Direct recycling of Li-ion batteries from cell to pack level:Challenges and prospects on technology,scalability,sustainability,and economics

Direct recycling is a novel approach to overcoming the drawbacks of conventional lithium-ion battery(LIB)recycling processes and has gained considerable attention from the academic and industrial sectors in recent years.The primary objective of directly recycling LIBs is to efficiently recover and restore the active electrode materials and other components in the solid phase while retaining electrochemical performance.This technology's advantages over traditional pyrometallurgy and hydrometallurgy are costeffectiveness,energy efficiency,and sustainability,and it preserves the material structure and morphology and can shorten the overall recycling path.This review extensively discusses the advancements in the direct recycling of LIBs,including battery sorting,pretreatment processes,separation of cathode and anode materials,and regeneration and quality enhancement of electrode materials.It encompasses various approaches to successfully regenerate high-value electrode materials and streamlining the recovery process without compromising their electrochemical properties.Furthermore,we highlight key challenges in direct recycling when scaled from lab to industries in four perspectives:(1)battery design,(2)disassembling,(3)electrode delamination,and(4)commercialization and sustainability.Based on these challenges and changing market trends,a few strategies are discussed to aid direct recycling efforts,such as binders,electrolyte selection,and alternative battery designs;and recent transitions and technological advancements in the battery industry are presented.

Designing electrodes and electrolytes for batteries by leveraging deep learning

High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions,which,in turn,promote a sustainable future.However,the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry.Recent advancements have demonstrated the potential of deep learning techniques in efficiently designing batteries,particularly in optimizing electrodes and electrolytes.This review provides comprehensive concepts and principles of deep learning and its application in solving battery-related electrochemical problems,which bridges the gap between artificial intelligence and electrochemistry.We also examine the potential challenges and opportunities associated with different deep learning approaches,tailoring them to specific battery requirements.Ultimately,we aim to inspire future advancements in both fundamental scientific understanding and practical engineering in the field of battery technology.Furthermore,we highlight the potential challenges and opportunities for different deep learning methods according to the specific battery demand to inspire future advancement in fundamental science and practical engineering.

Synthesis of Cu-Doped Layered Transition Metal Oxide Cathode Materials Directly from Metal-Organic Frameworks for Sodium-Ion Batteries

Mn-based layered transition metal oxides are promising cathode materials for sodium-ion batteries(SIBs)because of their high theoretical capacities,abundant raw materials,and environment-friendly advantages.However,they often show insufficient performance due to intrinsic issues including poor structural stability and dissolution of Mn^(3+).Atomic doping is an effective way to address these structural degradation issues.Herein,we reported a new synthesis strategy of a Cu-doped layered cathode by directly calcinating a pure metal-organic framework.Benefiting from the unique structure of MOF with atomic-level Cu doping,a homogeneous Cu-doped layered compound P2-Na_(0.674)Cu_(0.01)Mn_(0.99)O_(2) was obtained.The Cu substitution promotes the crystal structural stability and suppresses the dissolution of Mn,thus preventing the structure degradation of the layered cathode materials.A remarkably enhanced cyclability is realized for the Cu-doped cathode compared with that without Cu doping,with 83.8%capacity retention after 300 cycles at 100 mA·g^(-1).Our findings provide new insights into the design of atomic-level doping layered cathode materials constructed by MOFs for high-performance SIBs.

Unsupervised machine learning to classify crystal structures according totheir structural distortion: A case study on Li-argyrodite solid-state electrolytes

High-throughput approaches in computational materials discovery often yield a combinatorial explosionthat makes the exhaustive rendering of complete structural and chemical spaces impractical. A commonbottleneck when screening new compounds with archetypal crystal structures is the lack of fast and reliabledecision-making schemes to quantitatively classify the computed candidates as inliers or outliers (too distortedstructures). Machine learning-aided workflows can solve this problem and make geometrical optimizationprocedures more efficient. However, for this to occur, there is still a lack of appropriate combinations ofsuitable geometrical descriptors and accurate unsupervised models which are capable of accurately differentiating between systems with subtle structural changes. Here, considering as a case study the compositionalscreening of cubic Li-argyrodites solid electrolytes, we tackle this problem head on. We find that Steinhardtorder parameters are very accurate descriptors of the cubic argyrodite structure to train a range of commonunsupervised outlier detection models. And, most importantly, the approach enables us to automatically classifycrystal structures with uncertainty control. The resulting models can then be used to screen computed structureswith respect to an user-defined error threshold and discard too distorted structures during geometricaloptimization procedures. Implemented as a decision node in computer-aided materials discovery workflows,this approach can be employed to perform autonomous high-throughput screening methods and make the useof computational and data storage resources more efficient.