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Titanium Dioxide Nanoparticles: Synthesis, X-Ray Line Analysis and Chemical Composition Study
(2016)
TiO2 nanoparticleshave been synthesized by the sol-gel method using titanium alkoxide and isopropanolas a precursor. The structural properties and chemical composition of the TiO2 nanoparticles were studied usingX-ray diffraction, scanning electron microscopy, and X-ray photoelectron spectroscopy.The X-ray powder diffraction pattern confirms that the particles are mainly composed of the anatase phase with the preferential orientation along [101] direction. The physical parameters such as strain, stress and energy density were investigated from the Williamson- Hall (W-H) plot assuming a uniform deformation model (UDM), and uniform deformation energy density model (UDEDM). The W-H analysis shows an anisotropic nature of the strain in nanopowders. The scanning electron microscopy image shows clear TiO2 nanoparticles with particle sizes varying from 60 to 80nm. The results of mean particle size of TiO2 nanoparticles show an inter correlation with the W-H analysis and SEM results. Our X-ray photoelectron spectroscopy spectra show that nearly a complete amount of titanium has reacted to TiO2
In this thesis, thin-film solar cells on the basis of Cu(In,Ga)(S,Se)2 (CIGSSe) were investigated.
Until today, most high efficient CIGSSe-based solar cells use a toxic and wetchemical deposited CdS buffer layer, which doesn’t allow a dry inline production. However, a promising and well-performing alternative buffer layer, namely indium sulfide, has been found which doesn’t comprise these disadvantages. In order to shed light on these well-performing devices, the surfaces and in particular the interfaces which play a major role for the charge carrier transport are investigated in the framework of this thesis. Both, the chemical and electronic properties of the solar cells’ interfaces were characterized.
In case of the physical vapor deposition of an InxSy-based buffer layer, the cleaning step of the CdS chemical-bath deposition is not present and thus changes of the absorber surface have to be taken into account. Therefore, adsorbate formation, oxidation, and segregation of absorber elements in dependence of the storing temperature and the humidity are investigated in the first part of this thesis.
The efficiencies of CIGSSe-based solar cells with an InxSy buffer layer depend on the nominal indium concentration x and display a maximum for x = 42 %. In this thesis, InxSy samples with a nominal indium concentration of 40.2% ≤ x ≤ 43.2% were investigated by surface-sensitive and surface-near bulk-sensitive techniques, namely with photoemission spectroscopy (PES) and x-ray emission spectroscopy (XES). The surfaces of the films were found to be sulfur-poor and indium-rich in comparison with stoichiometric In2S3. Moreover, a direct determination of the band alignment at the InxSy/CISSe interface in dependence of the nominal indium concentration x was conducted with the help of PES and inverse PES (IPES) and a flat band alignment was found for x = 42 %.
In order to study the impact of a heat treatment as it occurs during subsequent cell process steps, the indium sulfide-buffered absorbers were annealed for 30 minutes under UHV conditions at 200 °C after the initial data set was taken. Besides a reported enhanced solar cell performance, a significant copper diffusion from the absorber into the buffer layer takes place due to the thermal treatment. Accordingly, the impact of the copper diffusion on the hidden InxSy/CISSe interface was discussed and for x = 40.2% a significant cliff (downwards step in the conduction band) is observed. For increasing x, the alignment in the conduction band turns into a small upwards step (spike) for the region 41% ≤ x ≤ 43.2%. This explains the optimal solar cell performance for this indium contents.
In a further step, the sodium-doped indium sulfide buffer which leads to significantly higher efficient solar cells was investigated. It was demonstrated by PES/IPES that the enhanced performance can be ascribed to a significant larger surface band gap in comparison with undoped InxSy. The occurring spike in the Na:InxSy/CISSe band alignment gets reduced due to a Se diffusion induced by the thermal treatment. Furthermore, after the thermal treatment the sodium doped indium sulfide layer experiences a copper diffusion which is reduced by more than a factor of two compared to pure InxSy.
Next, the interface between the Na:InxSy buffer layer and the i-ZnO (i = intrinsic, non-deliberately doped), as a part of the transparent front contact was analyzed. The i-ZnO/Na:InxSy interface shows significant interdiffusion, leading to the formation of, e.g., ZnS and hence to a reduction of the nominal cliff in the conduction band alignment.
In the last part of this thesis, the well-established surface-sensitive reflective electron energy loss spectroscopy (REELS) was utilized to study the CIGSSe absorber, the InxSy buffer, and annealed InxSy buffer surfaces. By fitting the characteristic inelastic scattering cross sections λK(E) with Drude-Lindhard oscillators the dielectric function was identified. The determined dielectric functions are in good agreement with values from bulk-sensitive optical measurements on indium sulfide layers. In contrast, for the chalcopyrite-based absorber significant differences appear. In particular, a substantial larger surface band gap of the CIGSSe surface of E^Ex_Gap = (1.4±0.2) eV in comparison with bulk values is determined. This provides for the first time an independent verification of earlier PES/IPES results. Finally, the electrons’ inelastic mean free paths l for the three investigated surfaces are compared for different primary energies with theoretical values and the universal curve.
Background
Several recent publications have demonstrated the use of convolutional neural networks to classify images of melanoma at par with board-certified dermatologists. However, the non-availability of a public human benchmark restricts the comparability of the performance of these algorithms and thereby the technical progress in this field.
Methods
An electronic questionnaire was sent to dermatologists at 12 German university hospitals. Each questionnaire comprised 100 dermoscopic and 100 clinical images (80 nevi images and 20 biopsy-verified melanoma images, each), all open-source. The questionnaire recorded factors such as the years of experience in dermatology, performed skin checks, age, sex and the rank within the university hospital or the status as resident physician. For each image, the dermatologists were asked to provide a management decision (treat/biopsy lesion or reassure the patient). Main outcome measures were sensitivity, specificity and the receiver operating characteristics (ROC).
Results
Total 157 dermatologists assessed all 100 dermoscopic images with an overall sensitivity of 74.1%, specificity of 60.0% and an ROC of 0.67 (range = 0.538–0.769); 145 dermatologists assessed all 100 clinical images with an overall sensitivity of 89.4%, specificity of 64.4% and an ROC of 0.769 (range = 0.613–0.9). Results between test-sets were significantly different (P < 0.05) confirming the need for a standardised benchmark.
Conclusions
We present the first public melanoma classification benchmark for both non-dermoscopic and dermoscopic images for comparing artificial intelligence algorithms with diagnostic performance of 145 or 157 dermatologists. Melanoma Classification Benchmark should be considered as a reference standard for white-skinned Western populations in the field of binary algorithmic melanoma classification.
Background
Melanoma is the most dangerous type of skin cancer but is curable if detected early. Recent publications demonstrated that artificial intelligence is capable in classifying images of benign nevi and melanoma with dermatologist-level precision. However, a statistically significant improvement compared with dermatologist classification has not been reported to date.
Methods
For this comparative study, 4204 biopsy-proven images of melanoma and nevi (1:1) were used for the training of a convolutional neural network (CNN). New techniques of deep learning were integrated. For the experiment, an additional 804 biopsy-proven dermoscopic images of melanoma and nevi (1:1) were randomly presented to dermatologists of nine German university hospitals, who evaluated the quality of each image and stated their recommended treatment (19,296 recommendations in total). Three McNemar's tests comparing the results of the CNN's test runs in terms of sensitivity, specificity and overall correctness were predefined as the main outcomes.
Findings
The respective sensitivity and specificity of lesion classification by the dermatologists were 67.2% (95% confidence interval [CI]: 62.6%–71.7%) and 62.2% (95% CI: 57.6%–66.9%). In comparison, the trained CNN achieved a higher sensitivity of 82.3% (95% CI: 78.3%–85.7%) and a higher specificity of 77.9% (95% CI: 73.8%–81.8%). The three McNemar's tests in 2 × 2 tables all reached a significance level of p < 0.001. This significance level was sustained for both subgroups.
Interpretation
For the first time, automated dermoscopic melanoma image classification was shown to be significantly superior to both junior and board-certified dermatologists (p < 0.001).
Recent research revealed the superiority of artificial intelligence over dermatologists to diagnose melanoma from images. However, 30–50% of all melanomas and more than half of those in young patients evolve from initially benign lesions. Despite its high relevance for melanoma screening, neither clinicians nor computers are yet able to reliably predict a nevus’ oncologic transformation. The cause of this lies in the static nature of lesion presentation in the current standard of care, both for clinicians and algorithms. The status quo makes it difficult to train algorithms (and clinicians) to precisely assess the likelihood of a benign skin lesion to transform into melanoma. In addition, it inhibits the precision of current algorithms since ‘evolution’ image features may not be part of their decision. The current literature reveals certain types of melanocytic nevi (i.e. ‘spitzoid’ or ‘dysplastic’ nevi) and criteria (i.e. visible vasculature) that, in general, appear to have a higher chance to transform into melanoma. However, owing to the cumulative nature of oncogenic mutations in melanoma, a more fine-grained early morphologic footprint is likely to be detectable by an algorithm. In this perspective article, the concept of melanoma prediction is further explored by the discussion of the evolution of melanoma, the concept for training of such a nevi classifier and the implications of early melanoma prediction for clinical practice. In conclusion, the authors believe that artificial intelligence trained on prospective image data could be transformative for skin cancer diagnostics by (a) predicting melanoma before it occurs (i.e. pre-in situ) and (b) further enhancing the accuracy of current melanoma classifiers. Necessary prospective images for this research are obtained via free mole-monitoring mobile apps.