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Colloidal semiconductor nanocrystals for colour conversion and self-assembled lasers
No full textNanocrystals have been at the forefront of technological developments in photonics thanks to their unique optoelectronic properties. In this thesis, different types of luminescent semiconductor nanocrystals have been studied, improved, and implemented towards novel applications. The topics of research discussed are: (i) II-V colloidal quantum dots as the building blocks of novel lasers via self-assembly from the bottom up (the fabrication and study of these are in fact the main focus of the thesis), and (ii) a perovskite quantum dot-based structure as a robust colour-converter of LEDs.(i) II-VI alloyed core-shell CdxS1-xSe/ZnS quantum dots are nanosized colloids (dispersed in solution) with excellent optical properties in the visible spectrum. These Cd-based colloidal quantum dots represent the most mature colloidal quantum dot technology and their use as light emitters and laser gain material is being intensely pursued. To date, colloidal quantum dot (and related nanocrystal) lasers have been made from the top down with the quantum dots deposited into an optical resonator fabricated separately. Departing from this standard approach and fully capitalizing on the solution processability of colloids, this work uses quantum dots as nanobricks to create supracrystal/supraparticle microspheres that self-assemble from a bottom-up approach. These supraparticles act simultaneously as the gain material and the optical microcavity. In addition to that they are capable of laser emission under optical excitation. Using red-emitting CdxS1-xSe/ZnS quantum dots, laser oscillation between the 625 and the 655 nm is obtained from single quantum dot spheres with diameters of 5.6 ± 3.2 μm and energy threshold of 4.7 ± 2.1 nJ for a 532 nm pump source with a beam spot size of approximately 6 μm in diameter.The possibility of making hetero-supraparticles by selecting and self-assembling together quantum dots with different emission and absorption spectra is also demonstrated. When carefully selected, these combinations can enhance the quality factor of the sphere. As an example, microspheres of red quantum dots and green quantum dots had an increase in the quality factor from 135±19 to 340±60, when compared to red quantum dot microspheres of the same size (6.0 ± 0.5 μm in diameter). Microspheres with quantum dots of red and higher band gap species also maintain similar laser threshold energies to their red quantum dot microsphere counterparts. In the example mentioned above, both microspheres reached laser threshold at 12 – 14 mJ.cm-2, for a beam spot size of 2.88×10-7 cm2. In addition to that, microspheres with higher bandgap quantum dots in their composition have also reported laser for cavities of sizes 3 – 4 times bigger, further suggesting that the increase in the quality factor and decrease in self-absorption is promoted by the addition of higher bandgap quantum dots.Synthesis of microspheres with different bandgap quantum dots also allow for simultaneous multicolor lasing in a single sphere. Stable dual laser emission of yellow (575 nm) and red (630 nm) is shown in a microsphere of 6.0 ± 0.5 μm in diameter, for energy thresholds between 13.3 and 45.6 nJ and for a spot size of approximately 4.85 × 10-7 cm2.The integration of supraparticle lasers to other devices is demonstrated via transfer printing. This method can move them reliably between substrates, and this was done to successfully couple them to waveguides. This demonstration paves the way to more complex designs and applications in integrated photonics.In addition to quantum dots, the self-assembly procedure was also tested and adapted to other types of semiconductor nanocrystals, including nanoplatelets and tetrapods, in a collaboration work with Nanyang Technological University (LUMINOUS! group).(ii) A different material was studied for colour conversion. Ceasium lead bromide perovskite nanocrystals have up to date the fastest luminescent dynamics of all known nanocrystals and are therefore appealing for light communication applications. However, they are not stable in the presence of heat and humidity. Different coatings using two different polymers (PDMS and PMMA) have been studied as a way of protecting and increasing the stability of CsPbBr3@Cs4PbBr6 crystals. While PDMS samples were not stable upon immersion in water, PMMA composites showed little to no trace of degradation when immersed in water under vigorous stirring for up to 72 hours. Bandwidth measurements with PMMA samples have given similar results to the current state of the art, showing that PMMA is an effective matrix host for CsPbBr3@Cs4PbBr6 against moisture and water.Nanocrystals have been at the forefront of technological developments in photonics thanks to their unique optoelectronic properties. In this thesis, different types of luminescent semiconductor nanocrystals have been studied, improved, and implemented towards novel applications. The topics of research discussed are: (i) II-V colloidal quantum dots as the building blocks of novel lasers via self-assembly from the bottom up (the fabrication and study of these are in fact the main focus of the thesis), and (ii) a perovskite quantum dot-based structure as a robust colour-converter of LEDs.(i) II-VI alloyed core-shell CdxS1-xSe/ZnS quantum dots are nanosized colloids (dispersed in solution) with excellent optical properties in the visible spectrum. These Cd-based colloidal quantum dots represent the most mature colloidal quantum dot technology and their use as light emitters and laser gain material is being intensely pursued. To date, colloidal quantum dot (and related nanocrystal) lasers have been made from the top down with the quantum dots deposited into an optical resonator fabricated separately. Departing from this standard approach and fully capitalizing on the solution processability of colloids, this work uses quantum dots as nanobricks to create supracrystal/supraparticle microspheres that self-assemble from a bottom-up approach. These supraparticles act simultaneously as the gain material and the optical microcavity. In addition to that they are capable of laser emission under optical excitation. Using red-emitting CdxS1-xSe/ZnS quantum dots, laser oscillation between the 625 and the 655 nm is obtained from single quantum dot spheres with diameters of 5.6 ± 3.2 μm and energy threshold of 4.7 ± 2.1 nJ for a 532 nm pump source with a beam spot size of approximately 6 μm in diameter.The possibility of making hetero-supraparticles by selecting and self-assembling together quantum dots with different emission and absorption spectra is also demonstrated. When carefully selected, these combinations can enhance the quality factor of the sphere. As an example, microspheres of red quantum dots and green quantum dots had an increase in the quality factor from 135±19 to 340±60, when compared to red quantum dot microspheres of the same size (6.0 ± 0.5 μm in diameter). Microspheres with quantum dots of red and higher band gap species also maintain similar laser threshold energies to their red quantum dot microsphere counterparts. In the example mentioned above, both microspheres reached laser threshold at 12 – 14 mJ.cm-2, for a beam spot size of 2.88×10-7 cm2. In addition to that, microspheres with higher bandgap quantum dots in their composition have also reported laser for cavities of sizes 3 – 4 times bigger, further suggesting that the increase in the quality factor and decrease in self-absorption is promoted by the addition of higher bandgap quantum dots.Synthesis of microspheres with different bandgap quantum dots also allow for simultaneous multicolor lasing in a single sphere. Stable dual laser emission of yellow (575 nm) and red (630 nm) is shown in a microsphere of 6.0 ± 0.5 μm in diameter, for energy thresholds between 13.3 and 45.6 nJ and for a spot size of approximately 4.85 × 10-7 cm2.The integration of supraparticle lasers to other devices is demonstrated via transfer printing. This method can move them reliably between substrates, and this was done to successfully couple them to waveguides. This demonstration paves the way to more complex designs and applications in integrated photonics.In addition to quantum dots, the self-assembly procedure was also tested and adapted to other types of semiconductor nanocrystals, including nanoplatelets and tetrapods, in a collaboration work with Nanyang Technological University (LUMINOUS! group).(ii) A different material was studied for colour conversion. Ceasium lead bromide perovskite nanocrystals have up to date the fastest luminescent dynamics of all known nanocrystals and are therefore appealing for light communication applications. However, they are not stable in the presence of heat and humidity. Different coatings using two different polymers (PDMS and PMMA) have been studied as a way of protecting and increasing the stability of CsPbBr3@Cs4PbBr6 crystals. While PDMS samples were not stable upon immersion in water, PMMA composites showed little to no trace of degradation when immersed in water under vigorous stirring for up to 72 hours. Bandwidth measurements with PMMA samples have given similar results to the current state of the art, showing that PMMA is an effective matrix host for CsPbBr3@Cs4PbBr6 against moisture and water
Three essays in migration economics
Get PDFThis thesis examines the impact of migration on economic development and native labour market outcomes. In three distinct essays, we make use of applied econometric methods to examine the consequences of forced out-migration on the development of townships in Hungary, the cross-occupational impact of immigration on UK native wages, and an in-depth review of common measurements of migration found in the literature. Our findings are intended to contribute to the wider migration policy debateusing historical and contemporary evidence.This thesis examines the impact of migration on economic development and native labour market outcomes. In three distinct essays, we make use of applied econometric methods to examine the consequences of forced out-migration on the development of townships in Hungary, the cross-occupational impact of immigration on UK native wages, and an in-depth review of common measurements of migration found in the literature. Our findings are intended to contribute to the wider migration policy debateusing historical and contemporary evidence
Bioinspired amphipilic polymer conetworks
No full textNature has developed materials with high toughness and strength, which is usually not possible in man-made materials. More recently bioinspiration has aided development of highly mechanically reinforced polymeric materials. Key properties of amphiphilic polymer conetworks (APCNs) include transparency and swellability in water and hydrocarbons. Unfortunately, the mechanical properties are not high enough to be used in a wide range of applications where the key properties of these materials could otherwise be beneficial. In this thesis, several strategiesto improve the mechanical properties of APCNs and extend their potential range of applications using different processing techniques are explored.Inspired by spider silk, we used triblock copolymers with peptidic repeating units, poly-β-benzyl-l-aspartate (PBLA), that form β-sheets and/or α-helices to tailor the properties of APCNs. The effect of varying the number of peptide repeating units and concentration of hydrophobic to hydrophilic domains were studied. Through this study, the created hydrogenbondings showed the possibility to tailor the properties of the material to different applications.As many natural structures use hierarchical reinforcement to provide greater resilience, a second level of reinforcement using cellulose nanocrystals (CNCs) was also studied. A type ofCNCs modified with hydrophobic moieties was explored to be used in the initial hydrophobic monomer mixture. The amount of solvent used during processing was studied as well as the reinforcement with up to 22 wt% CNCs. This work showed the possibility of a two-level hierarchical reinforcement of APCNs which gives the possibility to have high mechanical propertiesin the dry and swollen state. Finally, APCNs were used as a base material for the shell of double emulsion microcapsules prepared using LEGO® inspired glass capillary devices. Inspired by pollen grains, the hydration and dehydration of the capsules was studied to understand the suitability of these microcapsules for use in different media. The capsules were also loaded with platinum nanoparticles to study their catalytic effect when mixed with hydrogen peroxide. The surface functionalization of the capsules was studied by adding different moieties such as Cy5-PEG5000-cholesterol.This thesis systematically investigated the properties of APCNs, explored different enhancement strategies and developed a new type of APCNs, greatly enhancing the potential to tailor APCNs for a wider range of applications.Nature has developed materials with high toughness and strength, which is usually not possible in man-made materials. More recently bioinspiration has aided development of highly mechanically reinforced polymeric materials. Key properties of amphiphilic polymer conetworks (APCNs) include transparency and swellability in water and hydrocarbons. Unfortunately, the mechanical properties are not high enough to be used in a wide range of applications where the key properties of these materials could otherwise be beneficial. In this thesis, several strategiesto improve the mechanical properties of APCNs and extend their potential range of applications using different processing techniques are explored.Inspired by spider silk, we used triblock copolymers with peptidic repeating units, poly-β-benzyl-l-aspartate (PBLA), that form β-sheets and/or α-helices to tailor the properties of APCNs. The effect of varying the number of peptide repeating units and concentration of hydrophobic to hydrophilic domains were studied. Through this study, the created hydrogenbondings showed the possibility to tailor the properties of the material to different applications.As many natural structures use hierarchical reinforcement to provide greater resilience, a second level of reinforcement using cellulose nanocrystals (CNCs) was also studied. A type ofCNCs modified with hydrophobic moieties was explored to be used in the initial hydrophobic monomer mixture. The amount of solvent used during processing was studied as well as the reinforcement with up to 22 wt% CNCs. This work showed the possibility of a two-level hierarchical reinforcement of APCNs which gives the possibility to have high mechanical propertiesin the dry and swollen state. Finally, APCNs were used as a base material for the shell of double emulsion microcapsules prepared using LEGO® inspired glass capillary devices. Inspired by pollen grains, the hydration and dehydration of the capsules was studied to understand the suitability of these microcapsules for use in different media. The capsules were also loaded with platinum nanoparticles to study their catalytic effect when mixed with hydrogen peroxide. The surface functionalization of the capsules was studied by adding different moieties such as Cy5-PEG5000-cholesterol.This thesis systematically investigated the properties of APCNs, explored different enhancement strategies and developed a new type of APCNs, greatly enhancing the potential to tailor APCNs for a wider range of applications
Undertaking innovation despite constraints : the case of Scottish food SMEs
Get PDFWhile it has been recognised that SMEs are constrained in their ability to innovate, little is known about how they innovate despite their constraints. Based on 30 in-depth discussions with food SME participants, this thesis seeks to advance knowledge by developing a theory to explain how low-tech SMEs innovate despite constraints. This study focuses on the low-tech sector given its key contributing role to the economy.
The literature highlights three key themes that majorly influence innovation in low-tech
SMEs. These themes are limited resources, large dominant customers, and family business culture. As SMEs, they are limited with resources, deal with a network of large dominant customers, and operate under a family business culture. The findings demonstrate distinct yet interdependent strategies executed by managers such as leveraging external network support, pursuing customer-centric incremental innovation, optimising internal processes and resources; operationalising professional management, building a long-term orientation on business and cultivating superior employee relations; mastering niche and specific markets, pursuing customer alignment, pursuing multiple channels to market. These strategies allow SMEs to resource orchestrate to navigate through, and manage, the challenges and opportunities presented from limited resources, large dominant customers, and family business culture for innovation. Moreover, identifying a holistic strategy consisting mainly in A) operating in niche markets and collaborating with customers firms can balance the constraining and facilitating effects of dominant customers on innovation; B) operationalising professional management, building a long-term orientation on business and developing greater employee relationships firms can balance the constraining and facilitating effects of family business culture on innovation; C) leveraging external network and community support, pursuing customer-centric
incremental innovation, and optimising internal processes and resources firms can limit the effects of limited resources on innovation. Furthermore, the data demonstrates by identifying a holistic strategy that firms can also manage the interactions between limited resources, dominant customers, and family business culture. Niche focus and product quality, and ability to stay close to market firms can achieve an innovation position of market and brand leadership with limited resources and increase
bargaining power thus overcoming the effects of limited resources and dominant customers. Similarly, patient capital of family firms promotes long-term innovation with stamina mitigating the effects of limited resources and customer driven short-term innovation. Long-term orientation supports cultivating greater employee relations and business community engagements. They further mitigate the effects of limited resources and enable higher quality and non-incremental innovation which also influence the effects of dominant customers.While it has been recognised that SMEs are constrained in their ability to innovate, little is known about how they innovate despite their constraints. Based on 30 in-depth discussions with food SME participants, this thesis seeks to advance knowledge by developing a theory to explain how low-tech SMEs innovate despite constraints. This study focuses on the low-tech sector given its key contributing role to the economy.
The literature highlights three key themes that majorly influence innovation in low-tech
SMEs. These themes are limited resources, large dominant customers, and family business culture. As SMEs, they are limited with resources, deal with a network of large dominant customers, and operate under a family business culture. The findings demonstrate distinct yet interdependent strategies executed by managers such as leveraging external network support, pursuing customer-centric incremental innovation, optimising internal processes and resources; operationalising professional management, building a long-term orientation on business and cultivating superior employee relations; mastering niche and specific markets, pursuing customer alignment, pursuing multiple channels to market. These strategies allow SMEs to resource orchestrate to navigate through, and manage, the challenges and opportunities presented from limited resources, large dominant customers, and family business culture for innovation. Moreover, identifying a holistic strategy consisting mainly in A) operating in niche markets and collaborating with customers firms can balance the constraining and facilitating effects of dominant customers on innovation; B) operationalising professional management, building a long-term orientation on business and developing greater employee relationships firms can balance the constraining and facilitating effects of family business culture on innovation; C) leveraging external network and community support, pursuing customer-centric
incremental innovation, and optimising internal processes and resources firms can limit the effects of limited resources on innovation. Furthermore, the data demonstrates by identifying a holistic strategy that firms can also manage the interactions between limited resources, dominant customers, and family business culture. Niche focus and product quality, and ability to stay close to market firms can achieve an innovation position of market and brand leadership with limited resources and increase
bargaining power thus overcoming the effects of limited resources and dominant customers. Similarly, patient capital of family firms promotes long-term innovation with stamina mitigating the effects of limited resources and customer driven short-term innovation. Long-term orientation supports cultivating greater employee relations and business community engagements. They further mitigate the effects of limited resources and enable higher quality and non-incremental innovation which also influence the effects of dominant customers
Machine learning models for the prediction of pharmaceutical powder properties
Get PDFUnderstanding how particle attributes affect the pharmaceutical manufacturing process performance remains a significant challenge for the industry, adding cost and time to the development of robust products and production routes. Tablet formation can be achieved by several techniques however, direct compression (DC) and granulation are the most widely used in industrial operations. DC is of particular interest as it offers lower-cost manufacturing and a streamlined process with fewer steps compared with other unit operations. However, to achieve the full potential benefits of DC for tablet manufacture, this places strict demands on material flow properties, blend uniformity, compactability, and lubrication, which need to be satisfied. DC is increasingly the preferred technique for pharmaceutical companies for oral solid dose manufacture, consequently making the flow prediction of pharmaceutical materials of increasing importance. Bulk properties are influenced by particle attributes, such as particle size and shape, which are defined during crystallization and/or milling processes. Currently, the suitability of raw materials and/or formulated blends for DC requires detailed characterization of the bulk properties. A key goal of digital design and Industry 4.0 concepts is through digital transformation of existing development steps be able to better predict properties whilst minimizing the amount of material and resources required to inform process selection during early- stage development.The work presented in Chapter 4 focuses on developing machine learning (ML) models to predict powder flow behaviour of routine, widely available pharmaceutical materials. Several datasets comprising powder attributes (particle size, shape, surface area, surface energy, and bulk density) and flow properties (flow function coefficient) have been built, for pure compounds, binary mixtures, and multicomponent formulations. Using these datasets, different ML models, including traditional ML (random forest, support vector machines, k nearest neighbour, gradient boosting, AdaBoost, Naïve Bayes, and logistic regression) classification and regression approaches, have been explored for the prediction of flow properties, via flow function coefficient. The models have been evaluated using multiple sampling methods and validated using external datasets, showing a performance over 80%, which is sufficiently high for their implementation to improve manufacturing efficiency. Finally, interpretability methods, namely SHAP (SHapley Additive exPlanaitions), have been used to understand the predictions of the machine learning models by determining how much each variable included in the training dataset has contributed to each final prediction.Chapter 5 expanded on the work presented in Chapter 4 by demonstrating the applicability of ML models for the classification of the viability of pharmaceutical formulations for continuous DC via flow function coefficient on their powder flow. More than 100 formulations were included in this model and the particle size and particle shape of the active pharmaceutical ingredients (APIs), the flow function coefficient of the APIs, and the concentration of the components of the formulations were used to build the training dataset. The ML models were evaluated using different sampling techniques, such as bootstrap sampling and 10-fold cross-validation, achieving a precision of 90%.Furthermore, Chapter 6 presents the comparison of two data-driven model approaches to predict powder flow: a Random Forest (RF) model and a Convolutional Neural Network (CNN) model. A total of 98 powders covering a wide range of particle sizes and shapes were assessed using static image analysis. The RF model was trained on the tabular data (particle size, aspect ratio, and circularity descriptors), and the CNN model was trained on the composite images. Both datasets were extracted from the same characterisation instrument. The data were split into training, testing, and validation sets. The results of the validation were used to compare the performance of the two approaches. The results revealed that both algorithms achieved a similar performance since the RF model and the CNN model achieved the same accuracy of 55%.Finally, other particle and bulk properties, i.e., bulk density, surface area, and surface energy, and their impact on the manufacturability and bioavailability of the drug product are explored in Chapter 7. The bulk density models achieved a high performance of 82%, the surface area models achieved a performance of 80%, and finally, the surface-energy models achieved a performance of 60%. The results of the models presented in this chapter pave the way to unified guidelines moving towards end-to-end continuous manufacturing by linking the manufacturability requirements and the bioavailability requirements.Understanding how particle attributes affect the pharmaceutical manufacturing process performance remains a significant challenge for the industry, adding cost and time to the development of robust products and production routes. Tablet formation can be achieved by several techniques however, direct compression (DC) and granulation are the most widely used in industrial operations. DC is of particular interest as it offers lower-cost manufacturing and a streamlined process with fewer steps compared with other unit operations. However, to achieve the full potential benefits of DC for tablet manufacture, this places strict demands on material flow properties, blend uniformity, compactability, and lubrication, which need to be satisfied. DC is increasingly the preferred technique for pharmaceutical companies for oral solid dose manufacture, consequently making the flow prediction of pharmaceutical materials of increasing importance. Bulk properties are influenced by particle attributes, such as particle size and shape, which are defined during crystallization and/or milling processes. Currently, the suitability of raw materials and/or formulated blends for DC requires detailed characterization of the bulk properties. A key goal of digital design and Industry 4.0 concepts is through digital transformation of existing development steps be able to better predict properties whilst minimizing the amount of material and resources required to inform process selection during early- stage development.The work presented in Chapter 4 focuses on developing machine learning (ML) models to predict powder flow behaviour of routine, widely available pharmaceutical materials. Several datasets comprising powder attributes (particle size, shape, surface area, surface energy, and bulk density) and flow properties (flow function coefficient) have been built, for pure compounds, binary mixtures, and multicomponent formulations. Using these datasets, different ML models, including traditional ML (random forest, support vector machines, k nearest neighbour, gradient boosting, AdaBoost, Naïve Bayes, and logistic regression) classification and regression approaches, have been explored for the prediction of flow properties, via flow function coefficient. The models have been evaluated using multiple sampling methods and validated using external datasets, showing a performance over 80%, which is sufficiently high for their implementation to improve manufacturing efficiency. Finally, interpretability methods, namely SHAP (SHapley Additive exPlanaitions), have been used to understand the predictions of the machine learning models by determining how much each variable included in the training dataset has contributed to each final prediction.Chapter 5 expanded on the work presented in Chapter 4 by demonstrating the applicability of ML models for the classification of the viability of pharmaceutical formulations for continuous DC via flow function coefficient on their powder flow. More than 100 formulations were included in this model and the particle size and particle shape of the active pharmaceutical ingredients (APIs), the flow function coefficient of the APIs, and the concentration of the components of the formulations were used to build the training dataset. The ML models were evaluated using different sampling techniques, such as bootstrap sampling and 10-fold cross-validation, achieving a precision of 90%.Furthermore, Chapter 6 presents the comparison of two data-driven model approaches to predict powder flow: a Random Forest (RF) model and a Convolutional Neural Network (CNN) model. A total of 98 powders covering a wide range of particle sizes and shapes were assessed using static image analysis. The RF model was trained on the tabular data (particle size, aspect ratio, and circularity descriptors), and the CNN model was trained on the composite images. Both datasets were extracted from the same characterisation instrument. The data were split into training, testing, and validation sets. The results of the validation were used to compare the performance of the two approaches. The results revealed that both algorithms achieved a similar performance since the RF model and the CNN model achieved the same accuracy of 55%.Finally, other particle and bulk properties, i.e., bulk density, surface area, and surface energy, and their impact on the manufacturability and bioavailability of the drug product are explored in Chapter 7. The bulk density models achieved a high performance of 82%, the surface area models achieved a performance of 80%, and finally, the surface-energy models achieved a performance of 60%. The results of the models presented in this chapter pave the way to unified guidelines moving towards end-to-end continuous manufacturing by linking the manufacturability requirements and the bioavailability requirements
Internationalization at home : English-medium instruction practices at sino-foreign cooperative universities
Get PDFEnglish-medium-instruction (EMI), an emerging phenomenon brought about by the accelerating trend of internationalization in higher education, has become a well-established area of study during the past few years. Numerous studies have investigated various facets of EMI, including the ideology and policymaking behind it, as well as the implementation at the classroom levels in a variety of internationalized settings. The benefits of EMI to students' language acquisition and content understanding have been recognised by various studies (e.g., Tsang, and Li 2021), but it is widely recognised that engaging in EMI can be an intellectually and emotionally challenging experience for university students for different reasons. One contributing factor identified by several studies is the EMI teaching practices including the pedagogical approaches adopted by EMI teachers and the language proficiency of teachers. Several EMI scholars (e.g. Zhang and Pladevall-Ballester, 2022; Thompson et al. 2022) have set out to systematically investigate students’ perspectives towards EMI to better understand and address the challenges.This research, however, seeks to understand and address the issues and challenges of EMI teaching practice from the perspective of EMI teachers. Specifically, this study is an exploration of EMI teachers’ experiences and perceptions in the adoption of EMI for the achievement of internationalization at home (IaH) at two Sino-foreign Cooperative universities (SFCUs) in China. Research data were generated through semi-structured interviews with 20 EMI teachers from these two SFCUs and through classroom observations of two of the interviewees, using a qualitative approach within a constructivist paradigm.The findings showed that EMI teachers interviewed agreed that there are numerous areas for improvement in the implementation of EMI in China’s SFCUs, and each offered their ideas of tackling the challenges. They did, however, raise issues at various stage in the EMI implementation process and the different stakeholders involved in EMI practice. For instance, they shared their perspectives and experiences on EMI practice in relation to EMI teacher recruitment, professional learning, and pedagogical strategies. Moreover, they offered suggestions for enhancing the effectiveness of EMI from the viewpoints of EMI administrators, teachers and the recipients of EMI teaching, i.e. EMI students. One of the essential findings include the necessity for more stringent screening of EMI teacher candidates at the point of teacher recruitment, more in-service professional learning opportunities and more reasonable evaluation approaches for EMI teachers, in order to improve their teaching effectiveness. Another significant finding is that the mental stress caused by the EMI dual goals of acquiring subject content knowledge and improving English proficiency is not negligible. Such mental health problems may be exacerbated by the pandemic-related regulations in China during the Covid-19 period.In addition to this, some of the pedagogical accommodation strategies adopted by EMI teachers and the main challenges in achieving the dual goals of EMI as perceived by EMI teachers are also discussed in this research.According to the research, there are a number of improvements that should be made to successfully realize IaH using EMI. For example, adjustments are needed from the ideology embedded in China’s EMI policies to the enactment of EMI policies in China. Additionally, communication and collaboration between different EMI stakeholders should be maintained to ensure that EMI is effectively practiced and promoted at SFCUs and across higher education in China more broadly, so as to properly broaden students’ international perspectives, improve students’ language and subject knowledge, and achieve successful IaH in China.English-medium-instruction (EMI), an emerging phenomenon brought about by the accelerating trend of internationalization in higher education, has become a well-established area of study during the past few years. Numerous studies have investigated various facets of EMI, including the ideology and policymaking behind it, as well as the implementation at the classroom levels in a variety of internationalized settings. The benefits of EMI to students' language acquisition and content understanding have been recognised by various studies (e.g., Tsang, and Li 2021), but it is widely recognised that engaging in EMI can be an intellectually and emotionally challenging experience for university students for different reasons. One contributing factor identified by several studies is the EMI teaching practices including the pedagogical approaches adopted by EMI teachers and the language proficiency of teachers. Several EMI scholars (e.g. Zhang and Pladevall-Ballester, 2022; Thompson et al. 2022) have set out to systematically investigate students’ perspectives towards EMI to better understand and address the challenges.This research, however, seeks to understand and address the issues and challenges of EMI teaching practice from the perspective of EMI teachers. Specifically, this study is an exploration of EMI teachers’ experiences and perceptions in the adoption of EMI for the achievement of internationalization at home (IaH) at two Sino-foreign Cooperative universities (SFCUs) in China. Research data were generated through semi-structured interviews with 20 EMI teachers from these two SFCUs and through classroom observations of two of the interviewees, using a qualitative approach within a constructivist paradigm.The findings showed that EMI teachers interviewed agreed that there are numerous areas for improvement in the implementation of EMI in China’s SFCUs, and each offered their ideas of tackling the challenges. They did, however, raise issues at various stage in the EMI implementation process and the different stakeholders involved in EMI practice. For instance, they shared their perspectives and experiences on EMI practice in relation to EMI teacher recruitment, professional learning, and pedagogical strategies. Moreover, they offered suggestions for enhancing the effectiveness of EMI from the viewpoints of EMI administrators, teachers and the recipients of EMI teaching, i.e. EMI students. One of the essential findings include the necessity for more stringent screening of EMI teacher candidates at the point of teacher recruitment, more in-service professional learning opportunities and more reasonable evaluation approaches for EMI teachers, in order to improve their teaching effectiveness. Another significant finding is that the mental stress caused by the EMI dual goals of acquiring subject content knowledge and improving English proficiency is not negligible. Such mental health problems may be exacerbated by the pandemic-related regulations in China during the Covid-19 period.In addition to this, some of the pedagogical accommodation strategies adopted by EMI teachers and the main challenges in achieving the dual goals of EMI as perceived by EMI teachers are also discussed in this research.According to the research, there are a number of improvements that should be made to successfully realize IaH using EMI. For example, adjustments are needed from the ideology embedded in China’s EMI policies to the enactment of EMI policies in China. Additionally, communication and collaboration between different EMI stakeholders should be maintained to ensure that EMI is effectively practiced and promoted at SFCUs and across higher education in China more broadly, so as to properly broaden students’ international perspectives, improve students’ language and subject knowledge, and achieve successful IaH in China
Safety Management Practices CP918 exam papers
No full textAccess restricted to staff and registered students of the University of Strathclyde.PAST EXAM PAPERS ARE NO LONGER BEING ADDED TO STAX. PLEASE VISIT SUPRIMO TO ACCESS AN UP-TO-DATE COLLECTION OF PAST EXAM PAPERS: https://suprimo.lib.strath.ac.uk
A novel methodology for robust, holistic, simulation-based ship design optimization
Get PDFThe herein presented thesis presents the author’s research work on the field of ship design that fulfils the requirements of the Doctor of Philosophy degree. The original scientific research is focused on the field of ship design optimization with the novelty of lifecycle simulation from the early ship design stage (basic/preliminary ship design) as well as the use of digital twin models generated based on big data acquired from a fleet of actual vessels. Three different of layers for uncertainty have been added (market uncertainty, environmental and vessel operation uncertainty, method and model error modelling), tightly coupled with a comprehensive voyage simulation framework for the vessel’s entire lifecycle (25 years). This robust holistic design approach (RHODA) has been deployed for formal and global ship design optimization studies and compared against deterministic runs showing a great potential for more effective design space exploration resulting into more robust dominant variants over different environments and market conditions. To showcase the applicability and potential of the herein proposed RHODA method and research work, the methodology has been also adapted to be applicable for zero emission vessels (NH3 powered bulk carriers) and a global ship design optimization case study has been performed for such vessels yielding many interesting design points for the future.The herein presented thesis presents the author’s research work on the field of ship design that fulfils the requirements of the Doctor of Philosophy degree. The original scientific research is focused on the field of ship design optimization with the novelty of lifecycle simulation from the early ship design stage (basic/preliminary ship design) as well as the use of digital twin models generated based on big data acquired from a fleet of actual vessels. Three different of layers for uncertainty have been added (market uncertainty, environmental and vessel operation uncertainty, method and model error modelling), tightly coupled with a comprehensive voyage simulation framework for the vessel’s entire lifecycle (25 years). This robust holistic design approach (RHODA) has been deployed for formal and global ship design optimization studies and compared against deterministic runs showing a great potential for more effective design space exploration resulting into more robust dominant variants over different environments and market conditions. To showcase the applicability and potential of the herein proposed RHODA method and research work, the methodology has been also adapted to be applicable for zero emission vessels (NH3 powered bulk carriers) and a global ship design optimization case study has been performed for such vessels yielding many interesting design points for the future
Enhancement of electrochemical sensor performance through the optimisation of nucleic acid probe architecture
Get PDFA key aim in the field of diagnostics is to engineer instrumentation that fulfils three
primary aims. This includes enhancing the sensitivity of a device, or improve the ability to determine minimal concentrations of analyte in a complex sample. Secondly, devices must be capable of producing a signal readout in response to the presence or absence of the target analyte in a short time window. Thirdly, manufactured devices must be feasibly deployed to a point of care setting at a low cost, often in challenging environments Electrochemical methods can serve as the workhorse in achieving such goals, with its power in discriminating variations to a series of properties that describe a bioelectric interface. Simply, these interfaces are composed of an immobilised biomolecule upon a metal transducer
surface that is capable of the capture, or detection, of a desired molecular target. In the case of nucleic acid detection, immobilised receptor nucleic acids, or DNA probes, serve as the detection element of the system. These DNA probes are engineered to share complementarity to a desired nucleic acid target, and in the presence of such a target, will capture the analyte by hybridisation through Watson-Crick base pairing laws. These hybridisation events change the interfacial
properties of the transducer, and by electrochemical techniques, devices can translate such derivations in to a signal read out for the user. Molecular self-assembly is a process whereby molecules spontaneously form organised
structures, governed by the inherent interactions between the local constituents. It is this principle that drives the formation of immobilised DNA probes in a “DNA Self-Assembled Monolayer”. This technique allows for a simple method of bioelectric interface construction. Conventionally, these DNA probes are single-stranded linear elements. However, an increasing number of publications are exploring ever more complex probe geometries in biosensing applications. Despite this, there is a distinct lack of contributions to the literature detailing
whether such advanced probe architectures may provide a meaningful solution to current problems facing low cost point of care devices. To this end, this thesis attempts to explore key metrics of biosensor performance with an ever-increasing bioelectric interface complexity. Here, increasing complexity is achieved by the introduction of higher order probe architectures, or by the introduction of DNA nanostructures free in solution, which may serve as signal amplifiers. The first section of this work provides an extensive literature review. This begins with
exploring the need for rapid PoC diagnostic technologies, with a particular focus on tackling antimicrobial resistance. This is followed by a detailing of current nucleic acid detection methods, the advent of DNA nanotechnology, and its recent advances and emerging applications. Thereafter, the DNA origami method is described, and its power and application in biosensor design is discussed. Finally, an account of key theoretical concepts governing electrochemical methods is provided. Experimental chapters then follow, detailing the development and testing of a series of electrochemical biosensor designs, each with an increasing degree of probe complexity. The first of which explores a class of 1D and 2D probes. These linear and hairpin probes are thoroughly interrogated to explore potential improvements in both sensitivity, and specificity. Within this chapter, successful enhancement in sensor selectivity was observed with a hairpin probe
architecture against a linear probe. Sensitivity to complementary target was deemed comparable between both probe apparatus; therefore, translation of the hairpin based bioelectric interface to a microelectrode platform was undertaken. This was successfully shown to boost sensitivity in accordance with literature reports, while maintaining the enhanced selectivity inherent to hairpin probe structures. The second experimental chapter focuses on the introduction of tetrahedral DNA nanostructures to electrochemical biosensor apparatus. Three distinct strategies where explored. Firstly, a designed tetrahedron serves as the immobilised probe. Secondly, the same tetrahedron was modified to harbour an electroactive redox tag producing a “signal off” biosensor design. Finally, a novel approach is detailed, using free tetrahedra in solution to serve as signal amplifiers by boosting impedance following their tethering to the surface by a complementary target oligonucleotide. A valuable proof of concept is established here in the ability of nanostructures to serve as inexpensive and powerful methods of signal amplification negating the need for complex and costly chemistries common to other strategies. The third experimental chapter builds upon the signal amplification strategy described above, with the introduction of a novel, and highly programmable DNA origami tile. In a first for the electrochemical biosensor field, this chapter reports on a series of tile nanostructure designs
capable of effectively crosslinking to a linear probe DNA functionalised transducer, with the presence of a complementary target serving as the linking tether. With this approach, growth in the impedance of the interface contributes to a significant improvement in sensor limit of detection, and importantly remains highly effective in a DNA rich complex media, proving its potential in future PoC devices.
The final section of experimental work here focuses on a novel sensing approach, with a divergence from nucleic acid detection, to the successful electrochemical interrogation of environmental conditions by a switchable DNA nanostructure. Here, a DNA origami “zipper” has been designed to be responsive to environmental stimuli, specifically pH. Such a sensing application is of need given the known alterations in local pH conditions associated with both bacterial growth, and a series of human pathologies. This zipper structure was successfully
immobilised as part of mixed SAM, forming a bioelectric interface capable of discriminated local pH conditions across a broad and clinically relevant pH range.A key aim in the field of diagnostics is to engineer instrumentation that fulfils three
primary aims. This includes enhancing the sensitivity of a device, or improve the ability to determine minimal concentrations of analyte in a complex sample. Secondly, devices must be capable of producing a signal readout in response to the presence or absence of the target analyte in a short time window. Thirdly, manufactured devices must be feasibly deployed to a point of care setting at a low cost, often in challenging environments Electrochemical methods can serve as the workhorse in achieving such goals, with its power in discriminating variations to a series of properties that describe a bioelectric interface. Simply, these interfaces are composed of an immobilised biomolecule upon a metal transducer
surface that is capable of the capture, or detection, of a desired molecular target. In the case of nucleic acid detection, immobilised receptor nucleic acids, or DNA probes, serve as the detection element of the system. These DNA probes are engineered to share complementarity to a desired nucleic acid target, and in the presence of such a target, will capture the analyte by hybridisation through Watson-Crick base pairing laws. These hybridisation events change the interfacial
properties of the transducer, and by electrochemical techniques, devices can translate such derivations in to a signal read out for the user. Molecular self-assembly is a process whereby molecules spontaneously form organised
structures, governed by the inherent interactions between the local constituents. It is this principle that drives the formation of immobilised DNA probes in a “DNA Self-Assembled Monolayer”. This technique allows for a simple method of bioelectric interface construction. Conventionally, these DNA probes are single-stranded linear elements. However, an increasing number of publications are exploring ever more complex probe geometries in biosensing applications. Despite this, there is a distinct lack of contributions to the literature detailing
whether such advanced probe architectures may provide a meaningful solution to current problems facing low cost point of care devices. To this end, this thesis attempts to explore key metrics of biosensor performance with an ever-increasing bioelectric interface complexity. Here, increasing complexity is achieved by the introduction of higher order probe architectures, or by the introduction of DNA nanostructures free in solution, which may serve as signal amplifiers. The first section of this work provides an extensive literature review. This begins with
exploring the need for rapid PoC diagnostic technologies, with a particular focus on tackling antimicrobial resistance. This is followed by a detailing of current nucleic acid detection methods, the advent of DNA nanotechnology, and its recent advances and emerging applications. Thereafter, the DNA origami method is described, and its power and application in biosensor design is discussed. Finally, an account of key theoretical concepts governing electrochemical methods is provided. Experimental chapters then follow, detailing the development and testing of a series of electrochemical biosensor designs, each with an increasing degree of probe complexity. The first of which explores a class of 1D and 2D probes. These linear and hairpin probes are thoroughly interrogated to explore potential improvements in both sensitivity, and specificity. Within this chapter, successful enhancement in sensor selectivity was observed with a hairpin probe
architecture against a linear probe. Sensitivity to complementary target was deemed comparable between both probe apparatus; therefore, translation of the hairpin based bioelectric interface to a microelectrode platform was undertaken. This was successfully shown to boost sensitivity in accordance with literature reports, while maintaining the enhanced selectivity inherent to hairpin probe structures. The second experimental chapter focuses on the introduction of tetrahedral DNA nanostructures to electrochemical biosensor apparatus. Three distinct strategies where explored. Firstly, a designed tetrahedron serves as the immobilised probe. Secondly, the same tetrahedron was modified to harbour an electroactive redox tag producing a “signal off” biosensor design. Finally, a novel approach is detailed, using free tetrahedra in solution to serve as signal amplifiers by boosting impedance following their tethering to the surface by a complementary target oligonucleotide. A valuable proof of concept is established here in the ability of nanostructures to serve as inexpensive and powerful methods of signal amplification negating the need for complex and costly chemistries common to other strategies. The third experimental chapter builds upon the signal amplification strategy described above, with the introduction of a novel, and highly programmable DNA origami tile. In a first for the electrochemical biosensor field, this chapter reports on a series of tile nanostructure designs
capable of effectively crosslinking to a linear probe DNA functionalised transducer, with the presence of a complementary target serving as the linking tether. With this approach, growth in the impedance of the interface contributes to a significant improvement in sensor limit of detection, and importantly remains highly effective in a DNA rich complex media, proving its potential in future PoC devices.
The final section of experimental work here focuses on a novel sensing approach, with a divergence from nucleic acid detection, to the successful electrochemical interrogation of environmental conditions by a switchable DNA nanostructure. Here, a DNA origami “zipper” has been designed to be responsive to environmental stimuli, specifically pH. Such a sensing application is of need given the known alterations in local pH conditions associated with both bacterial growth, and a series of human pathologies. This zipper structure was successfully
immobilised as part of mixed SAM, forming a bioelectric interface capable of discriminated local pH conditions across a broad and clinically relevant pH range
Development of new approaches for understanding and optimizing antisolvent crystallization processes
Get PDFAs more and more complex molecules find their way to the market, alongside the increasing demand for pharmaceuticals, the need for development and improvement of key process steps is crucial. By reducing the time and amount of drug substance used up during each phase in the crystallisation development process, pharmaceuticals can be delivered to patients faster. To achieve our aim, three contributions have been made, looking at both single and multicomponent systems for solvent selection protocols alongside the development and application of a model equation to accurately describe antisolvent phase diagrams. Initially solubility and activity coefficient models were applied to different solute-solvent systems. In doing so, both the Margules and Wilson models showed accurate modelling capability with the experimental activity coefficients. Due to the parameters of the Wilson model, interactions between the solute-solvent molecules can be quantified. This information about molecular interaction from specific functional groups, allow solvent selection protocols to be engineered towards specific solvent systems. From the investigation of single solvent systems, antisolvent systems were investigated. By using compounds like those observed in industry such as salts and highly hydrophobic compounds, coupled with a temperature variation method, a generic model equation was developed encompassing the general trend observed by antisolvent phase diagrams systems for both synergistic and non-synergistic systems. This model allowed the prediction and optimization of key process characteristics such as yield and productivity at different temperature and antisolvent fractions, so identifying specific antisolvent fractions and temperatures where processes provide the most optimal results. The final contribution integrates the previous observations and techniques, revealing how well the Wilson model can describe antisolvent phase diagrams, while providing insight into the molecular interactions that dictate synergistic antisolvent phase diagrams. Although the multicomponent Wilson model was not able to describe synergistic relationships, the binary model was able to describe the systems at individual antisolvent fractions. From the binary equation, the interaction parameters at individual antisolvent fractions showed variation in non-ideality as the antisolvent fraction increases, identifying that the interaction parameters between the solute-solvent-antisolvent required are not static and change as the component content changes. From these variations, interpretations of how antisolvent phase diagrams behave is established with regards to molecular interactions occurring with the systems. From each of these contributions, an aspect of crystallization processes was encompassed, identifying either different strategies or developing methodologies which can help reduce the need for time-consuming studies. In doing so allowing a greater pace for getting drugs to patients.As more and more complex molecules find their way to the market, alongside the increasing demand for pharmaceuticals, the need for development and improvement of key process steps is crucial. By reducing the time and amount of drug substance used up during each phase in the crystallisation development process, pharmaceuticals can be delivered to patients faster. To achieve our aim, three contributions have been made, looking at both single and multicomponent systems for solvent selection protocols alongside the development and application of a model equation to accurately describe antisolvent phase diagrams. Initially solubility and activity coefficient models were applied to different solute-solvent systems. In doing so, both the Margules and Wilson models showed accurate modelling capability with the experimental activity coefficients. Due to the parameters of the Wilson model, interactions between the solute-solvent molecules can be quantified. This information about molecular interaction from specific functional groups, allow solvent selection protocols to be engineered towards specific solvent systems. From the investigation of single solvent systems, antisolvent systems were investigated. By using compounds like those observed in industry such as salts and highly hydrophobic compounds, coupled with a temperature variation method, a generic model equation was developed encompassing the general trend observed by antisolvent phase diagrams systems for both synergistic and non-synergistic systems. This model allowed the prediction and optimization of key process characteristics such as yield and productivity at different temperature and antisolvent fractions, so identifying specific antisolvent fractions and temperatures where processes provide the most optimal results. The final contribution integrates the previous observations and techniques, revealing how well the Wilson model can describe antisolvent phase diagrams, while providing insight into the molecular interactions that dictate synergistic antisolvent phase diagrams. Although the multicomponent Wilson model was not able to describe synergistic relationships, the binary model was able to describe the systems at individual antisolvent fractions. From the binary equation, the interaction parameters at individual antisolvent fractions showed variation in non-ideality as the antisolvent fraction increases, identifying that the interaction parameters between the solute-solvent-antisolvent required are not static and change as the component content changes. From these variations, interpretations of how antisolvent phase diagrams behave is established with regards to molecular interactions occurring with the systems. From each of these contributions, an aspect of crystallization processes was encompassed, identifying either different strategies or developing methodologies which can help reduce the need for time-consuming studies. In doing so allowing a greater pace for getting drugs to patients
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