Carbon Trading in BRICS Countries: Challenges and Recommendations

As one of the world’s largest emerging economies, BRICS countries are playing an increasingly important role in addressing the global issue of climate change. To achieve their emissions reduction targets, these nations are actively promoting the construction of carbon trading markets. However, they face multiple challenges and obstacles in this endeavor, including issues related to market norms, financial support, technical capacity, social participation, and development needs. This research investigates the problems and challenges faced by BRICS countries in terms of building carbon trading markets through literature reviews and case studies. To address these challenges, this research strengthening international cooperation and technical support, improving market norms and provide following recommendations: conducting regulatory measures, enhancing social participation and communication, and balancing the relationship between economic development and environmental protection requirements. Furthermore, it is crucial for these nations to continue to strengthen international cooperation and collaboration, working together to promote the construction of carbon trading markets, achieving their emissions reduction targets, and ensuring long-term sustainability and economic development

State of health estimation for lithium-ion batteries under arbitrary usage using data-driven multi-model fusion

Accurately estimating the state of health (SoH) of batteries is indispensable for the safety, reliability, and optimal energy and power management of electric vehicles. However, from a data-driven perspective, complications, such as dynamic vehicle operating conditions, stochastic user behaviors, and cell-to-cell variations, make the estimation task challenging. This work develops a data-driven multi-model fusion method for SoH estimation under arbitrary usage profiles. All possible operating conditions are categorized into six scenarios. For each scenario, an appropriate feature set is extracted to indicate the SoH. Based on the obtained features, four machine learning algorithms are applied individually to train SoH estimation models using time-series data. In addition to the estimates at the current time step, a histogram data-based and online adaptive model is taken from previous work for predicting the next-step SoH. Then, a Kalman filter is applied to systematically fuse the results of all the estimation and prediction models. Experimental data collected from different types of batteries operated under diverse profiles verify the effectiveness and practicability of the developed method, as well as its superiority over individual models

A machine learning-based framework for online prediction of battery ageing trajectory and lifetime using histogram data

Accurately predicting batteries’ ageing trajectory and remaining useful life is not only required to ensure safe and reliable operation of electric vehicles (EVs) but is also the fundamental step towards health-conscious use and residual value assessment of the battery. The non-linearity, wide range of operating conditions, and cell to cell variations make battery health prediction challenging. This paper proposes a prediction framework that is based on a combination of global models offline developed by different machine learning methods and cell individualised models that are online adapted. For any format of raw data collected under diverse operating conditions, statistic properties of histograms can be still extracted and used as features to learn battery ageing. Our framework is trained and tested on three large datasets, one being retrieved from 7296 plug-in hybrid EVs. While the best global models achieve 0.93% mean absolute percentage error (MAPE) on laboratory data and 1.41% MAPE on the real-world fleet data, the adaptation algorithm further reduced the errors by up to 13.7%, all requiring low computational power and memory. Overall, this work proves the feasibility and benefits of using histogram data and also highlights how online adaptation can be used to improve predictions

Constructing quantum dots@flake g-C3N4 isotype heterojunctions for enhanced visible-light-driven NADH regeneration and enzymatic hydrogenation

The authors thank the financial support from National Natural Science Funds of China (21406163, 91534126, 21621004), Tianjin Research Program of Application Foundation and Advanced Technology (15JCQNJC10000), Open Funding Project of the National Key Laboratory of Biochemical Engineering (2015KF-03), and the Program of Introducing Talents of Discipline to Universities (B06006). X.W. also acknowledges financial support from The Carnegie Trust for the Universities of Scotland (70265) and The Royal Society (RG150001 and IE150611).Peer reviewedPostprin

Tunable Surface Wrinkling on Shape Memory Polymers with Application in Smart Micromirror

Surfaces with tunable topological features enable important applications, such as optical devices, precision metrology, adhesion, and&nbsp; wetting. In this study, we demonstrate a facile method to fabricate and control the surface morphologies by combining thin film wrinkling&nbsp; and thermal expansion. This approach utilizes self-assembled surface wrinkling induced by shape recovery of shape memory polymers and&nbsp; localized thermal expansion caused by Joule heating. Recovering the prestrain in the SMP substrate induces global wrinkling of the thin film&nbsp; on the substrate. Joule heating in the SMP by a heating wire embedded in the substrate induces thermal expansion of the substrate in a&nbsp; localized area, which leads to disappearance of the wrinkling pattern. This effect is reversed when heating is stopped, leading to reversible&nbsp; and repeatable tuning of the surface morphology in a controllable localized surface region. With metal coating, the SMP surface&nbsp; can be switched from specular to diffuse reflectance with respond to external Joule heating. Finally, we demonstrate a smart micromirror&nbsp; device with its diffuse reflectance tunable between 13.5% to 81.9% in visible light region. This approach provides a method to modulate&nbsp; surface diffusivity by controlling its surface morphologies, with potential applications in optical display and optical microelectromechanical (MEMS) devices.</p

2D nanomaterial sensing array using machine learning for differential profiling of pathogenic microbial taxonomic identification

An integrated custom cross-response sensing array has been developed combining the algorithm module's visible machine learning approach for rapid and accurate pathogenic microbial taxonomic identification. The diversified cross-response sensing array consists of two-dimensional nanomaterial (2D-n) with fluorescently labeled single-stranded DNA (ssDNA) as sensing elements to extract a set of differential response profiles for each pathogenic microorganism. By altering the 2D-n and different ssDNA with different sequences, we can form multiple sensing elements. While interacting with microorganisms, the competition between ssDNA and 2D-n leads to the release of ssDNA from 2D-n. The signals are generated from binding force driven by the exfoliation of either ssDNA or 2D-n from the microorganisms. Thus, the signal is distinguished from different ssDNA and 2D-n combinations, differentiating the extracted information and visualizing the recognition process. Fluorescent signals collected from each sensing element at the wavelength around 520 nm are applied to generate a fingerprint. As a proof of concept, we demonstrate that a six-sensing array enables rapid and accurate pathogenic microbial taxonomic identification, including the drug-resistant microorganisms, under a data size of n=288. We precisely identify microbial with an overall accuracy of 97.9%, which overcomes the big data dependence for identifying recurrent patterns in conventional methods. For each microorganism, the detection concentration is 10(5) similar to 10(8) CFU/mL for Escherichia coli, 10(2) similar to 10(7) CFU/mL for E. coli beta, 10(3) similar to 10(8) CFU/mL for Staphylococcus aureus, 10(3) similar to 10(7) CFU/mL for MRSA, 10(2) similar to 10(8) CFU/ mL for Pseudomonas aeruginosa, 10(3) similar to 10(8) CFU/mL for Enterococcus faecalis, 10(2) similar to 10(8) CFU/mL for Klebsiella pneumoniae, and 10(3) similar to 10(8) CFU/mL for Candida albicans. Combining the visible machine learning approach, this sensing array provides strategies for precision pathogenic microbial taxonomic identification