1,756 research outputs found
Approaching the diamond surface: first principles modelling the physics and chemistry of approaching radicals
No full textThe diamond surface plays a central role in much of the diamond research, and as such much of its properties are described and studied in great detail. There is a clear picture of the atomic scale structure of the different facets and their reconstructions. Also terminations with H, O, N and other atomic species as well as the incorporation of these elements has been modelled [1,2]. The electronic structure and the negative electron affinity mechanism is elucidated and so on. In contrast, however, the atomic scale models of diamond growth are much less developed, though progress is being made [3]. In these models the reaction barriers between stable and meta-stable intermediates are being calculated, providing insights into the kinetics of the surface. However, quantum mechanical models can provide much more insights than this. In this work, we simulated the approach of radical atoms and molecules towards the H-terminated diamond 001 surface. By allowing the model to equilibrate at every step, the physics and chemistry of the approach can be followed in minute detail. It allows us to indicate at which distance the surface and radical start interacting, and what that interaction entails. The charge evolution of the radical and the surface is followed by means of Hirshfeld-I charges, providing insights into charge transfer mechanisms. [4] Throughout the approach, the interaction can be followed through different physical and chemical concepts. Different types of bonding are identified as well as H-abstraction events and covalent bonding. In this work, our focus goes to C and P based radicals, showing them to behave very differently near the surface, providing insights into the requirements for improved P incorporation.The author name needs to be updated to include the middle names: Danny E.P. Vanpoucke, and linked to the correct personel account which incorrectly is missing the author middle names
Approaching the diamond surface: first principles modelling the physics and chemistry of approaching radicals
No full textThe diamond surface plays a central role in much of the diamond research, and as such much of its properties are described and studied in great detail. There is a clear picture of the atomic scale structure of the different facets and their reconstructions. Also terminations with H, O, N and other atomic species as well as the incorporation of these elements has been modelled [1,2]. The electronic structure and the negative electron affinity mechanism is elucidated and so on. In contrast, however, the atomic scale models of diamond growth are much less developed, though progress is being made [3]. In these models the reaction barriers between stable and meta-stable intermediates are being calculated, providing insights into the kinetics of the surface. However, quantum mechanical models can provide much more insights than this. In this work, we simulated the approach of radical atoms and molecules towards the H-terminated diamond 001 surface. By allowing the model to equilibrate at every step, the physics and chemistry of the approach can be followed in minute detail. It allows us to indicate at which distance the surface and radical start interacting, and what that interaction entails. The charge evolution of the radical and the surface is followed by means of Hirshfeld-I charges, providing insights into charge transfer mechanisms. [4] Throughout the approach, the interaction can be followed through different physical and chemical concepts. Different types of bonding are identified as well as H-abstraction events and covalent bonding. In this work, our focus goes to C and P based radicals, showing them to behave very differently near the surface, providing insights into the requirements for improved P incorporation.The author name needs to be updated to include the middle names: Danny E.P. Vanpoucke, and linked to the correct personel account which incorrectly is missing the author middle names
On discrimination and the status of immigrants in the Hong Kong labour market
No full textThis paper studies the extent of discrimination against mainland Chinese immigrants in the Hong Kong labour market and provides a quantitative assessment of the source of wage differentials between local born Hong Kong residents and Chinese immigrants. Using the 2001 Hong Kong Population Census data, we find strong evidence of a wage gap between locals and post-1980 Chinese immigrants. There is also clear evidence that discrimination accounts for a substantial proportion of the wage gap between the two groups. Furthermore, our findings suggest that while the overall wage gap may shrink with the immigrants' duration of residence in Hong Kong, the percentage of the wage gap due to discrimination does not change very much after the immediate post-immigration period.discrimination
National education in a democratizing society: An ethnographic study of education for citizenship in a Hong Kong school
No full textAfter its sovereignty transfer to China in 1997, Hong Kong has seen added new national components to its currently practiced school civic education curriculum which promotes basically a local democracy. The study is thus to examine what national curriculum is implemented in schools in building the 'one country, two systems' China locally. This is a case study, consisting of ethnographic participant observations for a period of 14 months in a secondary school, of ethnographic interviews with 9 secondary 6 and 5 student informants, of eight class observations ranging from secondary 1 to 7, and of documentary research about the school's civic education programme, which is focused on the exploration and explanation of how students learn, from their viewpoint, the different facets and levels of a national citizenship being developed in the school. Different from what it has in the mainland China, it is found that the national identity students have learnt is territorialized in the sense that it is a composite identity of nationalism and democracy, with a two-tier loyalty towards Hong Kong and China, a democratic Hong Kong and de-politicized ethno-cultural China. Also, the making of the national identity is more an interactive process of consensus and of cultural decisions among various participants like the government, teachers, parents, students and past students, media and outside bodies rather than a national imprinting. It demonstrates characteristics very like Smith' ร plural model of nation building at its macro-process level and at its micro-process level Anderson's national theory of imagination with a modification. The study hints that the school's national programme turns out to be citizenship education for divergence rather than for convergence as it is initially planned. While the school enlarges the commonality of the ethno-cultural base for national identification, it at the same time widens the political differences of the two sides of the border through its deliberate neglect and avoidance of teaching of mainland politics and its focus on local politics. Despite the fact that the national civic education is the school-based programme and the study is context-specific, there are points and possible lines of development found in the case school, the author believes, more commonly shared than distinct in other local schools which imagine in more or less the same way that they face similar - situations in conducting-the- civic education programme in the HKSAR in the early post-handover years
DFT-based Vibrational Spectra for THz-Spectroscopy and Defect Fingerprinting in Molecular Crystals and Solids.
No full textSpectroscopic techniques based on atomic vibrations provide a powerful tool for the
atomic scale characterization of solids. Unfortunately, the translation of their spectra into
atomistic structures tends to be an inverse-problem, as a structural model is required to
assign the observed spectral peaks. This is further complicated by the fact that the exact
position of the latter is sensitive to the precise underlying atomic structure. This results
in the need for very accurate models.
With the steady growth of computational resources, the calculation of vibrational spectra
for extended and periodic systems has become more attainable at the level of quantum
mechanical calculations. In this work, we first present the example of the THz vibrational
spectrum of lactose-monohydrate (LM), and use our results to identify the spectral lines
of the observed spectra of different phases, obtained experimentally by heating the LM
sample.1 The accompanying water loss induces two phase transitions. According to our
results, all phases, including the starting high purity commercial sample, are mixtures of
different phases. We discuss the impact of both structural—such as water content and
orientation— and methodological—such as Pulay stresses, periodic boundaries, and
supercell sizes—aspects on the calculated spectra, and show that DFT-based spectra
under periodic boundaries can be matched with experimental data.
The importance of an extended periodic system for obtaining an accurate vibrational
spectrum is also shown in studying defects in diamond. However, here, we show that the
qualitative picture of the defect character of each atom in the system is independent of
the system size, allowing for small periodic cells to determine the relevant defect atoms
at much reduced computational cost.2 Defects tend to be very localized, resulting in
atomic modes.3 Therefore, an often-used strategy for selecting the contributing atoms
considers only their relative position with regard to the defect center. Using the atomprojected
vibrational spectrum, we present a quantitative method for determining the
defect character of each atom in the system, allowing for a rational incremental
improvement of the defect spectrum. This method is then applied on several simple
defects in diamond.Author : Danny E.P. Vanpoucke
Author name needs to be updated to include middle names, and correctly linked to the uhasselt personel databas
DFT-based Vibrational Spectra for THz-Spectroscopy and Defect Fingerprinting in Molecular Crystals and Solids.
No full textSpectroscopic techniques based on atomic vibrations provide a powerful tool for the
atomic scale characterization of solids. Unfortunately, the translation of their spectra into
atomistic structures tends to be an inverse-problem, as a structural model is required to
assign the observed spectral peaks. This is further complicated by the fact that the exact
position of the latter is sensitive to the precise underlying atomic structure. This results
in the need for very accurate models.
With the steady growth of computational resources, the calculation of vibrational spectra
for extended and periodic systems has become more attainable at the level of quantum
mechanical calculations. In this work, we first present the example of the THz vibrational
spectrum of lactose-monohydrate (LM), and use our results to identify the spectral lines
of the observed spectra of different phases, obtained experimentally by heating the LM
sample.1 The accompanying water loss induces two phase transitions. According to our
results, all phases, including the starting high purity commercial sample, are mixtures of
different phases. We discuss the impact of both structural—such as water content and
orientation— and methodological—such as Pulay stresses, periodic boundaries, and
supercell sizes—aspects on the calculated spectra, and show that DFT-based spectra
under periodic boundaries can be matched with experimental data.
The importance of an extended periodic system for obtaining an accurate vibrational
spectrum is also shown in studying defects in diamond. However, here, we show that the
qualitative picture of the defect character of each atom in the system is independent of
the system size, allowing for small periodic cells to determine the relevant defect atoms
at much reduced computational cost.2 Defects tend to be very localized, resulting in
atomic modes.3 Therefore, an often-used strategy for selecting the contributing atoms
considers only their relative position with regard to the defect center. Using the atomprojected
vibrational spectrum, we present a quantitative method for determining the
defect character of each atom in the system, allowing for a rational incremental
improvement of the defect spectrum. This method is then applied on several simple
defects in diamond.Author : Danny E.P. Vanpoucke
Author name needs to be updated to include middle names, and correctly linked to the uhasselt personel databas
Buddhism in Theosophical Interpretation of E.P. Blavatskaya: «Philosophic Invention» Problem
No full textThe subject matter of the paper is a most interesting philosophic phenomenon- theosophy which received a considerable emphasis at the end of the nineteenth and the beginning of the twentieth century and greatly influenced viewpoints of the majority of philosophers, writers, musicians and artists. The key point of the article is the author's approach to the problem of close connection between E.P. Blavatskaya's theory and Buddhism. Diverse viewpoints of both famous Russian religious philosophers and buddhologists of that time as well as modern Russian esoteric investigators and scientific papers of E.P. Blavatskaya have been studied.
The famous Russian neo-Kantian philosopher I.I. Lapshin study «Philosophy of Inventing and Invention in Philosophy: Introduction to the History of Philosophy» being inside the scope of her basic investigations, the author comes to the conclusion that theosophy is the original form of «philosophic invention» based on the specific usage of various philosophic and religious ideas of Neo-Platonism, Christian mysticism, cabbalistic theories' symbols as well as Buddhism from the point of view of their esoteric content
The sword of the spirit /
No full textCopyright date from verso of t.p."By the same author": p. [2] of preliminary p.Mode of access: Internet
Materials design through ensemble learning: When the average model knows best
No full textMachine Learning plays an ever more important role in modern materials-design and-discovery presenting a steady flow of new discoveries. Unfortunately, these achievements are generally rooted in large data sets. Although such big data sets are becoming more common place, they are generally not representative for the day-today work performed by materials researchers, where large numbers of samples are often unfeasible due to production-cost or-time, or availability of raw materials. In this work, we investigate the impact of very small data sets (<25 samples) on model quality and show how even for these data sets high quality models can be constructed. Machine Learning in small data sets Due to the success of Machine Learning within the context of large data sets, there is a natural interest to apply these methods in the context of small data sets. The use of AI and ML is these cases is generally aimed at improved design of experiments for materials optimisation, often in combination with robotic automation. Some work on small datasets (50 to several 100 samples) performed using active learning and small deep neural networks show that, even in the context of small data sets, ML can be successful for materials research. However, the quality of the obtained models is often defined in an ad hoc fashion and their sensitivity on the used data. Though clear, the required human selection steps are generally not discussed. Model quality in small data sets In this work, we present a critical investigation of the role of small (< 25 data samples) data sets in ML based regression analysis. We start from a conceptual analysis of the quality of ML models, using training, validation and test sets. In this discussion the strong dependence of the model quality on the considered datapoints is highlighted as an important limitation of ML. Using both synthetic and experimental data sets, we show that the model instances of an ensemble are distributed around the model average [1,2] This result appears to be independent of the underlying model. More interestingly, we find that this ensemble average presents a model-quality on par with that of the best available model instance in the ensemble for the data set. We therefore propose to construct a model instance that is equivalent to the ensemble average, but presents a much lower computational cost for evaluation and storage. This mitigates the observed limitation of ML for small data sets, and makes it also accessible within the context of day-today small scale materials projects. [2]Author name needs to be updated to include middel names:
Danny Vanpoucke is to be updated to Danny E.P. Vanpoucke, and correctly coupled to the personel ID in the UHasselt database which incorrectly is missing the middle names.
This work is not a duplicate, false possitive as consequence of poor checking on only title of the object
Materials design through ensemble learning: When the average model knows best
No full textMachine Learning plays an ever more important role in modern materials-design and-discovery presenting a steady flow of new discoveries. Unfortunately, these achievements are generally rooted in large data sets. Although such big data sets are becoming more common place, they are generally not representative for the day-today work performed by materials researchers, where large numbers of samples are often unfeasible due to production-cost or-time, or availability of raw materials. In this work, we investigate the impact of very small data sets (<25 samples) on model quality and show how even for these data sets high quality models can be constructed. Machine Learning in small data sets Due to the success of Machine Learning within the context of large data sets, there is a natural interest to apply these methods in the context of small data sets. The use of AI and ML is these cases is generally aimed at improved design of experiments for materials optimisation, often in combination with robotic automation. Some work on small datasets (50 to several 100 samples) performed using active learning and small deep neural networks show that, even in the context of small data sets, ML can be successful for materials research. However, the quality of the obtained models is often defined in an ad hoc fashion and their sensitivity on the used data. Though clear, the required human selection steps are generally not discussed. Model quality in small data sets In this work, we present a critical investigation of the role of small (< 25 data samples) data sets in ML based regression analysis. We start from a conceptual analysis of the quality of ML models, using training, validation and test sets. In this discussion the strong dependence of the model quality on the considered datapoints is highlighted as an important limitation of ML. Using both synthetic and experimental data sets, we show that the model instances of an ensemble are distributed around the model average [1,2] This result appears to be independent of the underlying model. More interestingly, we find that this ensemble average presents a model-quality on par with that of the best available model instance in the ensemble for the data set. We therefore propose to construct a model instance that is equivalent to the ensemble average, but presents a much lower computational cost for evaluation and storage. This mitigates the observed limitation of ML for small data sets, and makes it also accessible within the context of day-today small scale materials projects. [2]Author name needs to be updated to include middel names:
Danny Vanpoucke is to be updated to Danny E.P. Vanpoucke, and correctly coupled to the personel ID in the UHasselt database which incorrectly is missing the middle names.
This work is not a duplicate, false possitive as consequence of poor checking on only title of the object
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