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Commercially available formulations of the popular conductive polymer, poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) are aqueous dispersions that require the addition of secondary dopants such as dimethyl sulphoxide (DMSO) or ethylene glycol (EG) for fabricated films to have the desired levels of conductivity. CleviosTM F HC Solar, a formulation of PEDOT:PSS produced by Heraeus, GmbH, achieves over 500 S/cm without these secondary dopants. This work studies whether secondary dopants such as DMSO have any additional effect on this type of PEDOT:PSS. The temperature dependencies of the conductivity of F HC Solar spin-coated thin films measured using a four-probe method seem to exhibit different charge transport properties compared with secondary doped PH1000. Observations made using atomic force microscopy (AFM) show that different concentrations of DMSO affect the orientation of the PEDOT domains in the thin film. These morphological changes cause room temperature conductivity to reduce from 640 S/cm in pristine films to as low as 555 S/cm after adding 7 wt% of DMSO along the film. Such tuning may prove useful in future applications of PEDOT:PSS, such as nanoprobes, transistors and hybrid solar cells.

PEDOT:PSS/Si hybrid photovoltaic cells have been attracting attention as a potential way to simplify the manufacturing process and democratize solar energy production. Control of the PEDOT/Si interface is also one of the primary ways to ensure the improved performance and lifetimes of multijunction devices, such as perovskite/Si tandem solar cells. In this work, the effects of the interfacial silicon oxide layer were investigated by creating a novel and controllable neutral beam oxide interlayer with different thicknesses. A novel self-doped PEDOT (S-PEDOT) was used to improve interfacial contact and avoid the secondary doping of PEDOT:PSS. X-ray photoelectron spectroscopy (XPS) showed that the saturation of interfacial silicon atoms in SiOx-Si bonds as well as a very thin, (~1 nm) damage-free oxide interlayer were the keys to maintaining good passivation with a high tunneling current. Lifetime measurements also showed that the interlayers with the most SiO2 content degraded the least. The degradation of the devices was due to the continued growth of the oxide layer through reactions with silicon sub-oxides and the degradation of S-PEDOT.

This research studies the occurrence of data breaches in healthcare provider settings regarding patient data. Using visual analytics and data visualization tools, we study the distribution of healthcare breaches by state. We review the main causes and types of breaches, as well as their impact on both providers and patients. The research shows a range of data breach victims. Network servers are the most popular location for common breaches, such as hacking and information technology (IT) incidents, unauthorized access, theft, loss, and improper disposal. We offer proactive recommendations to prepare for a breach. These include, but are not limited to, regulatory compliance, implementing policies and procedures, and monitoring network servers. Unfortunately, the results indicate that the probability of data breaches will continue to rise.

While data breaches are a frequent and universal phenomenon, the characteristics and dimensions of data breaches are unexplored. In this novel exploratory research, we apply machine learning (ML) and text analytics to a comprehensive collection of data breach litigation cases to extract insights from the narratives contained within these cases. Our analysis shows stakeholders (e.g., litigants) are concerned about major topics related to identity theft, hacker, negligence, FCRA (Fair Credit Reporting Act), cybersecurity, insurance, phone device, TCPA (Telephone Consumer Protection Act), credit card, merchant, privacy, and others. The topics fall into four major clusters: “phone scams”, “cybersecurity”, “identity theft”, and “business data breach”. By utilizing ML, text analytics, and descriptive data visualizations, our study serves as a foundational piece for comprehensively analyzing large textual datasets. The findings hold significant implications for both researchers and practitioners in cybersecurity, especially those grappling with the challenges of data breaches.

Background Certain echocardiographic parameters may serve as early predictors of adverse events in patients with hemodynamically compromising pulmonary embolism (PE). Methods and Results An observational analysis was conducted for patients with acute pulmonary embolism evaluated by a Pulmonary Embolism Response Team (PERT) between 2014 and 2020. The performance of clinical prediction algorithms including the Pulmonary Embolism Severity Index and Carl Bova score were compared using a ratio of right ventricle and left ventricle hemodynamics by dividing the pulmonary artery systolic pressure by the left ventricle stroke volume. The primary outcome of in‐hospital mortality, cardiac arrest, and the need for advanced therapies was evaluated by univariate and multivariable analyses. Of the 343 patients meeting the inclusion criteria, 215 had complete data. Pulmonary artery systolic pressure/left ventricle stroke volume was a clear predictor of the primary end point (odds ratio [OR], 2.31; P=0.005), performing as well or better than the Pulmonary Embolism Severity Index (OR, 1.43; P=0.06) or the Bova score (OR, 1.28; P=0.01). Conclusions This study is the first study to demonstrate the utility of early pulmonary artery systolic pressure/left ventricle stroke volume in predicting adverse clinical events in patients with acute pulmonary embolism. Pulmonary artery systolic pressure/left ventricle stroke volume may be a surrogate marker of ventricular asynchrony in high‐risk pulmonary embolism and should be prognostically evaluated.