Synthesis of different sizes & functions nanoparticles

Nanogels, whose size range from 1 to 100nm, have been interested in many research areas: cosmetics, pharmaceuticals, catalysts, photochemistry, and in optical switches or sensors. In Petroleum Engineering area, nanogels can be used as conformance control agent and emulsion stabilizer. And after grafting functional groups or hybrid, nanogels can be used as tracer for the visual modeling. Though nanoparticles have been studied for more than 20 years, few of them are about nanogels. In the thesis, stirring rate, surfactant type and concentration were found have a large impact on the synthesis of nanogels. And cationic nanogels have salt and acid resistant properties. In the thesis, the most used methods for synthesizing small size particles is reviewed. The experiments section covers three parts: a) microemulsion preparation, b) cationic nanoparticles synthesis and evaluation, c) nanoparticle size control. In microemulsion preparation part, optimum surfactants ratio of Span80 to Tween60 was given. In cationic nanoparticles synthesis and evaluation part, nanoparticles of different cationic degree were synthesized via suspension polymerization. And after introducing cationic groups to it, nanoparticles can have acid and salt resistant properties. In size control part, stirring rate, type and concentration of surfactants all affect the morphology and size of nanogels --Abstract, page iii

Synthesis and comprehensive studies of nanogels for enhanced oil recovery

“Nanomaterials have been developed and applied for enhanced oil recovery (EOR) in oil industry. Among the developed nanomaterials, nano-sized crosslinked polymeric gel particles (nanogels) have shown the potential in improving both the macro- and micro-displacing efficiency. This dissertation focuses on the synthesis and the potential EOR mechanisms of nanogels. Nanogels with positive, negative, and neutral charges were synthesized through suspension polymerization. The morphology, size distribution, and zeta potential of nanogels were elucidated by corresponding technologies, such as scanning electron microscopy and dynamic light scattering. The ability of nanogels in oil-water interfacial tension (IFT) reduction and oil-in-water (o/w) emulsion stabilization was studied at various nanogel concentrations, temperatures, and salinities. Moreover, nanogels showed an electro-interaction controlled adsorption on the rock surface, which could modify the wettability of rock surface and reduce the permeability of porous medium. The core flooding results demonstrate that nanogels with proper charges can easily transport through the porous medium and reduce the water permeability by forming multilayer adsorption on the rock surface. The residual oil was produced in o/w emulsion state during the nanogel flooding and in isolated oil drops during the post water flooding, which indicated the abilities of nanogels in increase the displacing efficiency and sweeping efficiency, respectively”--Abstract, page iv

Dual Crosslinked Poly(acrylamide-co-N-vinylpyrrolidone) Microspheres With Re-crosslinking Ability For Fossil Energy Recovery

Microspheres have been proposed to be applied in controlling wastewater production for mature oilfields and migrating leakage for gas and nuclear waste storage. However, it remains challenging for stacked microspheres to maintain strong blocking ability in micron-sized small pores or fractures. In this study, a novel microsphere was developed with comprehensive properties including high deformability and long re-crosslinking time upon tunable swelling ratio for the applications. A dual covalent and physical crosslinking strategy was used to develop novel microspheres reinforced by a hydrogen bond (H-bond, between pyrrole ring and amide group) and coordination bond (between chromium acetate (CrAc) and carboxyl group via hydrolysis process). The microspheres were fabricated via radical suspension copolymerization of acrylamide (AM) and N-vinylpyrrolidone (NVP) in the presence of N, Nʹ-methylene-diacrylamide (MBA) with subsequent introduction of CrAc. MBA induced the strong crosslinking through a chemical covalent bond and H-bond triggered the weak crosslinking which was anticipated to prohibit the hydrolysis of the amide group. The H-bond delayed the formation of CrAc coordination bond by delaying the formation of carboxyl groups, resulting in achieving the re-crosslinking of the microspheres. As a result, the microspheres exhibit the tunable initial size (8–165 μm) and swelling ratio (30–630 μm), with controllable network parameters. The microspheres showed high migration ability (can transport through pores with 1/16 size of microsphere itself), and long re-crosslinking time (up to 16.5 days). The re-crosslinked gel demonstrated dual network structure with districted mesh size ζ distribution

Relightable and Animatable Neural Avatar from Sparse-View Video

This paper tackles the challenge of creating relightable and animatable neural avatars from sparse-view (or even monocular) videos of dynamic humans under unknown illumination. Compared to studio environments, this setting is more practical and accessible but poses an extremely challenging ill-posed problem. Previous neural human reconstruction methods are able to reconstruct animatable avatars from sparse views using deformed Signed Distance Fields (SDF) but cannot recover material parameters for relighting. While differentiable inverse rendering-based methods have succeeded in material recovery of static objects, it is not straightforward to extend them to dynamic humans as it is computationally intensive to compute pixel-surface intersection and light visibility on deformed SDFs for inverse rendering. To solve this challenge, we propose a Hierarchical Distance Query (HDQ) algorithm to approximate the world space distances under arbitrary human poses. Specifically, we estimate coarse distances based on a parametric human model and compute fine distances by exploiting the local deformation invariance of SDF. Based on the HDQ algorithm, we leverage sphere tracing to efficiently estimate the surface intersection and light visibility. This allows us to develop the first system to recover animatable and relightable neural avatars from sparse view (or monocular) inputs. Experiments demonstrate that our approach is able to produce superior results compared to state-of-the-art methods. Our code will be released for reproducibility.Comment: Project page: https://zju3dv.github.io/relightable_avata

LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model

How to efficiently transform large language models (LLMs) into instruction followers is recently a popular research direction, while training LLM for multi-modal reasoning remains less explored. Although the recent LLaMA-Adapter demonstrates the potential to handle visual inputs with LLMs, it still cannot generalize well to open-ended visual instructions and lags behind GPT-4. In this paper, we present LLaMA-Adapter V2, a parameter-efficient visual instruction model. Specifically, we first augment LLaMA-Adapter by unlocking more learnable parameters (e.g., norm, bias and scale), which distribute the instruction-following ability across the entire LLaMA model besides adapters. Secondly, we propose an early fusion strategy to feed visual tokens only into the early LLM layers, contributing to better visual knowledge incorporation. Thirdly, a joint training paradigm of image-text pairs and instruction-following data is introduced by optimizing disjoint groups of learnable parameters. This strategy effectively alleviates the interference between the two tasks of image-text alignment and instruction following and achieves strong multi-modal reasoning with only a small-scale image-text and instruction dataset. During inference, we incorporate additional expert models (e.g. captioning/OCR systems) into LLaMA-Adapter to further enhance its image understanding capability without incurring training costs. Compared to the original LLaMA-Adapter, our LLaMA-Adapter V2 can perform open-ended multi-modal instructions by merely introducing 14M parameters over LLaMA. The newly designed framework also exhibits stronger language-only instruction-following capabilities and even excels in chat interactions. Our code and models are available at https://github.com/ZrrSkywalker/LLaMA-Adapter.Comment: Code and models are available at https://github.com/ZrrSkywalker/LLaMA-Adapte