PbS and PbO Thin Films via E-Beam Evaporation: Morphology, Structure, and Electrical Properties.

Thin films of lead sulfide (PbS) are being extensively used for the fabrication of optoelectronic devices for commercial and military applications. In the present work, PbS films were fabricated onto a soda lime glass substrate by using an electron beam (e-beam) evaporation technique at a substrate temperature of 300 °C. Samples were annealed in an open atmosphere at a temperature range of 200-450 °C for 2 h. The deposited films were characterized for structural, optical, and electrical properties. Structural properties of PbS have been studied by X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), energy dispersive spectroscopy (EDS), and Rutherford backscattering spectrometry (RBS). The results of XRD showed that the PbS thin film was crystalline in nature at room temperature with cubic crystal structure (galena) and preferential (111) and orientation (022). The morphology of the thin films was studied by FESEM, which also showed uniform and continuous deposition without any peel-off and patches. EDS analysis was performed to confirm the presence of lead and sulfur in as-deposited and annealed films. The thickness of the PbS film was found to be 172 nm, which is slightly greater than the intended thickness of 150 nm, determined by RBS. Ultraviolet-Visible-Near-Infrared (UV-Vis-NIR) spectroscopy revealed the maximum transmittance of ~25% for as-deposited films, with an increase of 74% in annealed films. The band gap of PbS was found in the range of 2.12-2.78 eV for as-deposited and annealed films. Hall measurement confirmed the carriers are p-type in nature. Carrier concentration, mobility of the carriers, conductivity, and sheet resistance are directly determined by Hall-effect measurement. The as-deposited sample showed a conductivity of 5.45 × 10-4 S/m, which gradually reduced to 1.21 × 10-5 S/m due to the composite nature of films (lead sulfide along with lead oxide). Furthermore, the present work also reflects the control of properties by controlling the amount of PbO present in the PbS films which are suitable for various applications (such as IR sensors).

GHS-NET a generic hybridized shallow neural network for multi-label biomedical text classification.

Exponential growth of biomedical literature and clinical data demands more robust yet precise computational methodologies to extract useful insights from biomedical literature and to perform accurate assignment of disease-specific codes. Such approaches can largely enhance the effectiveness of diverse biomedicine and bioinformatics applications. State-of-the-art computational biomedical text classification methodologies either solely leverage discrimintaive features extracted through convolution operations performed by deep convolutional neural network or contextual information extracted by recurrent neural network. However, none of the methodology takes advantage of both convolutional and recurrent neural networks. Further, existing methodologies lack to produce decent performance for the classification of different genre biomedical text such as biomedical literature or clinical notes. We, for the very first time, present a generic deep learning based hybrid multi-label classification methodology namely GHS-NET which can be utilized to accurately classify biomedical text of diverse genre. GHS-NET makes use of convolutional neural network to extract most discriminative features and bi-directional Long Short-Term Memory to acquire contextual information. GHS-NET effectiveness is evaluated for extreme multi-label biomedical literature classification and assignment of ICD-9 codes to clinical notes. For the task of extreme multi-label biomedical literature classification, performance comparison of GHS-Net and state-of-the-art deep learning based methodology reveals that GHS-Net marks the increment of 1%, 6%, and 1% for hallmarks of cancer dataset, 10%, 16%, and 11% for chemical exposure dataset in terms of precision, recall, and F1-score. For the task of clinical notes classification, GHS-Net outperforms previous best deep learning based methodology over Medical Information Mart for Intensive Care dataset (MIMIC-III) by the significant margin of 6%, 8% in terms of recall and F1-score. GHS-NET is available as a web service at1 and potentially can be used to accurately classify multi-variate disease and chemical exposure specific text.

The synthesis of CdZnTe semiconductor thin films for tandem solar cells.

A new approach is adopted to grow cadmium zinc telluride (CdZnTe) thin films using the close spaced sublimation (CSS) technique. The deposition parameters for the growth of cadmium telluride (CdTe) thin films onto the glass substrate were optimized. A zinc telluride (ZnTe) thin film layer was deposited onto already-deposited CdTe thin film to fabricate the CdZnTe (CZT) thin film sample as a ternary compound. Annealing was done after the successful deposition of CZT thin films before further characterization of the CZT thin film samples. The structures of the CZT thin film samples were studied using X-ray diffraction (XRD) and cubic phases were found. A spectrophotometer was used to study the optical parameters, and the energy band gap was found to be in the range of 1.45 eV to 1.75 eV after annealing. The nature of the direct band gap predicts that it might be an ideal component in second-generation solar cells. A Hall measurement system was used to find that the electrical conductivity was in the range of 4.6 × 10-6 to 8.2 × 10-11 (ohm cm)-1. XPS analysis confirmed the presence of Zn in the CdTe thin films. A significant change in electronic properties was observed. These results show that these CZT thin film samples can not only play a key role in the tandem structures of high-efficiency solar cells but they could also be used in the detection of X-rays and gamma rays.

Pronounced Impact of p-Type Carriers and Reduction of Bandgap in Semiconducting ZnTe Thin Films by Cu Doping for Intermediate Buffer Layer in Heterojunction Solar Cells.

Stabilized un-doped Zinc Telluride (ZnTe) thin films were grown on glass substrates under vacuum using a closed space sublimation (CSS) technique. A dilute copper nitrate solution (0.1/100 mL) was prepared for copper doping, known as an ion exchange process, in the matrix of the ZnTe thin film. The reproducible polycrystalline cubic structure of undoped and the Cu doped ZnTe thin films with preferred orientation (111) was confirmed by X-rays diffraction (XRD) technique. Lattice parameter analyses verified the expansion of unit cell volume after incorporation of Cu species into ZnTe thin films samples. The micrographs of scanning electron microscopy (SEM) were used to measure the variation in crystal sizes of samples. The energy dispersive X-rays were used to validate the elemental composition of undoped and Cu-doped ZnTe thin films. The bandgap energy 2.24 eV of the ZnTe thin film decreased after doping Cu to 2.20 eV and may be due to the introduction of acceptors states near to valance band. Optical studies showed that refractive index was measured from 2.18 to 3.24, whereas thicknesses varied between 220 nm to 320 nm for un-doped and Cu doped ZnTe thin film, respectively, using the Swanepoel model. The oxidation states of Zn+2, Te+2, and Cu+1 through high resolution X-ray photoelectron spectroscopy (XPS) analyses was observed. The resistivity of thin films changed from ~107 Ω·cm or undoped ZnTe to ~1 Ω·cm for Cu-doped ZnTe thin film, whereas p-type carrier concentration increased from 4 × 109 cm-2 to 1.4 × 1011 cm-2, respectively. These results predicted that Cu-doped ZnTe thin film can be used as an ideal, efficient, and stable intermediate layer between metallic and absorber back contact for the heterojunction thin film solar cell technology.

Multi-label biomedical question classification for lexical answer type prediction.

Question classification is considered one of the most significant phases of a typical Question Answering (QA) system. It assigns certain answer types to each question which leads to narrow down the search space of possible answers for factoid and list type questions. The process of assigning certain answer types to each question is also known as Lexical Answer Type (LAT) Prediction. Although much work has been done to enhance the performance of question classification into coarse and fine classes in diverse domains, it is still considered a challenging task in the biomedical field. The difficulty in biomedical question classification stems from the fact that one question might have more than one label or expected answer types associated with it (also, referred to as a multi-label classification). In the biomedical domain, only preliminary work is done to classify multi-label questions by transforming them into a single label through copy transformation technique. In this paper, we have generated a multi-labeled corpus (MLBioMedLAT) by exploring the process of Open Advancement of Question Answering (OAQA) system for the task of biomedical question classification. We use 780 biomedical questions from BioASQ challenge and assign them appropriate labels. To annotate these labels, we use the answers for each question and assign the question semantic type labels by leveraging an existing corpus and utilizing OAQA system. The paper introduces a data transformation approach namely Label Power Set with logistic regression (LPLR) for the task of multi-label biomedical question classification and compares its performance with Structured SVM (SSVM), Restricted Boltzmann Machine (RBM), and copy transformation based logistic regression (CLR) (previously used for a similar task in the OAQA system). To evaluate the integrity of the introduced data transformation technique, we use three prominent evaluation measures namely MicroF1, Accuracy, and Hamming Loss. Regarding MicroF1, our introduced technique coupled with a new feature set surpasses CLR, SSVM, and RBM with a margin of 7%, 8%, and 22% respectively.

Identification of a distinct imaging phenotype may improve the management of palindromic rheumatism.

OBJECTIVES: To use high-resolution imaging to characterise palindromic rheumatism (PR) and to compare the imaging pattern observed to that seen in new-onset rheumatoid arthritis (NORA). METHODS: Ultrasound (US) assessment of synovitis, tenosynovitis and non-synovial extracapsular inflammation (ECI) was performed during and between flares in a prospective treatment-naive PR cohort. MRI of the flaring region was performed where possible. For comparison, the same US assessment was also performed in anticyclic citrullinated peptide (CCP) positive individuals with musculoskeletal symptoms (CCP+ at risk) and patients with NORA. RESULTS: Thirty-one of 79 patients with PR recruited were assessed during a flare. A high frequency of ECI was identified on US; 19/31 (61%) of patients had ECI including 12/19 (63%) in whom ECI was identified in the absence of synovitis. Only 7/31 (23%) patients with PR had synovitis (greyscale ≥1 and power Doppler ≥1) during flare. In the hands/wrists, ECI was more prevalent in PR compared with NORA and CCP+ at risk (65% vs 29 % vs 6%, p<0.05). Furthermore, ECI without synovitis was specific for PR (42% PR vs 4% NORA (p=0.003) and 6% CCP+ at risk (p=0.0012)). Eleven PR flares were captured by MRI, which was more sensitive than US for synovitis and ECI. 8/31 (26%) patients with PR developed RA and had a similar US phenotype to NORA at progression. CONCLUSION: PR has a distinct US pattern characterised by reversible ECI, often without synovitis. In patients presenting with new joint swelling, US may refine management by distinguishing relapsing from persistent arthritis.

A survey of ontology learning techniques and applications.

Ontologies have gained a lot of popularity and recognition in the semantic web because of their extensive use in Internet-based applications. Ontologies are often considered a fine source of semantics and interoperability in all artificially smart systems. Exponential increase in unstructured data on the web has made automated acquisition of ontology from unstructured text a most prominent research area. Several methodologies exploiting numerous techniques of various fields (machine learning, text mining, knowledge representation and reasoning, information retrieval and natural language processing) are being proposed to bring some level of automation in the process of ontology acquisition from unstructured text. This paper describes the process of ontology learning and further classification of ontology learning techniques into three classes (linguistics, statistical and logical) and discusses many algorithms under each category. This paper also explores ontology evaluation techniques by highlighting their pros and cons. Moreover, it describes the scope and use of ontology learning in several industries. Finally, the paper discusses challenges of ontology learning along with their corresponding future directions.

Improved biomedical term selection in pseudo relevance feedback.

Biomedical information retrieval systems are becoming popular and complex due to massive amount of ever-growing biomedical literature. Users are unable to construct a precise and accurate query that represents the intended information in a clear manner. Therefore, query is expanded with the terms or features that retrieve more relevant information. Selection of appropriate expansion terms plays key role to improve the performance of retrieval task. We propose document frequency chi-square, a newer version of chi-square in pseudo relevance feedback for term selection. The effects of pre-processing on the performance of information retrieval specifically in biomedical domain are also depicted. On average, the proposed algorithm outperformed state-of-the-art term selection algorithms by 88% at pre-defined test points. Our experiments also conclude that, stemming cause a decrease in overall performance of the pseudo relevance feedback based information retrieval system particularly in biomedical domain.Database URL: http://biodb.sdau.edu.cn/gan/.