Challenges and Opportunities for Second-life Batteries: A Review of Key Technologies and Economy

Due to the increasing volume of Electric Vehicles in automotive markets and the limited lifetime of onboard lithium-ion batteries (LIBs), the large-scale retirement of LIBs is imminent. The battery packs retired from Electric Vehicles still own 70%-80% of the initial capacity, thus having the potential to be utilized in scenarios with lower energy and power requirements to maximize the value of LIBs. However, spent batteries are commonly less reliable than fresh batteries due to their degraded performance, thereby necessitating a comprehensive assessment from safety and economic perspectives before further utilization. To this end, this paper reviews the key technological and economic aspects of second-life batteries (SLBs). Firstly, we introduce various degradation models for first-life batteries and identify an opportunity to combine physics-based theories with data-driven methods to establish explainable models with physical laws that can be generalized. However, degradation models specifically tailored to SLBs are currently absent. Therefore, we analyze the applicability of existing battery degradation models developed for first-life batteries in SLB applications. Secondly, we investigate fast screening and regrouping techniques and discuss the regrouping standards for the first time to guide the classification procedure and enhance the performance and safety of SLBs. Thirdly, we scrutinize the economic analysis of SLBs and summarize the potentially profitable applications. Finally, we comprehensively examine and compare power electronics technologies that can substantially improve the performance of SLBs, including high-efficiency energy transformation technologies, active equalization technologies, and technologies to improve reliability and safety

Controllable Entanglement Distribution Network Based on Silicon Quantum Photonics

The entanglement distribution network connects remote users through sharing entanglement resources, which is essential for realizing quantum internet. We proposed a controllable entanglement distribution network (c-EDN) based on a silicon quantum photonic chip. The entanglement resources were generated by a quantum light source array based on spontaneous four-wave mixing (SFWM) in silicon waveguides and distributed to different users through time-reversed Hong-Ou-Mandel interferences in on-chip Mach-Zehnder interferometers with thermal phase shifters. A chip sample was designed and fabricated, supporting a c-EDN with 3 subnets and 24 users. The network topology of entanglement distributions could be reconfigured in three network states by controlling the quantum interferences through the phase shifters, which was demonstrated experimentally. Furthermore, a reconfigurable entanglement-based QKD network was realized as an application of the c-EDN. The reconfigurable network topology makes the c-EDN suitable for future quantum networks requiring complicated network control and management. Moreover, it is also shows that silicon quantum photonic chips have great potential for large-scale c-EDN, thanks to their capacities on generating and manipulating plenty of entanglement resources

Venice: Exploring Server Architectures for Effective Resource Sharing

Consolidated server racks are quickly becoming the backbone of IT infrastructure for science, engineering, and business, alike. These servers are still largely built and organized as when they were distributed, individual entities. Given that many fields increasingly rely on analytics of huge datasets, it makes sense to support flexible resource utilization across servers to improve cost-effectiveness and performance. We introduce Venice, a family of data-center server architectures that builds a strong communication substrate as a first-class resource for server chips. Venice provides a diverse set of resource-joining mechanisms that enables user programs to efficiently leverage non-local resources. To better understand the implications of design decisions about system support for resource sharing we have constructed a hardware prototype that allows us to more accurately measure end-to-end performance of at-scale applications and to explore tradeoffs among performance, power, and resource-sharing transparency. We present results from our initial studies analyzing these tradeoffs when sharing memory, accelerators, or NICs. We find that it is particularly important to reduce or hide latency, that data-sharing access patterns should match the features of the communication channels employed, and that inter-channel collaboration can be exploited for better performance

Generative Pretraining in Multimodality

We present Emu, a Transformer-based multimodal foundation model, which can seamlessly generate images and texts in multimodal context. This omnivore model can take in any single-modality or multimodal data input indiscriminately (e.g., interleaved image, text and video) through a one-model-for-all autoregressive training process. First, visual signals are encoded into embeddings, and together with text tokens form an interleaved input sequence. Emu is then end-to-end trained with a unified objective of classifying the next text token or regressing the next visual embedding in the multimodal sequence. This versatile multimodality empowers the exploration of diverse pretraining data sources at scale, such as videos with interleaved frames and text, webpages with interleaved images and text, as well as web-scale image-text pairs and video-text pairs. Emu can serve as a generalist multimodal interface for both image-to-text and text-to-image tasks, and supports in-context image and text generation. Across a broad range of zero-shot/few-shot tasks including image captioning, visual question answering, video question answering and text-to-image generation, Emu demonstrates superb performance compared to state-of-the-art large multimodal models. Extended capabilities such as multimodal assistants via instruction tuning are also demonstrated with impressive performance.Comment: Code and Demo: https://github.com/baaivision/Em

Experimental Investigation on Bioremediation of Heavy Metal Contaminated Solution by Sporosarcina pasteurii under Some Complex Conditions

Bioremediation of contaminated solutions has attracted extensive attention in recent years due to its wide range of applicability to various types of contaminants and environmental friendliness. Previous studies adequately confirmed the potential of Sporosarcina pasteurii (i.e., S. pasteurii)-based bioremediation for heavy metal contaminated solutions, but they focused mainly on the bioremediation ability of single-heavy-metal contaminated solutions. This study focuses on S. pasteurii-based bioremediation under more complex pollution conditions. A series of laboratory experiments were performed to explore the efficiency and mechanism of S. pasteurii-based bioremediation to heavy metal contaminated solutions under various conditions, including single-heavy-metal pollution condition, heavy metal pollution under high mineral salinity context and multi-heavy-metal pollution scenarios. The results show that S. pasteurii can effectively remove heavy metals such as Cd, Cr(III), and Zn through biomineralization; for the typical range of mineral salinity (including NaCl and KCl) possibly encountered in practice in some contaminated solutions, such as leachate of landfills, the detrimental influence of high mineral salinity on efficiency of S. pasteurii-based bioremediation can be neglected; more importantly, S. pasteurii-based bioremediation can be considered as a potential option for remedying multi-heavy-metal contaminated solutions, though the addition of some heavy metals tends to produce a substantially detrimental influence on the bioremediation ability of S. pasteurii to other heavy metals

Theoretical Study on the Origin of Abnormal Regioselectivity in Ring-Opening Reaction of Hexafluoropropylene Oxide

That nucleophiles preferentially attack at the less sterically hindered carbon of epoxides under neutral and basic conditions has been generally accepted as a fundamental rule for predicting the regioselectivity of this type of reaction. However, this rule does not hold for perfluorinated epoxides, such as hexafluoropropylene oxide (HFPO), in which nucleophiles were found to attack at the more hindered CF3 substituted β-C rather than the fluorine substituted α-C. In this contribution, we aim to shed light on the nature of this intriguing regioselectivity by density functional theory methods. Our calculations well reproduced the observed abnormal regioselectivities and revealed that the unusual regiochemical preference for the sterically hindered β-C of HFPO mainly arises from the lower destabilizing distortion energy needed to reach the corresponding ring-opening transition state. The higher distortion energy required for the attack of the less sterically hindered α-C results from a significant strengthening of the C(α)-O bond by the negative hyperconjugation between the lone pair of epoxide O atom and the antibonding C-F orbital

Experimental Investigation on Bioremediation of Heavy Metal Contaminated Solution by <i>Sporosarcina pasteurii</i> under Some Complex Conditions

Bioremediation of contaminated solutions has attracted extensive attention in recent years due to its wide range of applicability to various types of contaminants and environmental friendliness. Previous studies adequately confirmed the potential of Sporosarcina pasteurii (i.e., S. pasteurii)-based bioremediation for heavy metal contaminated solutions, but they focused mainly on the bioremediation ability of single-heavy-metal contaminated solutions. This study focuses on S. pasteurii-based bioremediation under more complex pollution conditions. A series of laboratory experiments were performed to explore the efficiency and mechanism of S. pasteurii-based bioremediation to heavy metal contaminated solutions under various conditions, including single-heavy-metal pollution condition, heavy metal pollution under high mineral salinity context and multi-heavy-metal pollution scenarios. The results show that S. pasteurii can effectively remove heavy metals such as Cd, Cr(III), and Zn through biomineralization; for the typical range of mineral salinity (including NaCl and KCl) possibly encountered in practice in some contaminated solutions, such as leachate of landfills, the detrimental influence of high mineral salinity on efficiency of S. pasteurii-based bioremediation can be neglected; more importantly, S. pasteurii-based bioremediation can be considered as a potential option for remedying multi-heavy-metal contaminated solutions, though the addition of some heavy metals tends to produce a substantially detrimental influence on the bioremediation ability of S. pasteurii to other heavy metals