Development of Fly Ash Derived Sorbents to Capture CO2 from Flue Gas of Power Plants

This research program focused on the development of fly ash derived sorbents to capture CO{sub 2} from power plant flue gas emissions. The fly ash derived sorbents developed represent an affordable alternative to existing methods using specialized activated carbons and molecular sieves, that tend to be very expensive and hinder the viability of the CO{sub 2} sorption process due to economic constraints. Under Task 1 'Procurement and characterization of a suite of fly ashes', 10 fly ash samples, named FAS-1 to -10, were collected from different combustors with different feedstocks, including bituminous coal, PRB coal and biomass. These samples presented a wide range of LOI value from 0.66-84.0%, and different burn-off profiles. The samples also spanned a wide range of total specific surface area and pore volume. These variations reflect the difference in the feedstock, types of combustors, collection hopper, and the beneficiation technologies the different fly ashes underwent. Under Task 2 'Preparation of fly ash derived sorbents', the fly ash samples were activated by steam. Nitrogen adsorption isotherms were used to characterize the resultant activated samples. The cost-saving one-step activation process applied was successfully used to increase the surface area and pore volume of all the fly ash samples. The activated samples present very different surface areas and pore volumes due to the range in physical and chemical properties of their precursors. Furthermore, one activated fly ash sample, FAS-4, was loaded with amine-containing chemicals (MEA, DEA, AMP, and MDEA). The impregnation significantly decreased the surface area and pore volume of the parent activated fly ash sample. Under Task 3 'Capture of CO{sub 2} by fly ash derived sorbents', sample FAS-10 and its deashed counterpart before and after impregnation of chemical PEI were used for the CO{sub 2} adsorption at different temperatures. The sample FAS-10 exhibited a CO{sub 2} adsorption capacity of 17.5mg/g at 30 C, and decreases to 10.25mg/g at 75 C, while those for de-ashed counterpart are 43.5mg/g and 22.0 mg/g at 30 C and 75 C, respectively. After loading PEI, the CO{sub 2} adsorption capacity increased to 93.6 mg/g at 75 C for de-ashed sample and 62.1 mg/g at 75 C for raw fly ash sample. The activated fly ash, FAS-4, and its chemical loaded counterparts were tested for CO{sub 2} capture capacity. The activated carbon exhibited a CO{sub 2} adsorption capacity of 40.3mg/g at 30 C that decreased to 18.5mg/g at 70 C and 7.7mg/g at 120 C. The CO{sub 2} adsorption capacity profiles changed significantly after impregnation. For the MEA loaded sample the capacity increased to 68.6mg/g at 30 C. The loading of MDEA and DEA initially decreased the CO{sub 2} adsorption capacity at 30 C compared to the parent sample but increased to 40.6 and 37.1mg/g, respectively, when the temperature increased to 70 C. The loading of AMP decrease the CO{sub 2} adsorption capacity compared to the parent sample under all the studied temperatures. Under Task 4 'Comparison of the CO{sub 2} capture by fly ash derived sorbents with commercial sorbents', the CO{sub 2} adsorption capacities of selected activated fly ash carbons were compared to commercial activated carbons. The CO{sub 2} adsorption capacity of fly ash derived activated carbon, FAS-4, and its chemical loaded counterpart presented CO{sub 2} capture capacities close to 7 wt%, which are comparable to, and even better than, the published values of 3-4%

TRUSTLLM:Trustworthiness in Large Language Models

Large language models (LLMs) have gained considerable attention for their excellent natural language processing capabilities. Nonetheless, these LLMs present many challenges, particularly in the realm of trustworthiness. This paper introduces TRUSTLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TRUSTLLM, consisting of over 30 datasets. Our findings firstly show that in general trustworthiness and capability (i.e., functional effectiveness) are positively related. Secondly, our observations reveal that proprietary LLMs generally outperform most open-source counterparts in terms of trustworthiness, raising concerns about the potential risks of widely accessible open-source LLMs. However, a few open-source LLMs come very close to proprietary ones, suggesting that open-source models can achieve high levels of trustworthiness without additional mechanisms like moderator, offering valuable insights for developers in this field. Thirdly, it is important to note that some LLMs may be overly calibrated towards exhibiting trustworthiness, to the extent that they compromise their utility by mistakenly treating benign prompts as harmful and consequently not responding. Besides these observations, we've uncovered key insights into the multifaceted trustworthiness in LLMs. We emphasize the importance of ensuring transparency not only in the models themselves but also in the technologies that underpin trustworthiness. We advocate that the establishment of an AI alliance between industry, academia, and the open-source community to foster collaboration is imperative to advance the trustworthiness of LLMs. Our dataset, code, and toolkit will be available at § https://github.com/HowieHwong/TrustLLM and the leaderboard is released at https://trustllmbenchmark.github. io/TrustLLM-Website/.</p

TRUSTLLM:Trustworthiness in Large Language Models

Large language models (LLMs) have gained considerable attention for their excellent natural language processing capabilities. Nonetheless, these LLMs present many challenges, particularly in the realm of trustworthiness. This paper introduces TRUSTLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TRUSTLLM, consisting of over 30 datasets. Our findings firstly show that in general trustworthiness and capability (i.e., functional effectiveness) are positively related. Secondly, our observations reveal that proprietary LLMs generally outperform most open-source counterparts in terms of trustworthiness, raising concerns about the potential risks of widely accessible open-source LLMs. However, a few open-source LLMs come very close to proprietary ones, suggesting that open-source models can achieve high levels of trustworthiness without additional mechanisms like moderator, offering valuable insights for developers in this field. Thirdly, it is important to note that some LLMs may be overly calibrated towards exhibiting trustworthiness, to the extent that they compromise their utility by mistakenly treating benign prompts as harmful and consequently not responding. Besides these observations, we've uncovered key insights into the multifaceted trustworthiness in LLMs. We emphasize the importance of ensuring transparency not only in the models themselves but also in the technologies that underpin trustworthiness. We advocate that the establishment of an AI alliance between industry, academia, and the open-source community to foster collaboration is imperative to advance the trustworthiness of LLMs. Our dataset, code, and toolkit will be available at § https://github.com/HowieHwong/TrustLLM and the leaderboard is released at https://trustllmbenchmark.github. io/TrustLLM-Website/.</p

Genic evidence that gnetophytes are sister to all other seed plants

AbstractGnetophytes, comprising three relict genera, Gnetum, Welwitchia and Ephedra, are a morphologically diverse and enigmatic assemblage among seed plants. Despite recent progress on phylogenomic analyses or the insights from the recently decoded Gnetum genome, the relationship between gnetophytes and other seed plant lineages is still one of the outstanding, unresolved questions in plant sciences. Here, we showed that phylogenetic studies from nuclear genes support the hypothesis that places gnetophytes as sister to all other extant seed plants and so this hypothesis should not be ruled out according to phylogenetic inference based on nuclear genes. However, this extraordinarily difficult phylogenetic problem might never be solved by phylogenetic inference based gene tree under various artificial selection. Hence, we adopted a novel approach, comparing gene divergence among different lineages, to solve the conflicts by showing that gnetophytes actually did not gained a set of genes like the most recent common ancestor (MRCA) of other seed plants. This distinct gene evolution pattern could not be explained by random gene lost as in other seed plants but should be interpreted by the early divergence of gnetophytes from rest of seed plants. With such a placement, the gymnosperms are paraphyletic and there should be three distinct groups of living seed plants: gnetophytes, non-gnetophytes gymnosperms and angiosperms.</jats:p