Energy-related CO₂ emission accounts and datasets for 40 emerging economies in 2010-2019

Since 2000, CO2 emissions from emerging economies have outstripped those of developed economies. To limit global warming to under 1.5gg C by 2100, over 100 emerging economies have proposed net-zero carbon targets. Yet the supportive data are lacking-no inventory of CO2 emission outlines detailed sources by sector or distribution at the subnational level for these economies. Here, we redress the balance by establishing a dataset for an energy-related CO2 emission inventory that covers 47 sectors and eight energy types in 40 emerging economies (10.5281/zenodo.7309360, Cui et al., 2021). Their emissions, growing rapidly by 3.0g%gyr-1, reached 7.5gGt in 2019 and were sourced primarily in coal and oil (34.6g% and 28.1g%, respectively) and consumed by the power and transportation sectors. Meanwhile, among African countries in this group, biomass combustion was responsible for 34.7g%-96.2g% of emissions. Our dataset fills a data gap by providing a detailed, robust emission accounting baseline for emerging economies-an advance that will support emission reduction policymaking at global, national, and subnational levels.</p

Lemur: Harmonizing Natural Language and Code for Language Agents

We introduce Lemur and Lemur-Chat, openly accessible language models optimized for both natural language and coding capabilities to serve as the backbone of versatile language agents. The evolution from language chat models to functional language agents demands that models not only master human interaction, reasoning, and planning but also ensure grounding in the relevant environments. This calls for a harmonious blend of language and coding capabilities in the models. Lemur and Lemur-Chat are proposed to address this necessity, demonstrating balanced proficiencies in both domains, unlike existing open-source models that tend to specialize in either. Through meticulous pre-training using a code-intensive corpus and instruction fine-tuning on text and code data, our models achieve state-of-the-art averaged performance across diverse text and coding benchmarks among open-source models. Comprehensive experiments demonstrate Lemur's superiority over existing open-source models and its proficiency across various agent tasks involving human communication, tool usage, and interaction under fully- and partially- observable environments. The harmonization between natural and programming languages enables Lemur-Chat to significantly narrow the gap with proprietary models on agent abilities, providing key insights into developing advanced open-source agents adept at reasoning, planning, and operating seamlessly across environments. https://github.com/OpenLemur/Lemu

An Efficient Algorithm for Solving Minimum Cost Flow Problem with Complementarity Slack Conditions

This paper presents an algorithm for solving a minimum cost flow (MCF) problem with a dual approach. The algorithm holds the complementary slackness at each iteration and finds an augmenting path by updating node potential iteratively. Then, flow can be augmented at the original network. In contrast to other popular algorithms, the presented algorithm does not find a residual network, nor find a shortest path. Furthermore, our algorithm holds information of node potential at each iteration, and we update node potential within finite iterations for expanding the admissible network. The validity of our algorithm is given. Numerical experiments show that our algorithm is an efficient algorithm for the MCF problem, especially for the network with a small interval of cost of per unit flow

Effect of water presence on choline chloride-2urea ionic liquid and coating platings from the hydrated ionic liquid

In the present study, hygroscopicity of the choline chloride-urea (ChCl-2Urea) ionic liquid (IL) was confirmed through Karl-Fisher titration examination, indicating that the water content in the hydrated ChCl-2Urea IL was exposure-time dependent and could be tailored by simple heating treatment. The impact of the absorbed water on the properties of ChCl-2Urea IL, including viscosity, electrical conductivity, electrochemical window and chemical structure was investigated. The results show that water was able to dramatically reduce the viscosity and improve the conductivity, however, a broad electrochemical window could be persisted when the water content was below ~6 wt.%. These characteristics were beneficial for producing dense and compact coatings. Nickel (Ni) coatings plating from hydrated ChCl-2Urea IL, which was selected as an example to show the effect of water on the electroplating, displayed that a compact and corrosion-resistant Ni coating was plated from ChCl-2Urea IL containing 6 wt.% water doped with 400 mg/L NA at a moderate temperature. As verified by FTIR analysis, the intrinsic reason could be ascribed that water was likely linked with urea through strong hydrogen bond so that the water decomposition was suppressed during plating. Present study may provide a reference to prepare some similar water-stable ILs for plating

Enhanced photocatalytic capability of SiO2/CQD nanocomposites

It is also well known that naked SiO2 is not an efficient photocatalyst due to its relatively large band gap, which could only absorb short-wave ultraviolet light. In this report, SiO2/carbon quantum dot (SiO2/CQD, about 4 nm) nanocomposites were successfully prepared by one-step thermal method using tetraethylorthosilicate (TEOS) as the source of both silicon and carbon. The nanocomposites were characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS). These nanocomposites exhibit higher photocatalytic activity for rhodamine B (RhB) degradation than C-doped SiO2 and pure SiO2 nanoparticles under near-UV light irradiation at room temperature in air. In the present catalyst system, CQD play two important roles for the enhanced photocatalytic activity of SiO2/CQD nanocomposites. It is expected that these novel, environmentally friendly and highly photoactive SiO2/CQD nanocomposites may promote practical applications of photocatalysts and provide an effective approach to high-efficiency complex catalyst design

Preparation and Adsorption Properties of Magnetic Molecularly Imprinted Polymers for Selective Recognition of 17&beta;-Estradiol

In this paper, magnetic molecularly imprinted polymers (MMIPs) were fabricated on the surface of Fe3O4 by surface molecular imprinting technology, which can selectively adsorb 17&beta;-estradiol (E2). The optimized experiments demonstrated that MMIPs possessed the best adsorption capacity when methanol was used as the solvent and MAA was used as the crosslinking agent, with a molar ratio of E2: MMA: EGDMA as 1:4:50. SEM, FTIR, and XRD were employed to investigate the morphologies of MMIPs and the results demonstrated that the MMIPs that can selectively adsorb E2 were successfully prepared on Fe3O4 particles. The adsorption experiments showed that 92.1% of E2 was adsorbed by the MMIPs, which is higher than the magnetic non-molecularly imprinted polymers (MNIPs). The Freundlich isotherm model was more suitable to describe the adsorption process of E2 by MMIPs. Meanwhile, MMIPs had a better recognition ability for E2 and its structural analogs such as estrone and estriol. The MMIPs still had good adsorption performance after methanol regeneration five times. The prepared MMIPs had the advantages of efficient adsorption ability and high reusability, so they can be applied for selective recognition and removal of E2

Emission accounting and drivers in Central Asian countries

Emerging countries are at the frontier of climate change actions, and carbon emissions accounting provides a quantifiable measure of the environmental impact of economic activities, which allows for comparisons of emissions across different entities. However, currently there is no study covering detailed emissions inventories for emerging countries in Central Asian. This paper compiles detailed and accurate carbon emissions inventories in several Central Asian countries (i.e., Kazakhstan, Kyrgyzstan, Pakistan, Palestine, Tajikistan, and Uzbekistan) during the period 2010–2020. Using the IPCC administrative territorial approach, we for the first time compile their emissions inventories in 47 economic sectors and five energy categories. Moreover, we also investigate decoupling status based on Tapio decoupling model and examine emissions driving factors based on the index decomposition analysis method. The primary results illustrate that carbon emissions in Central Asian countries are increasing with huge differences. Decoupling results highlight that most of the sample countries still need more effort to decouple the economy and emissions except that Pakistan achieves an ideal strong decoupling state. The results of the decomposition indicate that the economy and population both raise emissions, while energy intensity and carbon intensity are negative drivers in some countries. We propose practical policy implications for decarbonization and energy transition roadmap in Central Asian countries.</p

Preparation and Adsorption Properties of Magnetic Molecularly Imprinted Polymers for Selective Recognition of 17β-Estradiol

In this paper, magnetic molecularly imprinted polymers (MMIPs) were fabricated on the surface of Fe3O4 by surface molecular imprinting technology, which can selectively adsorb 17β-estradiol (E2). The optimized experiments demonstrated that MMIPs possessed the best adsorption capacity when methanol was used as the solvent and MAA was used as the crosslinking agent, with a molar ratio of E2: MMA: EGDMA as 1:4:50. SEM, FTIR, and XRD were employed to investigate the morphologies of MMIPs and the results demonstrated that the MMIPs that can selectively adsorb E2 were successfully prepared on Fe3O4 particles. The adsorption experiments showed that 92.1% of E2 was adsorbed by the MMIPs, which is higher than the magnetic non-molecularly imprinted polymers (MNIPs). The Freundlich isotherm model was more suitable to describe the adsorption process of E2 by MMIPs. Meanwhile, MMIPs had a better recognition ability for E2 and its structural analogs such as estrone and estriol. The MMIPs still had good adsorption performance after methanol regeneration five times. The prepared MMIPs had the advantages of efficient adsorption ability and high reusability, so they can be applied for selective recognition and removal of E2

<i>Pinctada martensii</i> Hydrolysate Modulates the Brain Neuropeptidome and Proteome in Diabetic (db/db) Mice via the Gut–Brain Axis

Pinctada martensii hydrolysate (PMH) has been proved to have the effect of ameliorating disorders of glucose and lipid metabolism in db/db mice, but the mechanism of its hyperglycemia effect is still unclear. Bacterial communities in fecal samples from a normal control group, a diabetic control group, and a PMH-treated diabetes mellitus type 2 (T2DM) group were analyzed by 16S gene sequencing. Nano LC-MS/MS was used to analyze mice neuropeptides and proteomes. The 16S rDNA sequencing results showed that PMH modulated the structure and composition of the gut microbiota and improved the structure and composition of Firmicutes and Bacteroidetes at the phylum level and Desulfovibrionaceae and Erysipelatoclostridiaceae at the family level. Furthermore, the expressions of functional proteins of the central nervous system, immune response-related protein, and proteins related to fatty acid oxidation in the brain disrupted by an abnormal diet were recovered by PMH. PMH regulates the brain neuropeptidome and proteome and further regulates blood glucose in diabetic mice through the gut–brain axis. PMH may be used as a prebiotic agent to attenuate T2DM, and target-specific microbial species may have unique therapeutic promise for metabolic diseases