Combined SEM-CL and STEM investigation of green InGaN quantum wells

Abstract The microstructure of green-emitting InGaN/GaN quantum well (QW) samples grown at different temperatures was studied using cross-section scanning transmission electron microscopy (STEM) and plan-view cathodoluminescence (CL). The sample with the lowest InGaN growth temperature exhibits microscale variations in the CL intensity across the sample surface. Using STEM analysis of such areas, the observed darker patches do not correspond to any observable extended defect. Instead, they are related to changes in the extent of gross-well width fluctuations in the QWs, with more brightly emitting regions exhibiting a high density of such fluctuations, whilst dimmer regions were seen to have InGaN QWs with a more uniform thickness.</jats:p

Estimated ultimate recovery prediction of fractured horizontal wells in tight oil reservoirs based on deep neural networks

Accurate estimated ultimate recovery prediction of fractured horizontal wells in tight reservoirs is crucial to economic evaluation and oil field development plan formulation. Advances in artificial intelligence and big data have provided a new tool for rapid production prediction of unconventional reservoirs. In this study, the estimated ultimate recovery prediction model based on deep neural networks was established using the data of 58 horizontal wells in Mahu tight oil reservoirs. First, the estimated ultimate recovery of oil wells was calculated based on the stretched exponential production decline model and a five-region flow model. Then, the calculated estimated ultimate recovery, geological attributes, engineering parameters, and production data of each well were used to build a machine learning database. Before the model training, the number of input parameters was reduced from 14 to 9 by feature selection. The prediction accuracy of the model was improved by data normalization, the early stopping technique, and 10-fold cross validation. The optimal activation function, hidden layers, number of neurons in each layer, and learning rate of the deep neural network model were obtained through hyperparameter optimization. The average determination coefficient on the testing set was 0.73. The results indicate that compared with the traditional estimated ultimate recovery prediction methods, the established deep neural network model has the strengths of a simple procedure and low time consumption, and the deep neural network model can be easily updated to improve prediction accuracy when new well information is obtained.Cited as: Luo, S., Ding, C., Cheng, H., Zhang, B., Zhao, Y., Liu, L. Estimated ultimate recovery prediction of fractured horizontal wells in tight oil reservoirs based on deep neural networks. Advances in Geo-Energy Research, 2022, 6(2): 111-122. https://doi.org/10.46690/ager.2022.02.0

Synthesis of single-walled carbon nanotubes in rich hydrogen/air flames

We explore the production of single-walled carbon nanotubes (CNTs) in a stream surrounded by rich premixedlaminar H2/air flames using a feedstock containing ethanol and ferrocene. The as-produced nanomaterialswere characterised by Raman spectroscopy, transmission electron microscopy, scanning electron microscopyand X-ray diffraction. A formation window of equivalence ratios of 1.00–1.20 was identified, and single-walledCNT bundles with individual CNTs of an average diameter of 1 nm were observed. The formation of CNTswas accompanied by the production of highly crystalline Fe3O4nanoparticles of a size of 20–100 nm. Theinvestigation of the limiting factors for the CNT synthesis was carried out systematically, assisted by numericalmodelling. We conclude that the key factors affecting CNT synthesis are the surrounding flame temperatures and the concentration of carbon available for CNT nucleation.N/

STUDY OF WURTZITE AND ZINCBLENDE GAN BASED GREEN LED HETEROSTRUCTURE

This thesis covers research on the efficiency of green light emitting diodes (LEDs). The work presented focusses on the characterisation of conventional wurtzite (wz)-GaN based LEDs and novel zincblende (zb)-GaN based LEDs. The investigated wz-GaN based multiple quantum well structures have been found in other studies to have higher internal quantum efficiency (IQE) when the quantum wells (QWs) were grown at relatively high temperatures in MOVPE. In this thesis, more QW fluctuations were found as a result of using higher QW growth temperatures for green emitting material. QW fluctuations tended to localise the carriers away from defects, suppressing non-radiative recombination rate and this is hence a possible mechanism for improvement of the IQE of LED devices. Conventional wz-GaN based LEDs are limited in IQE by a strong in-build electric field when a large amount of indium is used to achieve green emission. Despite being a metastable phase, zb-GaN does not suffer from the same problem and exhibits good potential in green emission. A full MOVPE-grown zb-GaN LED structure was characterised in this thesis. The density of stacking faults (SFs) in the MOVPE-grown material is shown to be comparable to the state-of-art MBE-grown samples. A SF-induced alloy segregation effect was discovered in correlated structural and compositional characterisation and was further confirmed by the measurements in atom probe tomography (APT). Indium was found segregating next to the SFs whereas aluminium was found segregating to the SFs. Protruding surface features were produced by the growth of QWs and were suggested to correlate with SF bunches. Cathodoluminescence of a zb-GaN LED structure was characterised in both plan-view and in cross sections. Free exciton transition, donor-acceptor pair (DAP) emission and QW emission of 2.58 – 2.82 eV were observed in a Si doped layer, a Mg doped layer and QW layers, respectively. Low-energy QW emission peaks were also found in large wurtzite inclusions, which have been identified by in-depth correlated STEM and CL characterisation. The understanding given by this thesis on the luminescence and the defects of wz-GaN and zb-GaN based LEDs can provide useful suggestions for future growth strategies to achieve high efficiency green LEDs

Research data supporting "Combined SEM-CL and STEM investigation of green InGaN Quantum Wells"

revised fig. 1:shows the 5 µm × 5 µm AFM scans of the MQW samples revealing the surface morphology. The QW growth temperature (T), rms roughness (R) and image height (H) are stated on the image. H is the difference in height between the highest and lowest pixels in the image fig. 2: The density of dislocations against QW growth temperatures for the MQW samples. Fig. 2 data: data set for Fig. 2 fig. 3: CL images of the MQW sample taken from the surface. The QW growth temperature (T) is stated on the image. fig. 4: (a) CL image taken on the sample grown at 698 °C and (b) SE image taken on the same position of (a). Example dislocations are enclosed in the red circles and an example dark patch is highlighted by the yellow curve. revised fig. 5: (a) The peak emission amplitude and The peak emission amplitude and (b) the peak emission wavelength obtained from a CL line scan performed on the sample grown at 698 °C across a dark patch boundary. Two fitting examples of the dark patch and the bright area are show in (c) and (d), respectively. The dark patch boundary is marked by the green lines. There are two large blue-shift peaks within the dark patch which are related with nearby dislocations. Disregarding the regions near TDs, the average emission amplitudes and wavelengths of the dark patch and the bright area are indicated by red dotted lines. Fig. 5 data: data set for Fig. 5 fig. 6: (a) The CL image taken in step 2. The line-scan position is highlighted by the yellow line. (b) The AFM profile corresponding to the line-scan position extracted from the AFM image taken in step 4. (c) The overview STEM HAADF image taken in step 6. Dislocations in (c) are matching with the dislocation pits found in (b). The dark patch boundary found in step 3 is marked by thicker Pt deposition on the sample surface, which is highlighted by the green arrow. Fig. 6 profile: data set for Fig. 6(b) fig. 7: STEM HAADF images showing the MQW structure of the dark patch and the bright area in the sample grown at 698 °C. The bright lines show the positions of QWs. GWWFs are marked in red doted boxes. revised fig. 8 The average densities of GWWFs plotted against the QW growth temperatures Fig. 8 data: data set for Fig.

Research data supporting "Multimicroscopy of cross-section zincblende GaN LED heterostructure"

Fig. 2(a): the SEM-CL image of the cross-section FIB specimen. Fig. 2(c): Data set of the the mean CL spectrum extracted from CL-Data-withoutfeature.bin in .csv format Fig. 3(a): data set of the mean spectrum taken near the SiC/GaN interface of the cross-section FIB specimen extracted from CL-Data-withoutfeature.bin in .csv format Fig. 3(b): data set of the mean spectrum taken just below the InxGa1-xN MQW of the cross-section FIB specimen extracted from CL-Data-withoutfeature.bin in .csv format Fig. 3(c): data set of the mean spectrum taken at the InxGa1-xN MQW layer of the cross-section FIB specimen extracted from CL-Data-withoutfeature.bin in .csv format Fig. 4(a): data set of the Gaussian fitted peak emission energy map for GaN NBE at around 3.27 eV of the cross-section FIB specimen, extracted from CL-Data-withoutfeature.bin in .csv format Fig. 4(b): data set of the Gaussian fitted peak emission energy map for QW emissions at around 2.71 eV of the cross-section FIB specimen, extracted from CL-Data-withoutfeature.bin in .csv format Fig. 4(c): data set of the spectrum of a selected location where the Gaussian fit does not accurately depict the behaviour of the MQW, extracted from CL-Data-withoutfeature.bin in .csv format Fig. 5(a): the HAADF STEM image of the cross-section FIB specimen including a protruding MQW structure, named feature A. Fig. 5(insert): the panchromatic CL image of the cross-section FIB specimen corresponding to Fig. 5(a),extracted from CL-Data-with feature.bin Fig. 5(b): data set of the spectrum of feature A, extracted from CL-Data-with feature.bin in .csv format Fig. 5(c): data set of the spectrum of the flat MQW area in Fig. 5(a), extracted from CL-Data-with feature.bin in .csv format Fig. 6(1): the diffraction patterns taken from GaN film Fig. 6(2): the diffraction patterns taken from feature A Fig. 6(3): the diffraction patterns taken from SFs below feature A Fig. 6(4): the diffraction patterns taken from the boundary of feature A Fig. 7(a): the HAADF STEM image of feature A Fig. 8: Dataset of the EDS map of Indium taken at feature A in .ser format. The raw data can be opened with the open source software ImageJ and other electron microscopy software packages. CL-Data-with feature.bin: Raw data of the CL hyperspectral image in binary format. The data can be opened with the open source LumiSpy Python library CL-Data-withoutfeature.bin: Raw data of the CL hyperspectral image in binary format. The data can be opened with the open source LumiSpy Python librar

Floating catalyst carbon nanotube synthesis assisted by premixed hydrogen/air flames

Carbon nanotubes (CNTs) were synthesised in the post flame region of premixed laminar flat H2/air flames using feedstocks containing ethanol and ferrocene. The as-produced nanomaterials were collected downstream of the post-flame region at a fixed height above burner of 230 mm and were characterised by various techniques including Raman spectroscopy, scanning electron microscopy and X-ray diffraction (XRD). A formation window of φ = 1.05–1.20 was identified, and the resulting flame temperatures were found to be the dominant limiting factor for producing CNTs. CNT bundles were formed and the diameter of individual CNTs were observed to be smaller than 5 nm. The formation of CNTs was accompanied by production of highly crystalline nanoparticles of a dimensions between 20 and 100 nm, which were identified as Fe3O4 by XREPSR

Research data supporting "Alloy Segregation at Stacking Faults in Zincblende GaN Heterostructures"

The csv files, "Fig. 5 profile" and "Fig. 6 profile", contain all the data points used to construct Fig. 5(d) and Fig. 6(d) of the publication, respectively. In "Fig. 5 profile", the first two columns represents the distance and the intensity, respectively, along a line scan taken on a HAADF STEM image of a quantum well grown in a zincblende LED structure. Column 3,4 and 5 represents the distance, the scaled intensity of Ga signal, the scaled intensity of In signal, respectively, along a line scan taken on a EDS map of the same region with the HAADF STEM image. In "Fig. 6 profile", the first two columns represents the distance and the intensity, respectively, along a line scan taken on a HAADF STEM image of a electron blocking layer grown in a zincblende LED structure. Column 3,4 and 5 represents the distance, the scaled intensity of Ga signal, the scaled intensity of Al signal, respectively, along a line scan taken on a EDS map of the same region with the HAADF STEM image. "Table 1 data" contains all the data used to for the statistic published in Table 1. From row 3 to 12, there are 10 regions examined, respectively, in the quantum wells and the electron blocking layer. For both, the data are presented in three kinds, which are the group III molar fraction of In (or Al) at stacking fault, the group III molar fraction of In (or Al) at the matrix next to the stacking fault, and the ratio between them.We would like to acknowledge funding from BEIS Energy Entrepreneurs fund 6 for their support of Kubos Semiconductors Ltd. We also acknowledge the support of EPSRC through Grant Nos. EP/M010589/1 and EP/R01146X/1. D.J.W. would like to thank the support of EPSRC through Grant No. EP/N01202X/1

Research data supporting "Encapsulation of methylammonium lead bromide perovskite in nanoporous GaN"

Supporting data for the article titled "Encapsulation of methylammonium lead bromide perovskite in nanoporous GaN". The article was accepted for publication in 2019 in the journal "APL Materials". Supplementary material is available from the publisher (AIP). The data files provided are named according to the specific figures in the article in which they are used. Data for Figure 3: Scanning transmission electron microscopy and energy dispersive X-ray spectroscopy (STEM-EDX) data (.ser) and metadata (.emi), as well as elemental maps of the spatial distribution of Ga, Pb, Br in a perovskite/porous GaN composite (2D arrays of the peak intensity of the respective K-alpha lines extracted from the EDX spectra at each point, in CSV format). Data for Figures 4 and 5: Photoluminescence (PL) emission spectra of perovskite samples presented in a two-column CSV format with one header row. Data for Figure S1: EDX spectra of a perovskite/porous GaN composite measured on a pore and between pores, presented in a two-column CSV format with one header row.This work was supported by funding from EPSRC Grant Nos. EP/L015978/1 (Cambridge NanoDTC), EP/L015455/1 (IPES CDT), and EP/M010589/1, funding from Trinity College, Cambridge (Krishnan-Ang Studentship), and funding from the Royal Society (Grant No. UF130278)