonic photocatalyst materials using Au nanoparticle-functionalized inverse opal (IO) photonic crystals. A catalyst prepared using a visible light responsive semiconductor (V2O5) displayed over an order of magnitude increase in reaction rate under green light excitation ($λ$=532 nm) compared to no illumination. The superior performance of Au-V2O5 IO was attributed to spectral overlap of the electronic band gap, localized surface plasmon resonance and incident light source. Comparing the photocatalytic performance of Au-V2O5 IO with a conventional Au-TiO2 IO catalyst, where the semiconductor band gap is in the UV, revealed that optimal photocatalytic activity is observed under different illumination conditions depending on the nature of the semiconductor. For the Au-TiO2 catalyst, despite coupling of the LSPR and excitation source at $λ$=532 nm, this was not as effective in enhancing photocatalytic activity compared to carrying out the reaction under broadband visible light, which is attributed to improved photon adsorption in the visible by the presence of a photonic band gap, and exploiting slow light in the photonic crystal to enhance photon absorption to create this synergistic type of photocatalyst.

physics.app-ph↗

arXiv2019

Filling in the gaps: The nature of light transmission through solvent-filled inverse opal photonic crystals

Understanding the nature of light transmission and the photonic bandgap in inverse opal photonic crystals is essential for linking their optical characteristics to any application. This is especially important when these structures are examined in liquids or solvents. We examined TiO2 and SnO2 IOs in a range of common solvents to solve the conflict between Bragg-Snell theory, optical and physical measurements by a comprehensive angle-resolved light transmission study coupled to microscopy examination of the IO structure. Tuning the position of the photonic bandgap and index contrast by solvent infiltration of each inverse opal requires a modification to the Bragg-Snell theory and the photonic crystal unit cell definition. We also demonstrate experimentally and theroetically that low fill factors are caused by less desne material infilling all interstitial vancancies in the opal template to form an IO. By also including an optical interference condition for inverse opals with an effective refractive index greater than its substrate, and an alternative internal refraction angle in the substrate, angle-resolved transmission spectra for inverse opals are now consistent with physical measurements. This work now allows an accurate correlation between the true response of an IO to the index contrast with a solvent, how an IO is infilled, and the directionality and bandwidth of the photonic bandgap. As control in functional photonic materials becomes more prevalent outside of optics and photonics, such as biosensing and energy storage, for example, a comprehensive and consistent correlation between photonic crystals structures and their primary optical signatures is a fundamental requirement for application.

physics.app-ph↗

arXiv2013

Experimental demonstration of near-infrared epsilon-near-zero multilayer metamaterial slabs

Near-infrared epsilon-near-zero (ENZ) metamaterial slabs based on silver-germanium (Ag-Ge) multilayers are experimentally demonstrated. Transmission, reflection and absorption spectra are characterized and used to determine the complex refractive indices and the effective permittivities of the ENZ metamaterial slabs, which match the results obtained from both the numerical simulations and the optical nonlocalities analysis. A rapid post-annealing process is used to reduce the collision frequency of silver and therefore decrease the optical absorption loss of multilayer metamaterial slabs. Furthermore, multilayer grating structures are studied to enhance the optical transmission and also tune the location of ENZ wavelength. The demonstrated near-infrared ENZ multilayer metamaterial slabs are important for realizing many exotic applications, such as phase front shaping and engineering of photonic density of states.

physics.optics↗

arXiv2004

Developing optimal nonlinear scoring function for protein design

Motivation. Protein design aims to identify sequences compatible with a given protein fold but incompatible to any alternative folds. To select the correct sequences and to guide the search process, a design scoring function is critically important. Such a scoring function should be able to characterize the global fitness landscape of many proteins simultaneously. Results. To find optimal design scoring functions, we introduce two geometric views and propose a formulation using mixture of nonlinear Gaussian kernel functions. We aim to solve a simplified protein sequence design problem. Our goal is to distinguish each native sequence for a major portion of representative protein structures from a large number of alternative decoy sequences, each a fragment from proteins of different fold. Our scoring function discriminate perfectly a set of 440 native proteins from 14 million sequence decoys. We show that no linear scoring function can succeed in this task. In a blind test of unrelated proteins, our scoring function misclassfies only 13 native proteins out of 194. This compares favorably with about 3-4 times more misclassifications when optimal linear functions reported in literature are used. We also discuss how to develop protein folding scoring function.

q-bio.BM↗

arXiv2003

Simplicial edge representation of protein structures and alpha contact potential with confidence measure

Protein representation and potential function are essential ingredients for studying proteins folding and protein prediction. We introduce a novel geometric representation of contact interactions using the edge simplices from alpha shape of protein structure. This representation can eliminate implausible neighbors not in physical contact, and can avoid spurious contact between two residues when a third residue is between them. We develop statistical alpha contact potential. A studentized bootstrap method is then introduced for assessing the 95% confidence intervals for each of the 210 parameters. We found with confidence that there is significant long range propensity (>30 residues apart) for hydrophobic interactions. We test alpha contact potential for native structure discrimination using several decoy sets, and found it often has comparable performance with atom-based potentials requiring more parameters. We also show that alpha contact potential has better performance than potential defined by cut-off distance between geometric centers of side chains. Clustering of alpha contact potentials reveals natural grouping of residues. To explore the relationship between shape representation and physicochemical representation, we test the minimum alphabet size for structure discrimination. We found that there is no significant difference in discrimination when alphabet size varies from 7 to 20, if geometry is represented accurately by alpha simplicial edges. This result suggests that the geometry of packing plays an important role, but the specific residue types are often interchangeable.

physics.bio-ph↗

arXiv2003

On Design of Optimal Nonlinear Kernel Potential Function for Protein Folding and Protein Design

Potential functions are critical for computational studies of protein structure prediction, folding, and sequence design. A class of widely used potentials for coarse grained models of proteins are contact potentials in the form of weighted linear sum of pairwise contacts. However, these potentials have been shown to be unsuitable choices because they cannot stabilize native proteins against a large number of decoys generated by gapless threading. We develop an alternative framework for designing protein potential. We describe how finding optimal protein potential can be understood from two geometric viewpoints, and we derive nonlinear potentials using mixture of Gaussian kernel functions for folding and design. The optimization criterion for obtaining parameters of the potential is to minimize bounds on the generalization error of discriminating protein structures and decoys not used in training. In our experiment we use a training set of 440 protein structures repre senting a major portion of all known protein structures, and about 14 million structure decoys and sequence decoys obtained by gapless threading. We succeeded in obtaining nonlinear potential with perfect discrimination of the 440 native structures and native sequences. For the more challenging task of sequence design when decoys are obtained by gapless threading, we show that there is no linear potential with perfect discrimination of all 440 native sequences. Results on an independent test set of 194 proteins also showed that nonlinear kernel potential performs well.

cond-mat.soft↗