519 research outputs found
Amphiphilic block copolymers : synthesis, self-assembly and applications
No full textSelf-assembly of amphiphilic block copolymers in aqueous solution is one of the most important nanotechnological methods to prepare nanocarriers for
different applications, such as drug delivery, biosensor, nanoreactor and so on. Synthesis of new types of amphiphilic block copolymers with novel
functionality and detailed characterization of self-assembly, influenced by self-assembly methods and different other parameters (molecular weight,
hydrophilic to hydrophobic ratio), are important. Especially, building up the relationship between the self-assembled nanomorphologies and molecule
constitution are helpful to understand amphiphilic block copolymer self-assemble theroy.
In this thesis, I present to you the influence of different parameters on the self-assembly nanostructures for the poly(dimethylsiloxane)-
block-poly(2-methyl-2-oxazoline) (PDMS-b-PMOXA) amphiphilic block copolymers.3D phase diagram clearly shows in which domain the PDMS-b-PMOXA
self-assemble into polymersome. The polymersome are possible for us to constribute the nano-sized based nanoreactor.
In addition, in order to develop more functional amphiphilic block copolymers and enlarge the potential application areas, another two types of
copolymers, grafted poly(2-methyl-2-oxazoline)-graft(ss)-poly(e-caprolactone) (PMOXA-graft(ss)-PCL) and linear poly(2-ethyl-2-oxazoline)-block-
poly(e-caprolactone)-ss-poly(L-lysine) (PEtOXA-b-PCL-ss-PLL), were designed and synthesized with reduction responsiveness, utilizing different
polymerization techniques, including ring openning polymerization and "graft-to" technology. Due to the amphiphilicity of these two types of
copolymers, nanoparticles are formed by them in aqueous solution. The primary evaluation of these two new type amphiphilic block copolymers
demonstrated that they can be promising candidates as smart nanocarries for the application of drug delivery.
In this dissertation, the result of our research have been comprehensivly compared with other publications and results from different groups.
We have new findingS. We find one new nano-object with 80-100 nm diameter, but without hollow aqueous cavity.
We also realize that basing on poly(2-ethyl-2-oxazoline)-block-poly(e-caprolactone)-ss-poly(L-lysine) (PEtOXA-b-PCL-ss-PLL) copolymer, it is possible
to synthesize more functional copolymer, for example introducing the pH-cleavable linker between PEtOXA and PCL, to mimic more closely the virus delivery
gene into cells
Curvature-Based Sparse Rule Base Generation for Fuzzy Interpolation Using Menger Curvature
No full textFuzzy interpolation improves the applicability of fuzzy inference by allowing the utilisation of sparse rule bases. Curvature-based rule base generation approach has been recently proposed to support fuzzy interpolation. Despite the ability to directly generating sparse rule bases from data, the approach often suffers from the high dimensionality of complex inference problems. In this work, a different curvature calculation approach, i.e., the Menger approach, is employed to the curvature-based rule base generation approach in an effort to address the limitation. The experimental results confirm better efficiency and efficacy of the proposed method in generating rule bases on high-dimensional datasets.</p
Photocatalytic and Electrochemical Synthesis of Biofuel via Efficient Valorization of Biomass
No full textAbstract The excessive use of fossil fuels has significantly increased environmental stress, driving the need for green, sustainable biofuel alternatives. Innovations in photocatalysis (PC), electrocatalysis (EC), and their synergistic approaches, like photothermal catalysis (PTC), photo‐enzymatic catalysis (PENC), and photoelectrocatalysis (PEC), offer advanced methods for biomass conversion into biofuels, surpassing traditional limitations. However, comprehensive research on these conversion processes is still lacking. This review aims to systematically analyze recent progress in catalytic strategies for biomass‐to‐biofuel conversion. It first describes the characteristics, types, and properties of biomass and biofuels. Then, it explores the fundamental mechanisms of PC, EC, and combined catalytic technologies. The chemical pathways involved in conversion—such as transesterification, esterification, hydrogenation, decarboxylation, bond cleavage, and cyclization—are examined. Efficient catalyst design for specific reactions and factors influencing catalyst efficiency and conversion rates are also discussed. Additionally, this paper assesses the environmental impact and economic benefits of green catalytic technology in biofuel production, offering a valuable reference for biomass energy research and application. It addresses challenges in technology deployment for biofuel production and suggests future research directions, aiming to provide scientific guidance and technical support for the development of this vital field. In summary, this review underscores the importance of continued innovation and research in catalytic biomass conversion to promote sustainable biofuel solutions.National Natural Science Foundation of China https://doi.org/10.13039/501100001809National Key Research and Development Program of China https://doi.org/10.13039/501100012166China Scholarship Council https://doi.org/10.13039/50110000454
Geometric Message Passing to utilize local and non-local information in 3D Graph Networks
No full textTo revolutionize computational chemistry within simulating and analysing molecular systems. A principled framework that captures the relative 3D information and long-range interactions is needed. In this work, we propose a generic framework to capture these interactions, known as the deterministic point graph network (DPGN). It provides a unified interface to interact with 3D graphs on different levels of bonds, angles, torsional and long-range effects. We then leverage this framework to add multiple structural improvements to propose the geometric message passing scheme (GMP) to realize DPGN. We demonstrate the benefit of the proposed changes in ablation studies. Finally, we validate this model by presenting results beating the predecessor MPNet on six properties on the QM9 data set, whereas one of them is state-of-art. Additionally, we demonstrate the models' ability within the field of molecular dynamics, where it beats state of the art on of the targets on in MD17 data set
CleanNav: Dirt Detection and Depth Prediction with Multi-Task Learning and Multi-View Learning
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L<sub>1</sub>-Induced Static Output Feedback Controller Design and Stability Analysis for Positive Polynomial Fuzzy Systems
No full textThe aim of this paper is to study the control synthesis and stability and positivity analysis under L1-induced performance for positive systems based on a polynomial fuzzy model. In this paper, not only the stability and positivity analysis are studied but also the L1-induced performance is ensured by designing a static output feedback polynomial fuzzy controller for the positive polynomial fuzzy (PPF) system. In order to improve the flexibility of controller implementation, imperfectly matched premise concept under membership-function-dependent analysis technique is introduced. In addition, although the static output feedback control strategy is more popular when the system states are not completely measurable, a tricky problem that non-convex terms exist in stability and positivity conditions will follow. The nonsingular transformation technique which can transform the non-convex terms into convex ones successfully plays an important role to solve this puzzle. Based on Lyapunov stability theory, the convex positivity and stability conditions in terms of sum of squares (SOS) are obtained, which can guarantee the closed-loop systems to be positive and asymptotically stable under the L1-induced performance. Finally, in order to test the effectiveness of the derived theory, we show an example in the simulation section.</p
Swarm Inspired Approaches for K-prototypes clustering
No full textData clustering is a well-researched area in data mining and machine learning. The clustering algorithms that can handle both numeric and categorical variables have been extensively researched in the recent years. However, the clustering algorithms have a major limitation that converge to a local optima. Therefore, to address this problem this paper has proposed a novel algorithm ABC k-prototypes (Artificial Bee Colony clustering based on k-prototypes) for clustering mixed data. In our proposed approach we use the combination between the distribution centroid and the mean to calculate the dissimilarity between data objects and prototypes. The proposed algorithm is tested on five different datasets taken from the UCI machine learning data repository. The comparative results in the performance measures of the clustering showed that the proposed algorithm outperformed the traditional k-prototypes
Improving imbalanced students’ text feedback classification using re-sampling based approach
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