CO2 Capture by Aqueous Solution Containing Mixed Alkanolamines and Diethylene Glycol in a Rotating Packed Bed

AbstractIn this study, an effective absorbent, 23.5% diethylenetriamine (DETA)/19.6% piperazine (PZ)/37.7% diethylene glycol (DEG)/19.2% H2 O, was proposed to capture CO2 from a nitrogen gas stream containing 10% of CO2 in a rotating packed bed (RPB). The addition of DEG could improve the solubility of PZ in absorbent, and there was no precipitation observed in CO2 - rich solution after CO2 absorption for the proposed absorbent. The regeneration energy could be reduced because the heat capacity and vapor pressure of DEG are lower than that of water. The higher gas-liquid contact area and mass transfer rate as well as the smaller size were observed in a RPB as compared with a packed bed to achieve the same CO2 capture efficiency

Stellar Variability in a Forming Massive Star Cluster

We present a near-infrared (NIR) variability analysis for an 6\arcmin $\times$ 6\arcmin region, which encompasses the massive protocluster G286.21+0.17. The total sample comprises more than 5000 objects, of which 562 show signs of a circumstellar disk based on their infrared colors. The data includes HST observations taken in two epochs separated by 3 years in the F110W and F160W bands. 363 objects (7% of the sample) exhibit NIR variability at a significant level (Stetson index >1.7), and a higher variability fraction (14%) is found for the young stellar objects (YSOs) with disk excesses. We identified 4 high amplitude (>0.6 mag) variables seen in both NIR bands. Follow up and archival observations of the most variable object in this survey (G286.2032+0.1740) reveal a rising light curve over 8 years from 2011 to 2019, with a K band brightening of 3.5 mag. Overall the temporal behavior of G286.2032+0.1740 resembles that of typical FU Ori objects, however its pre-burst luminosity indicates it has a very low mass ($<0.12\:M_\odot$), making it an extreme case of an outburst event that is still ongoing.Comment: 13 pages, 8 figures, accepted by Ap

Chromatin Laser Imaging Reveals Abnormal Nuclear Changes for Early Cancer Detection

We developed and applied rapid scanning laser-emission microscopy to detect abnormal changes in cell nuclei for early diagnosis of cancer and cancer precursors. Regulation of chromatins is essential for genetic development and normal cell functions, while abnormal nuclear changes may lead to many diseases, in particular, cancer. The capability to detect abnormal changes in apparently normal tissues at a stage earlier than tumor development is critical for cancer prevention. Here we report using LEM to analyze colonic tissues from mice at-risk for colon cancer by detecting prepolyp nuclear abnormality. By imaging the lasing emissions from chromatins, we discovered that, despite the absence of observable lesions, polyps, or tumors under stereoscope, high-fat mice exhibited significantly lower lasing thresholds than low-fat mice. The low lasing threshold is, in fact, very similar to that of adenomas and is caused by abnormal cell proliferation and chromatin deregulation that can potentially lead to cancer. Our findings suggest that conventional methods, such as colonoscopy, may be insufficient to reveal hidden or early tumors under development. We envision that this work will provide new insights into LEM for early tumor detection in clinical diagnosis and fundamental biological and biomedical research of chromatin changes at the biomolecular level of cancer development

Sparse Fr\'echet Sufficient Dimension Reduction with Graphical Structure Among Predictors

Fr\'echet regression has received considerable attention to model metric-space valued responses that are complex and non-Euclidean data, such as probability distributions and vectors on the unit sphere. However, existing Fr\'echet regression literature focuses on the classical setting where the predictor dimension is fixed, and the sample size goes to infinity. This paper proposes sparse Fr\'echet sufficient dimension reduction with graphical structure among high-dimensional Euclidean predictors. In particular, we propose a convex optimization problem that leverages the graphical information among predictors and avoids inverting the high-dimensional covariance matrix. We also provide the Alternating Direction Method of Multipliers (ADMM) algorithm to solve the optimization problem. Theoretically, the proposed method achieves subspace estimation and variable selection consistency under suitable conditions. Extensive simulations and a real data analysis are carried out to illustrate the finite-sample performance of the proposed method

Full analytic expression of overlap reduction function for gravitational wave background with pulsar timing arrays

Pulsar timing array (PTA) is expected to detect gravitational wave background (GWB) in the nanohertz band within the next decade. This provides an opportunity to test the gravity theory and cosmology. A typical data analysis method to detect GWB is cross-correlation analysis. The overlap reduction function (ORF) plays an important role in the correlation data analysis of GWB. The present approach to dealing with the intricate integration in ORF is to use short-wave approximation to drop out the tricky terms. In this paper, we provide the full analytic expression of the ORF for PTA without any approximation for all possible polarizations allowed by modifications of general relativity. Compared with the numerical simulation and short-wave approximation, our results are more efficient and widely applicable. Especially for the scalar-longitudinal mode where the short-wave approximation is not available, our analytical expression is particularly significant