DeCarbon最新文献

P-i-n type perovskite solar cells (PSCs) manifest some promising advantages in terms of remarkable operational stability, low-temperature processability, and compatibility for multi-junction devices, whereas they have relatively low efficiency compared to n-i-p type PSCs because of mismatched energy level alignment and poor interface quality at both n- and p-type contacts. Recently, great progress has been achieved in the p-i-n type PSCs, and efficiencies exceeding 25 ​% have been reported from different research groups. Herein, state-of-the-art strategies in the deployment of high-performance p-i-n type PSCs have been systematically reviewed including engineering top-surface and buried interface of perovskite films with eliminated non-radiative charge recombination, modulating conduction types of the perovskites with well aligned energy level to facilitate charge transport, and designing effective hole transport materials for lossless charge extraction, and so on, based on which perspectives in the further design of efficient, stable and scalable p-i-n type PSCs are provided from the aspects of materials design, device fabrication, scalability and functionalization.

Heating, ventilation and air-conditioning (HVAC) accounts for around 40% of the total building energy consumption. It has therefore become a major target for reductions, in terms of both energy usage and CO₂ emissions. In the light of progress in building intelligence and energy technologies, traditional methods for HVAC optimization, control, and fault diagnosis will struggle to meet essential requirements such as energy efficiency, occupancy comfort and reliable fault detection. Machine learning and data science have great potential in this regard, particularly with developments in information technology and sensor equipment, providing access to large volumes of high-quality data. There remains, however, a number of challenges before machine learning can gain widespread adoption in industry. This review summarizes the recent literature on machine learning for HVAC system optimization, control and fault detection. Unlike other reviews, we provide a comprehensive coverage of the applications, including the factors considered. A brief overview of machine learning and its applications to HVAC is provided, after which we critically appraise the recent literature on control, optimization and fault diagnosis and detection. Finally, we provide a comprehensive discussion on the limitations of current research and suggest future research directions.

Limiting global carbon dioxide (CO₂) emission is imperative to alleviate global warming and meet the growing energy demand. Electrocatalytic CO₂ reduction is a promising approach for achieving this goal. The utilization of single atom-based catalysts (SACs) has garnered substantial attention in this particular field. Although noble metal SACs offer many advantages in CO₂ reduction, their high cost and scarcity have deterred many researchers. Consequently, the focus has shifted toward low-priced transition metals, which have shown better performance than some rare metals. This comprehensive review focuses on the research advances in electrocatalysis for CO₂ reduction reaction using SACs in the past five years. The main synthesis strategies of SACs in recent years are also summarized in detail. Furthermore, based on the difference in the catalytic performance and stability of different catalysts, the review summarizes the performance of non-noble metal SACs (such as Fe, Ni, Co, Mn, Cu, Sn, and Zn) with single metal sites in CO₂ reduction reaction. The discussion of the potential mechanisms is included. Finally, the review ends by presenting an outlook on the difficulties and possibilities inherent in this developing area of single atom electrocatalytic CO₂ reduction.

This study presented a novel methodology to predict the CO₂ absorption enhancement performance of TiO₂-Monoethanolamine/Methyl diethanolamine (MEA/MDEA) blended amine nanofluids using back propagation neural network (BPNN) model in artificial neural networks. The absorption enhancement factor of TiO₂-MEA/MDEA nanofluid were determined experimentally by a two-step method with various nanoparticle solid contents (0.4–1.4 ​g/L). The results showed that the enhancement factor was firstly increased and then decreased with the rising nanoparticle solid content, and the extreme point appeared at 0.6 ​g/L. Based on the experimental data, a relevant empirical formula and a back propagation neural network (BPNN) model were used to estimate the enhancement factor of nanofluid and both exhibited good applicability. Additionally, an optimization model incorporating genetic algorithm, particle swarm algorithm and adaptive learning rate (C-BPNN) was also proposed to estimate the enhancement factor. Compared with the empirical formula and BPNN, C-BPNN exhibited a higher prediction accuracy (all data R² ​= ​0.9966) and a faster prediction rate. The weight analysis of key parameters (nanoparticle solid content, concentration of MEA and MDEA) showed that the relative importance of nanoparticle solid content was foremost (42.63%) in the absorption enhancement process. All these results indicate that the neural network can provide a guiding role for the research in the field of nanofluid transfer.

With the growth of energy and environment crisis, catalytic energy conversion and environment treatment have attracted tremendous attention among both scientific and industrial fields. Three-dimensional (3D) printing can construct various organic and inorganic materials into customized structures based on digitally designed 3D images models, which is a promising technology for manufacturing of high-performance materials for enhanced catalytic reactions. 3D printing has the advantages of free structure design, material saving and high manufacturing precision, and provides more possibilities for the design of materials and electrode structures in the field of catalysis. In this review, working principles of different 3D printing technologies are introduced, followed by the latest development of 3D printing applied for high-performance catalysis, including water-splitting and environment treatment reactions. Finally, the development prospects and challenges of combining 3D printing and catalytic technology are further discussed.

Microalgae biofilm is a typical porous structure where CO₂ is converted by microalgae into organic matter. Therefore, CO₂ transport and its distribution in porous biofilm are crucial for microalgae decarbonization and its energy utilization. In order to get detailed process information of CO₂ transport and its bioconversion, a mathematical model considering the microalgae growth and its material consumptions was established for substances' flow and transport processes in an immersed microalgae biofilm reactor. Modeling results showed that CO₂ concentration on the surface of biofilm reduced about 26.57% along the flow direction. Increased inlet CO₂ concentration (0.2–5.2 ​mM) significantly promoted the average specific growth rate of biofilm, which was more dramatical at a low flow rate with an enhancement about 7.38 times. Essential reason for it is a synchronous increase on total transfer flux of CO₂ (8.25 times by $\varphi {\_}_{C{O}_2}$) and average CO₂ consumption rate (7.48 times by $ACR{\_}_{C{O}_2}$) in biofilm. However, such promotion gradually waned with a growing initial carbon supply concentration. Enhanced CO₂ transport in biofilm caused by increasing culture medium's flow rate (1–6 ​mL ​min⁻¹) doesn't always result in synchronous improvements on biofilm growth. At sufficient carbon supply, increased flow rate doesn't further effectively improve biofilm growth but greatly reduced CO₂ removal. Whether increasing carbon supply concentration or flow rate, biofilm growth can't be significantly promoted unless the $\varphi {\_}_{C{O}_2}$ and $ACR{\_}_{C{O}_2}$ in biofilm were increased by almost the same level. This work provides a new and deeper mechanistic insight into macroscopic growth characteristics of biofilms from the perspective of CO₂ transport in it, as well as providing some theoretical guidance towards the cultivation of immersed microalgae biofilm for bio-decarbonization.

The European plan for a green transition includes the Fit for 55 package, designed to pave the way for climate neutrality. Despite its significant implications for cleaner technologies, it potentially correlates with high investment requirements, necessitating the pursuit of cost-effective environmental policies. Starting from the reference scenario previously envisaged in the Energy and Climate Plan, socioeconomic and environmental impacts are assessed using mixed methods. It is estimated that €1120 bn in investments are needed to meet decarbonization targets, while the total impact on public finance revenues to 2030 is projected at €529 bn. Additionally, the avoided costs of emissions amount to €36 bn, while those from energy savings are expected to reach €30 bn. This paper adds value by contributing to the literature on European climate policies, offering an in-depth appraisal of implications that integrates technoeconomic and environmental perspectives. Furthermore, it informs policymakers' public spending decisions for decarbonization.

Natural drying (without spin coating or assistance of antisolvent, gas, or vacuum) might be the least-cost drying method to make perovskite films for solar cells. However, perovskite films made without quenching generally show undesirable morphology and low photovoltaic performance. In this work, we developed a high-throughput screening method to find the perovskite compositions that can form high-quality film without quenching process. We found that composition-graded perovskite films can be easily made by using a solution interdiffusion process, which produces perovskite films with continuously changing composition and the resulting film morphology, enabling fast screening of desired perovskite compositions. Several perovskites (CsPbI₂Br, FA_0.4Cs_0.6PbI₃, FA_0.2MA_0.8PbI₃, and FA_0.95Cs_0.05PbI₃) which can form high-quality films without quenching were successfully found. FA_0.95Cs_0.05PbI₃ films yield a PCE of 23.28%, which is the highest PCE for perovskite solar cells with naturally-dried perovskite layer.

Achieving high short-circuit current density (J_SC) to boost power conversion efficiency (PCE) of all-polymer solar cells (all-PSCs) is a major challenge, mainly due to the difficulty in developing high-performance near-infrared (NIR)-absorbing polymer acceptors. Herein, a new polymer acceptor named PY2Se–4F employing a Y-series small-molecule acceptor as the precursor is designed and synthesized. Thanks to its unique molecular backbone structure combining selenophene-fused central core and bi-fluorinated end-group, PY2Se–4F shows desirable NIR-absorption with a spectral onset approaching 1000 ​nm, which is beneficial for obtaining high J_SC when matched with wide bandgap polymer donors such as PM6 and D18. For the binary all-PSCs, PM6:PY2Se–4F delivers a record-high J_SC of 26.5 ​mA ​cm⁻², which is superior to that of D18:PY2Se–4F, mainly due to stronger absorption in the range of 600–700 ​nm. In contrast, the D18:PY2Se–4F combination exhibits more favorable blend morphology, higher and more balanced charge-transporting, and less non-radiative energy loss compared with the PM6:PY2Se–4F. As a result, the D18:PY2Se–4F-based devices offer an improved PCE of 16.1 ​% with a J_SC of 25.5 ​mA ​cm⁻² and both higher photovoltage and fill factor, while the related PCE and J_SC are ones of the top values among the reported binary all-PSCs. The results indicate that PY2Se–4F is a promising NIR-absorbing polymer acceptor for obtaining efficient all-PSCs with record-high J_SC.

The world's marine litter consists mainly of plastic, and 99% of it does not float on the surface of the sea but on the seabed. The plastic carbon footprint necessarily includes the extraction or manufacture of raw materials, the conversion process, the distribution of products, the consumption of specific types of products and the disposal of the final product, as all these stages release carbon into the atmosphere. This work, inspired by marine microplastics and investigates how plastic waste is degraded and transformed in high-pressure, low-temperature seawater, this paper investigates the corrosion of polyethylene terephthalate (PET) and polypropylene (PP) plastics in seawater at high-pressure, using artificial seawater temperatures to simulate ocean temperatures of approximately 4 ​°C and time settings of 1 day–7 days. The results show that increasing the time enhances the degradation of the plastics and that changing the pressure has little effect on the degradation effect. Understanding its degradation in seawater can help us to better treat plastic waste and thus reduce the carbon footprint of the disposal process.