Nature Energy最新文献

Laboratory ageing campaigns elucidate the complex degradation behaviour of most technologies. In lithium-ion batteries, such studies aim to capture realistic ageing mechanisms to optimize cell chemistries and designs as well as to engineer reliable battery management systems. In this study, we systematically compared dynamic discharge profiles representative of electric vehicle driving to the well-accepted constant current profiles. Surprisingly, we found that dynamic discharge enhances lifetime substantially compared with constant current discharge. Specifically, for the same average current and voltage window, varying the dynamic discharge profile led to an increase of up to 38% in equivalent full cycles at end of life. Explainable machine learning revealed the importance of both low-frequency current pulses and time-induced ageing under these realistic discharge conditions. This work quantifies the importance of evaluating new battery chemistries and designs with realistic load profiles, highlighting the opportunities to revisit our understanding of ageing mechanisms at the chemistry, material and cell levels.

The heterointerfaces between perovskite and charge-transporting layers pose a major limitation to the durability of perovskite solar cells (PSCs), largely due to complex and conflicting chemical and mechanical interactions. Here we introduce an effective debonding technique to thoroughly analyse heterointerface behaviour during both crystal growth and ageing phases of PSCs. Our analysis reveals a strong correlation between interface bonding (fracture energy ranging from ~2.49 J m⁻² to ~0.38 J m⁻²), proton transfer interactions and degradation, highlighting a critical trade-off between mechanical and chemical stability in PSCs. To address these stability challenges, we mixed Me-4PACz and DCZ-4P molecules, which introduced additional phosphonic acid anchoring groups to enhance bonding at both the metal oxide and the perovskite interfaces. With a high efficiency of 25.6%, the devices retained 90% of their initial performance after 1,000 h of testing under ISOS-L-1I and ISOS-D-2I standard protocols. Under thermal cycling conditions, our PSCs sustained 95% of their efficiency over 500 cycles, exceeding the IEC 61215 and ISOS-T-3I standards.

The development of heat-resistant dielectric polymers that withstand intense electric fields at high temperatures is critical for electrification. Balancing thermal stability and electrical insulation, however, is exceptionally challenging as these properties are often inversely correlated. A traditional intuition-driven polymer design approach results in a slow discovery loop that limits breakthroughs. Here we present a machine learning-driven strategy to rapidly identify high-performance, heat-resistant polymers. A trustworthy feed-forward neural network is trained to predict key proxy parameters and down select polymer candidates from a library of nearly 50,000 polysulfates. The highly efficient and modular sulfur fluoride exchange click chemistry enables successful synthesis and validation of selected candidates. A polysulfate featuring a 9,9-di(naphthalene)-fluorene repeat unit exhibits excellent thermal resilience and achieves ultrahigh discharged energy density with over 90% efficiency at 200 °C. Its exceptional cycling stability underscores its promise for applications in demanding electrified environments.

The power conversion efficiency of organic solar cells (OSCs) is exceeding 20%, an advance in which morphology optimization has played a significant role. It is generally accepted that the processing solvent (or solvent mixture) can help optimize morphology, impacting the OSC efficiency. Here we develop OSCs that show strong tolerance to a range of processing solvents, with all devices delivering high power conversion efficiencies around 19%. By investigating the solution states, the film formation dynamics and the characteristics of the processed films both experimentally and computationally, we identify the key factors that control morphology, that is, the interactions between the side chains of the acceptor materials and the solvent as well as the interactions between the donor and acceptor materials. Our work provides new understanding on the long-standing question of morphology control and effective guides to design OSC materials towards practical applications, where green solvents are required for large-scale processing.

Lithium metal batteries operating under extreme conditions are limited by the sluggish desolvation process and poor stability of the electrode–electrolyte interphase. However, rational interphase design is hindered by the ill-defined understanding of interphasial chemistry at the molecular level. Here we design and synthesize a series of sulfoximide salts, lithium bis(trifluoromethanesulfinyl)imide (LiBSTFSI) and lithium (trifluoromethanesulfinyl)(trifluoromethanesulfonyl)imide (LiSTFSI), that possess distinctive oxidizability. Their molecular structure and interphasial chemistry were correlated. An anionic electro-polymerization was induced by the asymmetric LiSTFSI to establish a bilayer catholde–electrolyte interphase (CEI) with LiF dominated inner covered by negative-charged inorganic polymers. LiSTFSI-derived CEI enables superior mechanical stability and accelerated Li⁺ desolvation that contribute to the stable cycling and superior energy and power densities under ultra-high rate and ultra-low temperature conditions. Industrial pouch cells of 474 Wh kg⁻¹ achieved extreme power density of 5,080 W kg⁻¹ at 30 °C and exceptional low-temperature energy and power densities at −20 °C (382 Wh kg⁻¹, 3,590 W kg⁻¹) and −40 °C (321 Wh kg⁻¹, 1,517 W kg⁻¹).

Accurate energy system modelling of chemical separations is a critical component of technology selection to minimize operating costs, energy consumption and emissions. Here we report a hybrid modelling approach based on data-driven and mechanistic models to holistically compare chemical separation performance. Our model can be used to select the most suitable technology for a given chemical separation, such as membrane separation, evaporation, extraction or hybrid configurations, by training a machine learning model to predict solute rejection using an open-access membrane dataset. We estimated an average 40% reduction in energy consumption and carbon dioxide emissions for industrially relevant separations using our methodology. We predicted and analysed 7.1 million solute rejections across several industrial sectors. Pharmaceutical purification could realize carbon dioxide emissions reductions of up to 90% by selecting the most efficient technology. We mapped the reduction in carbon dioxide emissions and the reduction in operating costs globally, establishing parameter thresholds to facilitate corporate and governmental decision-making.

Electrocatalytic water splitting is a promising approach for clean hydrogen production, but the process is hindered by the sluggish kinetics of the anodic oxygen evolution reaction (OER) owing to the spin-dependent electron transfer process. Efforts to control spin through chirality and magnetization have shown potential in enhancing OER performance. Here we harnessed the potential of topological chiral semimetals (RhSi, RhSn and RhBiS) and their spin-polarized Fermi surfaces to promote the spin-dependent electron transfer in the OER, addressing the traditional volcano-plot limitations. We show that OER activities follow the trend RhSi < RhSn < RhBiS, corresponding to the increasing extent of spin–orbit coupling (SOC). The chiral single crystals outperform achiral counterparts (RhTe₂, RhTe and RuO₂) in alkaline electrolyte, with RhBiS exhibiting a specific activity two orders of magnitude higher than RuO₂. Our work reveals the pivotal roles of chirality and SOC in spin-dependent catalysis, facilitating the design of ultra-efficient chiral catalysts.