Performance optimization of eco-friendly CH3NH3SnI3based perovskite solar cell employing CH3NH3SnBr3as hole transport layer by SCAPS-1D device simulator.

This study focuses on the theoretical aspects of third-generation perovskite solar cells (PSC), with the aim of replacing traditional silicon-based counterparts. With potential for higher efficiency and low manufacturing costs, perovskite cells offer unique crystallographic structures allowing adjustments to photoluminescence wavelength. This research addresses challenges in cost-effective solar spectrum utilization and optimization of parameters, device architecture, and materials for high-efficiency cells. In this study, we simulated a perovskite-based solar cell (CH3NH3SnI3) using solar cell capacitance simulator-one dimension simulator under AM 1.5G illumination. The chosen electron transport layer is TiO2, and hole transport layer is CH3NH3SnBr3. The simulation explores variations in layer thickness, defect concentration, interface defects, doping concentration and electron affinity. Additionally, we analyzed the impact of back metal contact work function and temperature variations. Results indicate optimal absorber layer thickness at 0.5µm. Reduced defect concentrations, increased doping concentration and a higher work function for the back contact, enhance efficiency of PSC. The initial parameters yielded a 19.79% efficiency based on base values before optimization, which increased to 26.66% after optimization. According to the latest NREL data, the highest reported efficiency for PSC is 26.1%. This research provides insights into perovskite-based solar cell design for enhanced efficiency.

Automatic Extraction of Medication Data from Semi-Structured Prescriptions.

In many healthcare facilities, the prescription of drugs is done only in a semi-structured manner, using free-text fields where various information is often mixed. Therefore, automatic processing, especially for secondary use such as research purposes, is often challenging. This paper compares various approaches that identify and classify the various parts of these free-text fields in German language, namely simple Levenshtein-based, rule-based and CRF (conditional random field)-based approaches. Our results show that a F1-score >90% can be achieved with both the rule-based and the CRF-based approach, with the CRF-based approach even reaching nearly 95%.

Real-World Data in Greece: Mapping the Papageorgiou General Hospital Data to the OMOP Common Data Model.

This paper showcases the results of the Extract-Transform-Load process mapping the Electronic Health Record of Papageorgiou General Hospital in Thessaloniki, Greece, to the Observational Medical Outcomes Partnership Common Data Model. We describe the staged process utilized to account for the intricate structure of the database, along with some general findings from the mapping. Finally, we investigate potential directions for future research.

Challenges and Lessons Learned in Mapping HL7 v2 Data to openEHR: Insights from UKSH Medical Data Integration Center.

This paper explores the challenges and lessons learned during the mapping of HL7 v2 messages structured using custom schema to openEHR for the Medical Data Integration Center (MeDIC) of the University Hospital, Schleswig-Holstein (UKSH). Missing timestamps in observations, missing units of measurement, inconsistencies in decimal separators and unexpected datatypes were identified as critical inconsistencies in this process. These anomalies highlight the difficulty of automating the transformation of HL7 v2 data to any standard, particularly openEHR, using off-the-shelf tools. Addressing these anomalies is crucial for enhancing data interoperability, supporting evidence-based research, and optimizing clinical decision-making. Implementing proper data quality measures and governance will unlock the potential of integrated clinical data, empowering clinicians and researchers and fostering a robust healthcare ecosystem.

Quality Assessment Framework of Clinical Routine Data for Secondary Use.

To systematically and comprehensively identify data issues in large clinical datasets, we adopted a harmonized data quality assessment framework with Python scripts before integrating the data into FHIR® for secondary use. We also added a preliminary step of categorizing data fields within the database scheme to facilitate the implementation of the data quality framework. As a result, we demonstrated the efficiency and comprehensiveness of detecting data issues using the framework. In future steps, we plan to continually utilize the framework to identify data issues and develop strategies for improving our data quality.

MeDaX: A Knowledge Graph on FHIR.

In Germany, the standard format for exchange of clinical care data for research is HL7 FHIR. Graph databases (GDBs), well suited for integrating complex and heterogeneous data from diverse sources, are currently gaining traction in the medical field. They provide a versatile framework for data analysis which is generally challenging for raw FHIR-formatted data. For generation of a knowledge graph (KG) for clinical research data, we tested different extract-transform-load (ETL) approaches to convert FHIR into graph format. We designed a generalised ETL process and implemented a prototypic pipeline for automated KG creation and ontological structuring. The MeDaX-KG prototype is built from synthetic patient data and currently serves internal testing purposes. The presented approach is easy to customise to expand to other data types and formats.

Multi-Source Data ETL (Extract, Transform, Load) for a Genetic Epilepsy Diagnosis and Treatment Dashboard.

The growing number of genes identified in relation to epilepsy represents a major breakthrough in diagnosis and treatment, but experts face the challenge of efficiently accessing and consolidating the vast amount of genetic data available. Therefore, we present the process of transforming data from different sources and formats into an Entity-Attribute-Value (EAV) model database. Combined with the use of standard coding systems, this approach will provide a scalable and adaptable database to present the data in a comprehensive way to experts via a dashboard.

Naphthalene Diimide-Modified SnO2 Enabling Low-Temperature Processing for Efficient ITO-Free Flexible Perovskite Solar Cells.

A low-cost and indium-tin-oxide (ITO)-free electrode-based flexible perovskite solar cell (PSC) that can be fabricated by roll-to-roll processing shall be developed for successful commercialization. High processing temperatures present a challenge for the PSC fabrication on flexible substrates. The most efficient planar n-i-p PSC structures, which utilize a metal oxide as an electron transport layer (ETL), necessitate high annealing temperatures. In addition, the device performance deteriorates owing to the migration of halogen ions, which causes the oxidation of the metal electrodes. These drawbacks conflict with the development of highly efficient flexible PSCs fabricated on ITO-free transparent electrodes. Herein, an efficient ETL material that enables low-temperature processing is presented. Tin dioxide (SnO2) is modified by (sulfobetaine-N,N-dimethylamino)propyl naphthalene diimide (NDI-B) and used as an ETL. The NDI-B effectively reduces the interfacial nonradiative recombination between the ETL and perovskite and suppresses the ion migration by passivating oxygen-vacancy defects in SnO2 and strongly interacting with halogen ions, respectively. Based on the NDI-B-blended SnO2 ETL, a record PCE of 17.48% is achieved in the ITO-free flexible PSC fabricated at low temperature.

Ce-Doped SnO2 Electron Transport Layer for Minimizing Open Circuit Voltage Loss in Lead Perovskite Solar Cells.

In the planar heterostructure of perovskite-based solar cells (PSCs), tin oxide (SnO2) is a material that is often used as the electron transport layer (ETL). SnO2 ETL exhibits favorable optical and electrical properties in the PSC structures. Nevertheless, the open circuit voltage (VOC) depletion occurs in PSCs due to the defects arising from the high oxygen vacancy on the SnO2 surface and the deeper conduction band (CB) energy level of SnO2. In this research, a cerium (Ce) dopant was introduced in SnO2 (Ce-SnO2) to suppress the VOC loss of the PSCs. The CB minimum of SnO2 was shifted closer to that of the perovskite after the Ce doping. Besides, the Ce doping effectively passivated the surface defects on SnO2 as well as improved the electron transport velocity by the Ce-SnO2. These results enabled the power conversion efficiency (PCE) to increase from 21.1% (SnO2) to 23.0% (Ce-SnO2) of the PSCs (0.09 cm2 active area) with around 100 mV of improved VOC and reduced hysteresis. Also, the Ce-SnO2 ETL-based large area (1.0 cm2) PSCs delivered the highest PCE of 22.9%. Furthermore, a VOC of 1.19 V with a PCE of 23.3% was demonstrated by Ce-SnO2 ETL-based PSCs (0.09 cm2 active area) that were treated with 2-phenethylamine hydroiodide on the perovskite top surface. Notably, the unencapsulated Ce-SnO2 ETL-based PSC was able to maintain above 90% of its initial PCE for around 2000 h which was stored under room temperature condition (23-25 °C) with a relative humidity of 40-50%.

Recent Advances in the Photonic Curing of the Hole Transport Layer, the Electron Transport Layer, and the Perovskite Layers to Improve the Performance of Perovskite Solar Cells.

Perovskite solar cells (PSCs) have attracted increasing research interest, but their performance depends on both the choice of materials and the process used. The materials can typically be treated in solution, which makes them well suited for roll-to-roll processing methods, but their deposition under ambient conditions requires overcoming some challenges to improve stability and efficiency. In this review, we highlight the latest advancements in photonic curing (PC) for perovskite materials, as well as for hole transport layer (HTL) and electron transport layer (ETL) materials. We present how PC parameters can be used to control the optical, electrical, morphological, and structural properties of perovskite HTL and ETL layers. Emphasizing the significance of these advancements for perovskite solar cells could further highlight the importance of this research and underline its essential role in creating more efficient and sustainable solar technology.

Data harmonization and federated learning for multi-cohort dementia research using the OMOP common data model: A Netherlands consortium of dementia cohorts case study.

BACKGROUND: Establishing collaborations between cohort studies has been fundamental for progress in health research. However, such collaborations are hampered by heterogeneous data representations across cohorts and legal constraints to data sharing. The first arises from a lack of consensus in standards of data collection and representation across cohort studies and is usually tackled by applying data harmonization processes. The second is increasingly important due to raised awareness for privacy protection and stricter regulations, such as the GDPR. Federated learning has emerged as a privacy-preserving alternative to transferring data between institutions through analyzing data in a decentralized manner. METHODS: In this study, we set up a federated learning infrastructure for a consortium of nine Dutch cohorts with appropriate data available to the etiology of dementia, including an extract, transform, and load (ETL) pipeline for data harmonization. Additionally, we assessed the challenges of transforming and standardizing cohort data using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) and evaluated our tool in one of the cohorts employing federated algorithms. RESULTS: We successfully applied our ETL tool and observed a complete coverage of the cohorts' data by the OMOP CDM. The OMOP CDM facilitated the data representation and standardization, but we identified limitations for cohort-specific data fields and in the scope of the vocabularies available. Specific challenges arise in a multi-cohort federated collaboration due to technical constraints in local environments, data heterogeneity, and lack of direct access to the data. CONCLUSION: In this article, we describe the solutions to these challenges and limitations encountered in our study. Our study shows the potential of federated learning as a privacy-preserving solution for multi-cohort studies that enhance reproducibility and reuse of both data and analyses.

Use of Metadata-Driven Approaches for Data Harmonization in the Medical Domain: Scoping Review.

BACKGROUND: Multisite clinical studies are increasingly using real-world data to gain real-world evidence. However, due to the heterogeneity of source data, it is difficult to analyze such data in a unified way across clinics. Therefore, the implementation of Extract-Transform-Load (ETL) or Extract-Load-Transform (ELT) processes for harmonizing local health data is necessary, in order to guarantee the data quality for research. However, the development of such processes is time-consuming and unsustainable. A promising way to ease this is the generalization of ETL/ELT processes. OBJECTIVE: In this work, we investigate existing possibilities for the development of generic ETL/ELT processes. Particularly, we focus on approaches with low development complexity by using descriptive metadata and structural metadata. METHODS: We conducted a literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We used 4 publication databases (ie, PubMed, IEEE Explore, Web of Science, and Biomed Center) to search for relevant publications from 2012 to 2022. The PRISMA flow was then visualized using an R-based tool (Evidence Synthesis Hackathon). All relevant contents of the publications were extracted into a spreadsheet for further analysis and visualization. RESULTS: Regarding the PRISMA guidelines, we included 33 publications in this literature review. All included publications were categorized into 7 different focus groups (ie, medicine, data warehouse, big data, industry, geoinformatics, archaeology, and military). Based on the extracted data, ontology-based and rule-based approaches were the 2 most used approaches in different thematic categories. Different approaches and tools were chosen to achieve different purposes within the use cases. CONCLUSIONS: Our literature review shows that using metadata-driven (MDD) approaches to develop an ETL/ELT process can serve different purposes in different thematic categories. The results show that it is promising to implement an ETL/ELT process by applying MDD approach to automate the data transformation from Fast Healthcare Interoperability Resources to Observational Medical Outcomes Partnership Common Data Model. However, the determining of an appropriate MDD approach and tool to implement such an ETL/ELT process remains a challenge. This is due to the lack of comprehensive insight into the characterizations of the MDD approaches presented in this study. Therefore, our next step is to evaluate the MDD approaches presented in this study and to determine the most appropriate MDD approaches and the way to integrate them into the ETL/ELT process. This could verify the ability of using MDD approaches to generalize the ETL process for harmonizing medical data.

Evaluating the influence of novel charge transport materials on the photovoltaic properties of MASnI3 solar cells through SCAPS-1D modelling.

In recent decades, substantial advancements have been made in photovoltaic technologies, leading to impressive power conversion efficiencies (PCE) exceeding 25% in perovskite solar cells (PSCs). Tin-based perovskite materials, characterized by their low band gap (1.3 eV), exceptional optical absorption and high carrier mobility, have emerged as promising absorber layers in PSCs. Achieving high performance and stability in PSCs critically depends on the careful selection of suitable charge transport layers (CTLs). This research investigates the effects of five copper-based hole transport materials and two carbon-based electron transport materials in combination with methyl ammonium tin iodide (MASnI3) through numerical modelling in SCAPS-1D. The carbon-based CTLs exhibit excellent thermal conductivity and mechanical strength, while the copper-based CTLs demonstrate high electrical conductivity. The study comprehensively analyses the influence of these CTLs on PSC performance, including band alignment, quantum efficiency, thickness, doping concentration, defects and thermal stability. Furthermore, a comparative analysis is conducted on PSC structures employing both p-i-n and n-i-p configurations. The highest-performing PSCs are observed in the inverted structures of CuSCN/MASnI3/C60 and CuAlO2/MASnI3/C60, achieving PCE of 23.48% and 25.18%, respectively. Notably, the planar structures of Cu2O/MASnI3/C60 and CuSbS2/MASnI3/C60 also exhibit substantial PCE, reaching 20.67% and 20.70%, respectively.

Enhancement of efficiency in CsSnI3 based perovskite solar cell by numerical modeling of graphene oxide as HTL and ZnMgO as ETL.

Perovskite photovoltaics have an immense contribution toward the all-round development of the solar cell. Apart from the flexibility, stability, and high efficiency, more stress has been given to using lead-free as well as eco-friendly, inexpensive materials in the fabrication of PSC devices. The utilization of non-volatile material, such as cesium tin iodide (CsSnI3), can be proposed for designing the PSC device, which not only makes it eco-friendly but also offers better optoelectronic characteristics due to its smaller bandgap of 1.27 eV. The inclusion of Sn in the perovskite material also functions as an increment in the stability of the perovskite. In the present simulation, CsSnI3 is used as an active absorber layer while the ZnMgO is used as an ETL for a cost-effective nature. Similarly, graphene oxide (GO) is used as HTL for a superior collection of holes. The comprehensive numerical modeling of the ZnMgO can be utilized in solar cell designing with appropriate CsSnI3 thickness, working temperature, total defectivity, and resistance impact, respectively. The presently simulated device offers an excellent efficiency of 17.37 % with CsSnI3-based PSC. These results of the study also show an effective route to develop highly efficient lead-free PSC devices.

Numerical simulation and performance optimization of a lead-free inorganic perovskite solar cell using SCAPS-1D.

The perovskite solar cells, founded on lead halides, have garnered significant attention from the photovoltaic industry owing to their superior efficiency, ease of production, lightweight characteristics, and affordability. However, due to the hazardous nature of lead-based compounds, these solar cells are currently unsuitable for commercial production. In this context, a lead-free perovskite, cesium-bismuth iodide (Cs3Bi2I9) is considered as a potential alternative to the lead halide-based cell due to their non-toxicity and stability, but this perovskite cannot be matched with random hole transport layer (HTL) and electron transport layer (ETL) materials compared to lead halide-based perovskite because of their crystal structure and band gap. Therefore, in this study, performance comparison of different ideal HTL and ETL materials for Cs3Bi2I9 perovskite layer were studied using SCAPS-1D device simulation on the basis of open circuit voltage, short circuit current, power conversion efficiency (PCE) and fill factor (FF) as well as several novel PSC configuration models were designed that can direct for further experimental research for PSC device commercialization. Results from this investigation reveals that the maximum efficiency of 20.96 % is obtained for the configuration ITO/WS2/Cs3Bi2I9/NiO/Au with optimized parameters such as thickness 400 nm, band gap 2.1eV, absorber layer defect density 1012 cm-3, donor density of ETL 1018 cm-3 and the acceptor density of HTL 1020 cm-3.

Transforming Estonian health data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model: lessons learned.

Objective: To describe the reusable transformation process of electronic health records (EHR), claims, and prescriptions data into Observational Medical Outcome Partnership (OMOP) Common Data Model (CDM), together with challenges faced and solutions implemented. Materials and Methods: We used Estonian national health databases that store almost all residents' claims, prescriptions, and EHR records. To develop and demonstrate the transformation process of Estonian health data to OMOP CDM, we used a 10% random sample of the Estonian population (n = 150 824 patients) from 2012 to 2019 (MAITT dataset). For the sample, complete information from all 3 databases was converted to OMOP CDM version 5.3. The validation was performed using open-source tools. Results: In total, we transformed over 100 million entries to standard concepts using standard OMOP vocabularies with the average mapping rate 95%. For conditions, observations, drugs, and measurements, the mapping rate was over 90%. In most cases, SNOMED Clinical Terms were used as the target vocabulary. Discussion: During the transformation process, we encountered several challenges, which are described in detail with concrete examples and solutions. Conclusion: For a representative 10% random sample, we successfully transferred complete records from 3 national health databases to OMOP CDM and created a reusable transformation process. Our work helps future researchers to transform linked databases into OMOP CDM more efficiently, ultimately leading to better real-world evidence.

A High-Efficiency Deep Blue Emitter for OLEDs with a New Dual-Core Structure Incorporating ETL Characteristics.

In this study, we introduced the weak electron-accepting oxazole derivative 4,5-diphenyl-2-(4-(4,4,5,5-tetramethyl-1,3,2-dioxaborolan-2-yl)phenyl)oxazole (TPO) into both anthracene and pyrene moieties of a dual core structure. Ultimately, we developed 2-(4-(6-(anthracen-9-yl)pyren-1-yl)phenyl)-4,5-diphenyloxazole (AP-TPO) as the substitution on the second core, pyrene, and 4,5-diphenyl-2-(4-(10-(pyren-1-yl)anthracen-9-yl)phenyl)oxazole (TPO-AP) as the substitution on the first core, anthracene. Both materials exhibited maximum photoluminescence wavelengths at 433 and 443 nm in solution and emitted deep blue light with high photoluminescence quantum yields of 82% and 88%, respectively. When used as the emitting layer in non-doped devices, TPO-AP outperformed AP-TPO, achieving a current efficiency of 5.49 cd/A and an external quantum efficiency of 4.26% in electroluminescence. These materials introduce a new category of deep blue emitters in the organic light-emitting diodes field, combining characteristics related to the electron transport layer.

Tailoring the Size and Shape of ZnO Nanoparticles for Enhanced Performance of OLED Device.

We synthesized zinc oxide nanoparticles (ZnO NPs) by meticulously controlling both temperature and reaction times, allowing us to fine-tune their crystalline properties, morphology, and particle dimensions. This analysis confirmed the existence of a mixture of rod and sphere shapes (ZnO-I), including rod-shaped NPs with an average size of 14.8 nm × 5.2 nm and spherical NPs with an average diameter of 5.27 nm. We subsequently incorporated these synthesized ZnO NPs into organic light-emitting diode (OLED) devices for red, green, and blue colors, utilizing them as the electron injection layer through a solution-based process. The green OLED device using ZnO-I exhibited a promising current efficiency of 4.02 cd/A and an external quantum efficiency of 1.47%.

Adjustment of p-value expression to ontology using machine learning for genetic prediction, prioritization, interaction, and its validation in glomerular disease.

The results of gene expression analysis based on p-value can be extracted and sorted by their absolute statistical significance and then applied to multiple similarity scores of their gene ontology (GO) terms to promote the combination and adjustment of these scores as essential predictive tasks for understanding biological/clinical pathways. The latter allows the possibility to assess whether certain aspects of gene function may be associated with other varieties of genes, to evaluate regulation, and to link them into networks that prioritize candidate genes for classification by applying machine learning techniques. We then detect significant genetic interactions based on our algorithm to validate the results. Finally, based on specifically selected tissues according to their normalized gene expression and frequencies of occurrence from their different biological and clinical inputs, a reported classification of genes under the subject category has validated the abstract (glomerular diseases) as a case study.

Outstanding conversion efficiency of 38.39% from a Perovskite/CIGS tandem PV cell: A synergic optimization through computational modeling.

An all-inorganic lead-free tandem PV cell consisting of two sub-cells CsSn0.5Ge0.5I3 (perovskite) based top cell/CIGS-based bottom cell has been designed, simulated, and optimized by varying the thickness of pertinent layers utilizing the SCAPS-1D simulator. In the top sub-cell, a wide bandgap lead-free inorganic CsSn0.5Ge0.5I3 perovskite is inserted as the primary absorber layer because of its distinctive characteristics with an ETL of ZnO, which is recognized for its high electron mobility & absorption coefficient, and an HTL of NiO to offer increased hole mobility with good chemical-durability. For the bottom sub-cell, we have selected p-type CIGS as the absorber with Spiro-OMeTAD as the apposite HTL to provide suitable offsets of valence and conduction band distribution and TiO2 as ETL to offer low-cost, low-ecotoxicity, excellent optical properties, and chemical-stability and thus offers improved efficiency of the overall tandem structure. In the beginning, the two sub-cells were simulated independently; the top sub-cell was simulated under the standard spectrum of AM1.5G, while the bottom sub-cell was optimized using a filtered spectrum. Thereafter, the current matching point of both cells was attained by optimizing the absorber layer thicknesses. Finally, our computational modeling and simulation results offer the optimized cell structure revealing an outstanding overall 38.39% power conversion efficiency (PCE), Fill Factor (FF) of 83.4%, open-circuit voltage (VOC) of 2.48 V, and short-circuit current density (Jsc) of 18.64 mA cm-2. The proposed tandem structure's performance matrices outperform those stated in the most recent literature. These outcomes of the proposed structure are expected to facilitate the development and production of a low-cost and highly effective inorganic perovskite Tandem PV cell in the future.