Ionic Liquids for the Separation of Fluorocarbon Refrigerant Mixtures.

This review discusses the research being performed on ionic liquids for the separation of fluorocarbon refrigerant mixtures. Fluorocarbon refrigerants, invented in 1928 by Thomas Midgley Jr., are a unique class of working fluids that are used in a variety of applications including refrigeration. Fluorocarbon refrigerants can be categorized into four generations: chlorofluorocarbons, hydrochlorofluorocarbons, hydrofluorocarbons, and hydrofluoroolefins. Each generation of refrigerants solved a key problem from the previous generation; however, each new generation has relied on more complex mixtures that are often zeotropic, near azeotropic, or azeotropic. The complexity of the refrigerants used and the fact that many refrigerants form azeotropes when mixed makes handling the refrigerants at end of life extremely difficult. Today, less than 3% of refrigerants that enter the market are recycled. This is due to a lack of technology in the refrigerant reclaim market that would allow for these complex, azeotropic refrigerant mixtures to be separated into their components in order to be effectively reused, recycled, and if needed repurposed. As the market for recovering and reclaiming refrigerants continues to grow, there is a strong need for separation technology. Ionic liquids show promise for separating azeotropic refrigerant mixtures as an entrainer in extractive distillation process. Ionic liquids have been investigated with refrigerants for this application since the early 2000s. This review will provide a comprehensive summary of the physical property measurements, equations of state modeling, molecular simulations, separation techniques, and unique materials unitizing ionic liquids for the development of an ionic-liquid-based separation process for azeotropic refrigerant mixtures.

Ecological Momentary Assessment of Cognition in Clinical and Community Samples: Reliability and Validity Study.

BACKGROUND: The current methods of evaluating cognitive functioning typically rely on a single time point to assess and characterize an individual's performance. However, cognitive functioning fluctuates within individuals over time in relation to environmental, psychological, and physiological contexts. This limits the generalizability and diagnostic utility of single time point assessments, particularly among individuals who may exhibit large variations in cognition depending on physiological or psychological context (eg, those with type 1 diabetes [T1D], who may have fluctuating glucose concentrations throughout the day). OBJECTIVE: We aimed to report the reliability and validity of cognitive ecological momentary assessment (EMA) as a method for understanding between-person differences and capturing within-person variation in cognition over time in a community sample and sample of adults with T1D. METHODS: Cognitive performance was measured 3 times a day for 15 days in the sample of adults with T1D (n=198, recruited through endocrinology clinics) and for 10 days in the community sample (n=128, recruited from TestMyBrain, a web-based citizen science platform) using ultrabrief cognitive tests developed for cognitive EMA. Our cognitive EMA platform allowed for remote, automated assessment in participants' natural environments, enabling the measurement of within-person cognitive variation without the burden of repeated laboratory or clinic visits. This allowed us to evaluate reliability and validity in samples that differed in their expected degree of cognitive variability as well as the method of recruitment. RESULTS: The results demonstrate excellent between-person reliability (ranging from 0.95 to 0.99) and construct validity of cognitive EMA in both the sample of adults with T1D and community sample. Within-person reliability in both samples (ranging from 0.20 to 0.80) was comparable with that observed in previous studies in healthy older adults. As expected, the full-length baseline and EMA versions of TestMyBrain tests correlated highly with one another and loaded together on the expected cognitive domains when using exploratory factor analysis. Interruptions had higher negative impacts on accuracy-based outcomes (ß=-.34 to -.26; all P values <.001) than on reaction time-based outcomes (ß=-.07 to -.02; P<.001 to P=.40). CONCLUSIONS: We demonstrated that ultrabrief mobile assessments are both reliable and valid across 2 very different clinic versus community samples, despite the conditions in which cognitive EMAs are administered, which are often associated with more noise and variability. The psychometric characteristics described here should be leveraged appropriately depending on the goals of the cognitive assessment (eg, diagnostic vs everyday functioning) and the population being studied.

Construction of endothelial cell-related and autophagy-related prognostic models for hepatocellular carcinoma based on single-cell data.

BACKGROUND: We aimed to develop an autophagy-related prognostic model with single-cell RNA sequencing (ScRNA-Seq) data for hepatocellular carcinoma (HCC) patients. METHODS: ScRNA-Seq datasets of HCC patients were analyzed by Seurat. The expression of genes involved in canonical and noncanonical autophagy pathways in scRNA-seq data was also compared. Cox regression was applied to construct an AutRG risk prediction model. Subsequently, we examined the characteristics of AutRG high-risk and low-risk group patients. RESULTS: Six major cell types (hepatocytes, myeloid cells, T/NK cells, B cells, fibroblast cells, and endothelial cells) were identified in the scRNA-Seq dataset. The results showed that most of the canonical and noncanonical autophagy genes were highly expressed in hepatocytes, with the exception of MAP 1LC3B, SQSTM1, MAP 1LC3A, CYBB, and ATG3. Six AutRG risk prediction models originating from different cell types were constructed and compared. The AutRG prognostic signature (GAPDH, HSP90AA1, and TUBA1C) in endothelial cells had the best overall performance for predicting the overall survival of HCC patients, with 1-year, 3-year, and 5-year AUCs equal to 0.758, 0.68, and 0.651 in the training cohort and 0.760, 0.796, and 0.840 in the validation cohort, respectively. The different tumor mutation burden, immune infiltration, and gene set enrichment characteristics of the AutRG high-risk and low-risk group patients were identified. CONCLUSION: We constructed an endothelial cell-related and autophagy-related prognostic model of HCC patients using the ScRNA-Seq dataset for the first time. This model demonstrated the good calibration ability of HCC patients and provided a new understanding of the evaluation of prognosis.