التفاصيل البيبلوغرافية
| العنوان: |
Automated analysis of heterogeneous catalyst materials using deep learning |
| المؤلفون: |
Treder, Kevin |
| المساهمون: |
Kim, Judy, Kirkland, Angus |
| بيانات النشر: |
University of Oxford, 2022. |
| سنة النشر: |
2022 |
| المجموعة: |
University of Oxford |
| مصطلحات موضوعية: |
Deep learning (Machine learning), Heterogeneous catalysis, Nanoparticles, Transmission electron microscopy, Computer vision |
| الوصف: |
Heterogeneous catalyst materials play a key role in modern society, as many processes in the chemical and energy industry rely on them. Optimising their performance is directly connected to a large potential of reductions in energy consumption, and thus to a more sustainable future. A fundamental part in the optimisation process is represented by materials characterisation. This is often done using in situ (Scanning) Transmission Electron Microscopy ((S)TEM) imaging in order to obtain a full understanding of the catalyst performance in different environments and temperatures at high resolution. However, the analysis of corresponding dynamical datasets is often time-consuming and requires manual intervention alongside tailored post-processing routines. At the same time, the emergence of direct electron detectors allowing for the acquisition of datasets at kiloHertz frame rates, as well as novel imaging techniques raised data generation rates significantly and created a need for new, reliable and automated data processing techniques. This work introduces 'nNPipe' as Deep Learning based method for the automated analysis of morphologically diverse heterogeneous catalyst systems. The method is based on two Covolutional Neural Networks (CNNs) that were exclusively trained on computationally generated HRTEM image simulations and allow for rapid and precise analysis of raw 2048 x 2048 experimental HRTEM images. The performance of 'nNPipe' is demonstrated in a realistic automated imaging scenario where statistically significant material properties are inferred accurately. Moreover, time-efficient and reproducible retraining methods based small experimental datasets are described for both, further performance improvements and adaption to new imaging scenarios. In this context, a potentially new pathway for the generation of suitable training datasets obtained by thousands of mostly non-expert annotations is highlighted. Finally, the analytical capacities of the 'nNPipe' method are showcased on time-resolved datasets in two advanced applications scenarios: i) Live image analysis during sample acquisition and ii) Analysis of the particle coalescence of an in situ heated Pd/C catalyst. |
| نوع الوثيقة: |
Electronic Thesis or Dissertation |
| اللغة: |
English |
| URL الوصول: |
https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.886761 |
| رقم الانضمام: |
edsble.886761 |
| قاعدة البيانات: |
British Library EThOS |