Precisely Controllable Synthesized Nanoparticles for Surface Enhanced Raman Spectroscopy

Surface-enhanced Raman scattering (SERS) is a powerful technique for trace molecular detection because of its ultrahigh molecular structure sensitivity and unique fingerprinting spectra. The morphology, size and structure of the plasmonic nanoparticles seriously influence the Raman scattering intensity of sample. In this chapter, we focus on the influence of nanoparticle morphology. By tailoring the plasmonic properties of anisotropic Au, Ag nanoparticles and generating electromagnetic “hot spots” of SERS active substrate, the SERS intensity can be seriously influenced. We also focus on providing a general introduction to understand the main parameters of anisotropic noble metal nanoparticles of SERS performance

Noble Metal-Based Nanocomposites for Fuel Cells

Noble metal-based nanocomposites are attractive for a rich variety of electrocatalytic applications as they can exhibit not only a combination of the properties associated with each component but also synergy due to a strong coupling between different constituents. Using noble metal as the base component, a plenty of methods have been recently demonstrated for the synthesis of noble metal-based nanocomposites with novel structures (e.g., alloys, core-shell, skin and 1D/2D structures). In this chapter, an account of recent advances of synthetic approaches to noble metal-based nanocomposites with controlled structures, compositions and sizes are reviewed. The relationship between structures and electrochemical properties of these nanocomposites in fuel cell field is discussed. The potential future directions of research in the field are also addressed

Biomarkers for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the third leading cause of cancer deaths worldwide. The HCC diagnosis is usually achieved by biomarkers, which can also help in prognosis prediction. Furthermore, it might represent certain therapeutic interventions through some combinations of biomarkers. Here, we review on our current understanding of HCC biomarkers

Metal/Semiconductor Hybrid Nanocrystals and Synergistic Photocatalysis Applications

This review focuses on recent research efforts to synthesize metal/semiconductor hybrid nanocrystals, understand and control the photocatalytic applications. First, we summarize the synthesis methods and recent presented metal/seminconductor morphologies, including heterodimer, core/shell, and yolk/shell etc. The metal clusters and nanocrystals deposition on semiconductor micro/nano substrates with well-defined crystal face exposure will be clarified into heterodimer part. The outline of this synthesis part will be the large lattice mismatch directed interface, contact and morphologies evolution. For detailed instructions on each synthesis, the readers are referred to the corresponding literature. Secondly, the recent upcoming photocatalysis applications and research progress of these hybrid nanocrystals will be reviewed, including the photocatalytic hydrogen evolution (water splitting), photo-reduction of CO2 and other newly emerging potential photosynthesis applications of metal/semiconductor hybrid nanocrystals. Finally, we summarize and outlook the future of this topic. From this review, we try to facilitate the understanding and further improvement of current and practical metal/semiconductor hybrid nanocrystals and photocatalysis applications

Matryoshka Diffusion Models

Diffusion models are the de facto approach for generating high-quality images and videos, but learning high-dimensional models remains a formidable task due to computational and optimization challenges. Existing methods often resort to training cascaded models in pixel space or using a downsampled latent space of a separately trained auto-encoder. In this paper, we introduce Matryoshka Diffusion Models(MDM), an end-to-end framework for high-resolution image and video synthesis. We propose a diffusion process that denoises inputs at multiple resolutions jointly and uses a NestedUNet architecture where features and parameters for small-scale inputs are nested within those of large scales. In addition, MDM enables a progressive training schedule from lower to higher resolutions, which leads to significant improvements in optimization for high-resolution generation. We demonstrate the effectiveness of our approach on various benchmarks, including class-conditioned image generation, high-resolution text-to-image, and text-to-video applications. Remarkably, we can train a single pixel-space model at resolutions of up to 1024x1024 pixels, demonstrating strong zero-shot generalization using the CC12M dataset, which contains only 12 million images.Comment: 28 pages, 18 figure

BOOT: Data-free Distillation of Denoising Diffusion Models with Bootstrapping

Diffusion models have demonstrated excellent potential for generating diverse images. However, their performance often suffers from slow generation due to iterative denoising. Knowledge distillation has been recently proposed as a remedy that can reduce the number of inference steps to one or a few without significant quality degradation. However, existing distillation methods either require significant amounts of offline computation for generating synthetic training data from the teacher model or need to perform expensive online learning with the help of real data. In this work, we present a novel technique called BOOT, that overcomes these limitations with an efficient data-free distillation algorithm. The core idea is to learn a time-conditioned model that predicts the output of a pre-trained diffusion model teacher given any time step. Such a model can be efficiently trained based on bootstrapping from two consecutive sampled steps. Furthermore, our method can be easily adapted to large-scale text-to-image diffusion models, which are challenging for conventional methods given the fact that the training sets are often large and difficult to access. We demonstrate the effectiveness of our approach on several benchmark datasets in the DDIM setting, achieving comparable generation quality while being orders of magnitude faster than the diffusion teacher. The text-to-image results show that the proposed approach is able to handle highly complex distributions, shedding light on more efficient generative modeling.Comment: In progres

Interfacial regulation of aqueous synthesized metal-semiconductor hetero-nanocrystals

Integrating metal and semiconductor components to form metal-semiconductor heterostructures is an attractive strategy to develop nanomaterials for optoelectronic applications, and the rational regulation of their heterointerfaces could effectively influence their charge transfer properties and further determine their performance. Considering the natural large lattice mismatch between metal and semiconductor components, defects and low crystalline heterointerfaces could be easily generated especially for heterostructures with large contacting areas such as core-shell and over quantum-sized nanostructures. The defective interfaces of heterostructures could lead to the undesirable recombination of photo-induced electrons and holes, which would decrease their performances. Based on these issues, the perspective focusing on the most recent progress in the aqueous synthesis of metal-semiconductor heterostructures with emphasis on heterointerface regulation is proposed, especially in the aspect of non-epitaxial growth strategies initiated by cation exchange reaction (CER). The enhanced optoelectronic performance enabled by precise interfacial regulations is also illustrated. We hope this perspective could provide meaningful insights for researchers on nano synthesis and optoelectronic applications