Mass Transfer Enhancement in Carbon Dioxide Gas Hydrate Formation for Effective Carbon Separation and Storage

Carbon dioxide (CO2) is widely acknowledged as a significant contributor to global warming. Hydrate-based carbon capture (HBCC) technology holds high potential in delivering cost-effective and environmentally friendly carbon capture solutions. However, the relatively severe formation conditions and low formation rate of gas hydrates limit its practical applications. This thesis focuses on the mass transfer enhancement methods for effective CO2 hydrate formation through experimental and numerical studies. The thermodynamic and kinetic promotion experiments on CO2 hydrate formation using chemical promoters are implemented in tetra-n-butyl ammonium bromide (TBAB) solution with surfactants. TBAB, as a thermodynamic promoter, can moderate hydrate phase equilibrium by forming CO2-TBAB semiclathrate hydrates. However, it decreases CO2 gas uptake yields. Three kinds of surfactants, namely anionic surfactant sodium dodecyl sulfate (SDS), cationic surfactant dodecyl-trimethylammonium chloride (DTAC), and non-ionic surfactant Tween 80 (T-80), are added in the system to increase the formation rate and offset the low gas uptake yields. Induction time, normalized gas uptake, split fraction and separation factor are the performance metrics. The results in TBAB systems show that the hydrate formation is most accelerated with the addition of SDS, but DTAC shows better CO2 separation performance. Similar results of rapid formation rate with the addition of non-ionic surfactant T-80 are also found. Analysis of variance is used to analyze the difference among experimental results, and a decision box is proposed to evaluate the performance of the systems studied. Compared with SDS and DTAC, 2000-ppm T-80 shows the best CO2 separation performance in semiclathrate hydrates. The mass transfer can also be enhanced by adding microparticles due to their considerable surface areas. The kinetic promotion experiments of CO2 hydrate formation are thus further studied in "dry water" and silica gel (SG) microparticles of different sizes. The experimental results reveal that "dry water" particles with 8-wt% silica has the highest normalized gas uptakes. However, "dry water" are broken after a repeat cycle. SDS and DTAC are added to the SG system to further enhance gas-water mass transfer. With the addition of surfactants in 100-nm SGs, SDS systems save up to 23.7%-49.3% time to achieve the same amount of gas uptake, while DTAC systems save 16% of the time. SGs show better stability and promotion effect than "dry water". A modified shrinking core model (SCM) is established to study the CO2 hydrate formation kinetics in both "dry water" particles and SG pores. It is the first model that integrates the effects of CO2 solubility, capillary effect, volume expansion, and heat transfer model. The hydrate formation in both pure CO2 and CO2/N2 gas mixtures are simulated to reveal the different roles of CO2 and N2 molecule diffusion and reaction in hydrate formation. In "dry water" particles, the water consumed through capillaries accounts for less than 10% of the total water consumed. The decoupled heat transfer model reveals that the instantaneous temperature gradient in the hydrate shell is of a small magnitude of 10-2 K m-1. In SG pores, the initial proportion of water consumed by capillary effect is only 1%-26.6%, but it can be up to 74.9% in small pores with surfactants. This work provides comprehensive insights into gas hydrate formation in both water systems and microparticles. It contributes a theoretical basis for the improvement of gas hydrate kinetics through mass transfer enhancement. The modeling strategies in this work can be applied to hydrate formation mechanisms in other porous materials

Indirect measurement of infrared absorption spectrum through thermal emission of meta-cavity array

Controlling thermal emission is essential for various infrared spectroscopy applications. Metasurfaces can be utilized to control multiple degrees of freedom of thermal emission, enabling the compact thermal emission materials and devices. Infrared spectroscopy such as FTIR (Fourier transform infrared spectroscopy), usually requires external infrared radiation source and complex spectroscopic devices for absorption spectrum measurement, which hinders the implementation of integrated compact and portable measurement equipment. Measuring absorption spectrum through the thermal emission of pixelated thermal emitter array can facilitate the integration and miniaturization of measurement setup, which is highly demanded for on-chip spectroscopy applications. Here, we experimentally demonstrate an integrated technology that allows for indirect measurement of the absorption spectrum through the thermal emission of meta-cavity array. This indirect measurement method opens a new avenue for compact infrared spectroscopy analysis.Comment: 14 pages, 9 figure

Efficient In-Context Learning in Vision-Language Models for Egocentric Videos

Recent advancements in text-only large language models (LLMs) have highlighted the benefit of in-context learning for adapting to new tasks with a few demonstrations. However, extending in-context learning to large vision-language models (VLMs) using a huge amount of naturalistic vision-language data has shown limited success, particularly for egocentric videos, due to high data collection costs. We propose a novel training method $\mathbb{E}$fficient $\mathbb{I}$n-context $\mathbb{L}$earning on $\mathbb{E}$gocentric $\mathbb{V}$ideos ($\mathbb{EILEV}$), which elicits in-context learning in VLMs for egocentric videos without requiring massive, naturalistic egocentric video datasets. $\mathbb{EILEV}$ involves architectural and training data adaptations to allow the model to process contexts interleaved with video clips and narrations, sampling of in-context examples with clusters of similar verbs and nouns, use of data with skewed marginal distributions with a long tail of infrequent verbs and nouns, as well as homonyms and synonyms. Our evaluations show that $\mathbb{EILEV}$-trained models outperform larger VLMs trained on a huge amount of naturalistic data in in-context learning. Furthermore, they can generalize to not only out-of-distribution, but also novel, rare egocentric videos and texts via in-context learning, demonstrating potential for applications requiring cost-effective training, and rapid post-deployment adaptability. Our code and demo are available at \url{https://github.com/yukw777/EILEV}.Comment: 10 pages, LaTeX; added acknowledgment

Phytochemical Profiles and Antioxidant and Antimicrobial Activities of the Leaves of Zanthoxylum bungeanum

The ethanol crude extracts (ECE) and their subfractions from Zanthoxylum bungeanum leaves were prepared and their phytochemical profiles and antioxidant and antimicrobial activities were investigated. Moreover, the effective HPLC procedure for simultaneous quantification of twelve compounds in Z. bungeanum leaves was established. The correlation between the phytochemicals and antioxidant activity was also discussed. The ethyl acetate fraction (EAF) had the highest total phenolic (97.29 mmol GAE/100 g) and flavonoid content (67.93 mmol QE/100 g), while the greatest total alkaloid content (4.39 mmol GAE/100 g) was observed in the chloroform fraction (CF). Twelve compounds were quantified by RP-HPLC assay. EAF exhibited the highest content of quercitrin, kaempferol-3-rhamnoside, quercetin, sesamin, and nitidine chloride (125.21, 54.95, 24.36, 26.24, and 0.20 mg/g); acetone fraction (AF) contained the highest content of chlorogenic acid, rutin, hyperoside, and trifolin (5.87, 29.94, 98.33, and 31.24 mg/g), while kaempferol-3-rhamnoside, xanthyletin, and sesamin were rich in CF. EAF and AF exhibited significant DPPH, ABTS radical scavenging abilities and reducing power (FRAP), whereas CF exhibited significant antifungal activity. Moreover, EAF also showed stronger antibacterial activity. In conclusion, Z. bungeanum leaves have health benefits when consumed and could be served as an accessible source for production of functional food ingredients and medicinal exploration

OpenFE: Automated Feature Generation beyond Expert-level Performance

The goal of automated feature generation is to liberate machine learning experts from the laborious task of manual feature generation, which is crucial for improving the learning performance of tabular data. The major challenge in automated feature generation is to efficiently and accurately identify useful features from a vast pool of candidate features. In this paper, we present OpenFE, an automated feature generation tool that provides competitive results against machine learning experts. OpenFE achieves efficiency and accuracy with two components: 1) a novel feature boosting method for accurately estimating the incremental performance of candidate features. 2) a feature-scoring framework for retrieving effective features from a large number of candidates through successive featurewise halving and feature importance attribution. Extensive experiments on seven benchmark datasets show that OpenFE outperforms existing baseline methods. We further evaluate OpenFE in two famous Kaggle competitions with thousands of data science teams participating. In one of the competitions, features generated by OpenFE with a simple baseline model can beat 99.3\% data science teams. In addition to the empirical results, we provide a theoretical perspective to show that feature generation is beneficial in a simple yet representative setting. The code is available at https://github.com/ZhangTP1996/OpenFE.Comment: 23 pages, 3 figure