Zero-Emission Cement Plants with Advanced Amine-Based CO2 Capture.

Decarbonization of the cement sector is essentially required to achieve carbon neutrality to combat climate change. Amine-based CO2 capture is a leading and practical technology to deeply remove CO2 from the cement industry, owing to its high retrofittability to existing cement plants and extensive engineering experience in industrial flue gas decarbonization. While research efforts have been made to achieve low-carbon cement with 90% CO2 removal, a net-zero-emission cement plant that will be required for a carbon neutrality society has not yet been investigated. The present study proposed an advanced amine-based CO2 capture system integrated with a cement plant to achieve net-zero CO2 emission by pushing the CO2 capture efficiency to 99.7%. Monoethanomaine (MEA) and piperazine/2-amino-2-methyl-1-propanol (PZ-AMP) amine systems, which are considered to be the first- and second-generation capture agents, respectively, were detailed investigated to deeply decarbonize the cement plant. Compared to MEA, the advanced PZ-AMP system exhibited excellent energy performance with a regeneration duty of ∼2.6 GJ/tonne CO2 at 99.7% capture, 39% lower than the MEA process. This enabled a low CO2 avoided cost of $72.0/tonne CO2, which was 18% lower than that of the MEA-based zero-emission process and even 16.2% lower than the standard 90% MEA process. Sensitivity analysis revealed that the zero-emission capture cost of the PZ-AMP system would be further reduced to below $56/tonne CO2 at a $4/GJ steam production cost, indicating its economic competitiveness among various CO2 capture technologies to achieve a zero-emission cement plant.

Latent space search based multimodal optimization with personalized edge-network biomarker for multi-purpose early disease prediction.

Considering that cancer is resulting from the comutation of several essential genes of individual patients, researchers have begun to focus on identifying personalized edge-network biomarkers (PEBs) using personalized edge-network analysis for clinical practice. However, most of existing methods ignored the optimization of PEBs when multimodal biomarkers exist in multi-purpose early disease prediction (MPEDP). To solve this problem, this study proposes a novel model (MMPDENB-RBM) that combines personalized dynamic edge-network biomarkers (PDENB) theory, multimodal optimization strategy and latent space search scheme to identify biomarkers with different configurations of PDENB modules (i.e. to effectively identify multimodal PDENBs). The application to the three largest cancer omics datasets from The Cancer Genome Atlas database (i.e. breast invasive carcinoma, lung squamous cell carcinoma and lung adenocarcinoma) showed that the MMPDENB-RBM model could more effectively predict critical cancer state compared with other advanced methods. And, our model had better convergence, diversity and multimodal property as well as effective optimization ability compared with the other state-of-art methods. Particularly, multimodal PDENBs identified were more enriched with different functional biomarkers simultaneously, such as tissue-specific synthetic lethality edge-biomarkers including cancer driver genes and disease marker genes. Importantly, as our aim, these multimodal biomarkers can perform diverse biological and biomedical significances for drug target screen, survival risk assessment and novel biomedical sight as the expected multi-purpose of personalized early disease prediction. In summary, the present study provides multimodal property of PDENBs, especially the therapeutic biomarkers with more biological significances, which can help with MPEDP of individual cancer patients.

Study on the Voltage Reference Noise at Sub-Millihertz Frequencies for Developing an Ultra-Stable Temperature Measurement Subsystem.

A temperature measurement subsystem (TMS) is a critical piece of infrastructure of the space gravitational wave detection platform, necessary for monitoring minuscule temperature changes at the level of 1µK/Hz1/2 within the electrode house, in the frequency range of 0.1mHz to 1Hz. The voltage reference (VR), a key component of the TMS, must possess low noise characteristics in the detection band to minimize the impact on temperature measurements. However, the noise characteristics of the voltage reference in the sub-millihertz range have not been documented yet and require further study. This paper reports a dual-channel measurement method for measuring the low-frequency noise of VR chips down to 0.1mHz. The measurement method makes use of a dual-channel chopper amplifier and an assembly thermal insulation box to achieve a normalized resolution of 3×10-7/Hz1/2@0.1mHz in the VR noise measurement. The seven best-performance VR chips documented at a common frequency range are tested. The results show that their noise at sub-millihertz frequencies can significantly differ from that around 1Hz.

The inverted pattern of circulating miR-221-3p and miR-222-3p associated with isolated low HDL-C phenotype.

BACKGROUND: We investigated the baseline characterization of cardiovascular disease (CVD)-derived circulating miR-221-3p/222-3p in isolated low HDL-C phenotype (ILHP) to enhance our understanding on their molecular pathological pattern prior to disease onset. METHODS: We screened 174 asymptomatic subjects with isolated low HDL-C phenotype (n = 88) and normal lipid phenotype (n = 86), and detected circulating levels of CVD-derived circulating miR-221-3p/222-3p using TaqMan miRNA Real-time PCR detection system. RESULTS: We found the inverted pattern of decreased circulating miR-221-3p (0.415 [0.249, 1.004] vs 0.658 [0.347, 1.534], p = 0.002) versus increased miR-222-3p levels (0.379 [0.101, 0.701] vs 0.156 [0.043, 0.407], p < 0.001) in ILHP. The baseline levels of circulating miR-221-3p and miR-222-3p are correlated with serum HDL-C levels (miR-221-3p: r = 0.306, p < 0.001; miR-222-3p: r = - 0.201, p = 0.008). Gender-based analysis showed female-specific elevation of circulating miR-221-3p in asymptomatic individual. Multiple logistic regression analysis showed that circulating miR-222-3p is robustly independent factor (adjusted OR = 8.42, 95%CI: 2.53-27.98, p < 0.001) and significantly improved the performance of the predictive clinical model distinguished ILHP from normal lipid phenotype (AUC: 0.816, 95%CI (0.754, 0.879) vs AUC: 0.771, 95%CI (0.702, 0.840); Z = 2.169, p = 0.030). Moreover, the increased original Ct ratio of miR-221-3p to miR-222-3p in male ILHP (1.003 [0.927, 1.063] vs 0.927 [0.858, 0.967], p < 0.001) significantly enhanced the ability to classify male ILHP compared with the male predictive clinical model (AUC: 0.851, 95%CI (0.770, 0.933) vs AUC: 0.759, 95%CI (0.659, 0.859); Z = 2.474, p < 0.05). CONCLUSIONS: The inverted pattern of circulating miR-221-3p and miR-222-3p are potentially clinically actionable signature for molecular pathology in isolated low HDL-C phenotype.

A molecular dynamic study on the dissociation mechanism of SI methane hydrate in inorganic salt aqueous solutions.

Gas hydrate is not only a potential energy resource, but also almost the biggest challenge in oil/gas flow assurance. Inorganic salts such as NaCl, KCl and CaCl2 are widely used as the thermodynamic inhibitor to reduce the risk caused by hydrate formation. However, the inhibition mechanism is still unclear. Therefore, molecular dynamic (MD) simulation was performed to study the dissociation of structure I (SI) methane hydrate in existence of inorganic salt aqueous solution on a micro-scale. The simulation results showed that, the dissociation became stagnant due to the presence of liquid film formed by the decomposed water molecules, and more inorganic ions could shorten the stagnation-time. The diffusion coefficients of ions and water molecules were the largest in KCl system. The structures of ion/H2O and H2O/H2O were the most compact in hydrate/NaCl system. The ionic ability to decompose hydrate cells followed the sequence of: Ca2+>2K+>2Cl->2Na+.