Multi-objective optimisation: algorithms and application to computer-aided molecular and process design

Computer-Aided Molecular Design (CAMD) has been put forward as a powerful and systematic technique that can accelerate the identification of new candidate molecules. Given the benefits of CAMD, the concept has been extended to integrated molecular and process design, usually referred to as Computer-Aided Molecular and Process Design (CAMPD). In CAMPD approaches, not only is the interdependence between the properties of the molecules and the process performance captured, but it is also possible to assess the optimal overall performance of a given fluid using an objective function that may be based on process economics, energy efficiency, or environmental criteria. Despite the significant advances made in the field of CAM(P)D, there are remaining challenges in handling the complexities arising from the large mixed-integer nonlinear structure-property and process models and the presence of conflicting performance criteria that cannot be easily merged into a single metric. Many of the algorithms proposed to date, however, resort to single-objective decomposition-based approaches. To overcome these challenges, a novel CAMPD optimisation framework is proposed, in the first part of thesis, in the context of identifying optimal amine solvents for carbon dioxide (CO2) chemical absorption. This requires development and validation of a model that enables the prediction of process performance metrics for a wide range of solvents for which no experimental data exist. An equilibrium-stage model that incorporates the SAFT-γ Mie group contribution approach is proposed to provide an appropriate balance between accuracy and predictive capability with varying molecular design spaces. In order to facilitate the convergence behaviour of the process-molecular model, a tailored initialisation strategy is established based on the inside-out algorithm. Novel feasibility tests that are capable of recognising infeasible regions of molecular and process domains are developed and incorporated into an outer-approximation framework to increase solution robustness. The efficiency of the proposed algorithm is demonstrated by applying it to the design of CO2 chemical absorption processes. The algorithm is found to converge successfully in all 150 runs carried out. To derive greater insights into the interplay between solvent and process performance, it is desirable to consider multiple objectives. In the second part of the thesis, we thus explore the relative performance of five multi-objective optimisations (MOO) solution techniques, modified from the literature to address nonconvex MINLPs, on CAM(P)D problems to gain a better understanding of the performance of different algorithms in identifying the Pareto front efficiently. The combination of the sandwich algorithm with a multi-level single-linkage algorithm to solve nonconvex subproblems is found to perform best on average. Next, a robust algorithm for bi-objective optimisation (BOO), the SDNBI algorithm, is designed to address the theoretical and numerical challenges associated with the solution of general nonconvex and discrete BOO problems. The main improvements in the development of the algorithm are focused on the effective exploration of the nonconvex regions of the Pareto front and the early identification of regions where no additional Pareto solutions exist. The performance of the algorithm is compared to that of the sandwich algorithm and the modified normal boundary intersection method (mNBI) over a set of literature benchmark problems and molecular design problems. The SDNBI found to provide the most evenly distributed approximation of the Pareto front as well as useful information on regions of the objective space that do not contain a nondominated point. The advances in this thesis can accelerate the discovery of novel solvents for CO2 capture that can achieve improved process performance. More broadly, the modelling and algorithmic development presented extend the applicability of CAMPD and MOO based CAMD/CAMPD to a wider range of applications.Open Acces

A hydro/oxo-phobic top hole-selective layer for efficient and stable colloidal quantum dot solar cells

In this report, we explore the underlying mechanisms by which doped organic thin films as a top hole-selective layer (HSL) improve the performance and stability of colloidal quantum dot (CQD)-based solar cells. Molecular dynamics-based theoretical studies prove that the hydro/oxo-phobic properties of the HSL serve to efficiently passivate the CQD solid. Furthermore, the robust and outstanding electrical properties of the HSL, simultaneously ensure a high power conversion efficiency (PCE) and increase the stability performance of CQD-based solar cells. As a result, a best PCE of 11.7% in a lead sulfide (PbS)-based CQD solar cell is achieved and over 90% of the initial performance is retained after 1 year storage under ambient conditions

A hydro/oxo-phobic top hole-selective layer for efficient and stable colloidal quantum dot solar cells

In this report, we explore the underlying mechanisms by which doped organic thin films as a top hole-selective layer (HSL) improve the performance and stability of colloidal quantum dot (CQD)-based solar cells. Molecular dynamics-based theoretical studies prove that the hydro/oxo-phobic properties of the HSL serve to efficiently passivate the CQD solid. Furthermore, the robust and outstanding electrical properties of the HSL, simultaneously ensure a high power conversion efficiency (PCE) and increase the stability performance of CQD-based solar cells. As a result, a best PCE of 11.7% in a lead sulfide (PbS)-based CQD solar cell is achieved and over 90% of the initial performance is retained after 1 year storage under ambient conditions

The Effect of Secretory Factors of Adipose-Derived Stem Cells on Human Keratinocytes

The beneficial effects of adipose-derived stem cell conditioned medium (ADSC-CM) on skin regeneration have been reported. Although the mechanism of how ADSC-CM promotes skin regeneration is unclear, ADSC-CM contained various growth factors and it is an excellent raw material for skin treatment. ADSC-CM produced in a hypoxia condition of ADSC—in other words, Advanced Adipose-Derived Stem cell Protein Extract (AAPE)—has great merits for skin regeneration. In this study, human primary keratinocytes (HKs), which play fundamental roles in skin tissue, was used to examine how AAPE affects HK. HK proliferation was significantly higher in the experimental group (1.22 μg/mL) than in the control group. DNA gene chip demonstrated that AAPE in keratinocytes (p < 0.05) notably affected expression of 290 identified transcripts, which were associated with cell proliferation, cycle and migration. More keratinocyte wound healing and migration was shown in the experimental group (1.22 μg/mL). AAPE treatment significantly stimulated stress fiber formation, which was linked to the RhoA-ROCK pathway. We identified 48 protein spots in 2-D gel analysis and selected proteins were divided into 64% collagen components and 30% non-collagen components as shown by the MALDI-TOF analysis. Antibody array results contained growth factor/cytokine such as HGF, FGF-1, G-CSF, GM-CSF, IL-6, VEGF, and TGF-β3 differing from that shown by 2-D analysis. Conclusion: AAPE activates HK proliferation and migration. These results highlight the potential of the topical application of AAPE in the treatment of skin regeneration

Bioinspired Designs and Biomimetic Applications of Triboelectric Nanogenerators

The emerging novel power generation technology of triboelectric nanogenerators (TENGs) is attracting increasing attention due to its unlimited prospects in energy harvesting and self-powered sensing applications. The most important factors that determine TENGs’ electrical and mechanical performance include the device structure, surface morphology and the type of triboelectric material employed, all of which have been investigated in the past to optimize and enhance the performance of TENG devices. Amongst them, bioinspired designs, which mimic structures, surface morphologies, material properties and sensing/power generation mechanisms from nature, have largely benefited in terms of enhanced performance of TENGs. In addition, a variety of biomimetic applications based on TENGs have been explored due to the simple structure, self-powered property and tunable output of TENGs. In this review article, we present a comprehensive review of various researches within the specific focus of bioinspired TENGs and TENG enabled biomimetic applications. The review begins with a summary of the various bioinspired TENGs developed in the past with a comparative analysis of the various device structures, surface morphologies and materials inspired from nature and the resultant improvement in the TENG performance. Various ubiquitous sensing principles and power generation mechanisms in use in nature and their analogous artificial TENG designs are corroborated. TENG-enabled biomimetic applications in artificial electronic skins and neuromorphic devices are discussed. The paper concludes by providing a perspective towards promising directions for future research in this burgeoning field of study