Phosphorus-doped porous carbons as efficient electrocatalysts for oxygen reduction

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Efficient electrocatalysts for the oxygen reduction reaction (ORR) play a critical role in the performance of fuel cells and metal–air batteries. In this study, we report a facile synthesis of phosphorus (P)-doped porous carbon as a highly active electrocatalyst for the ORR. Phosphorus-doped porous carbon was prepared by simultaneous doping and activation of carbon with phosphoric acid (H3PO4) in the presence of Co. Both phosphorus and cobalt were found to play significant roles in improving the catalytic activity of carbon for the ORR. The as-prepared phosphorus-doped porous carbon exhibited considerable catalytic activity for the ORR as evidenced by rotating ring-disk electrode studies. At the same mass loading, the Tafel slope of phosphorus-doped porous carbon electrocatalysts is comparable to that of the commercial Pt/C catalysts (20 wt% Pt on Vulcan XC-72, Johnson Matthey) with stability superior to Pt/C in alkaline solutions

Downstream-agnostic Adversarial Examples

Self-supervised learning usually uses a large amount of unlabeled data to pre-train an encoder which can be used as a general-purpose feature extractor, such that downstream users only need to perform fine-tuning operations to enjoy the benefit of "large model". Despite this promising prospect, the security of pre-trained encoder has not been thoroughly investigated yet, especially when the pre-trained encoder is publicly available for commercial use. In this paper, we propose AdvEncoder, the first framework for generating downstream-agnostic universal adversarial examples based on the pre-trained encoder. AdvEncoder aims to construct a universal adversarial perturbation or patch for a set of natural images that can fool all the downstream tasks inheriting the victim pre-trained encoder. Unlike traditional adversarial example works, the pre-trained encoder only outputs feature vectors rather than classification labels. Therefore, we first exploit the high frequency component information of the image to guide the generation of adversarial examples. Then we design a generative attack framework to construct adversarial perturbations/patches by learning the distribution of the attack surrogate dataset to improve their attack success rates and transferability. Our results show that an attacker can successfully attack downstream tasks without knowing either the pre-training dataset or the downstream dataset. We also tailor four defenses for pre-trained encoders, the results of which further prove the attack ability of AdvEncoder.Comment: This paper has been accepted by the International Conference on Computer Vision (ICCV '23, October 2--6, 2023, Paris, France

La\u3csub\u3e0.6\u3c/sub\u3eSr\u3csub\u3e1.4\u3c/sub\u3eMnO\u3csub\u3e4+δ\u3c/sub\u3e Layered Perovskite Oxide: Enhanced catalytic Activity for the Oxygen Reduction Reaction

Efficient electrocatalysts for the oxygen reduction reaction (ORR) is a critical factor to influence the performance of lithium–oxygen batteries. In this study, La0.6Sr1.4MnO4+δ layered perovskite oxide as a highly active electrocatalyst for the ORR has been prepared, and a carbon-coating layer with thickness \u3c5 nm has been successfully introduced to enhance the electronic conductivity of the as-prepared oxide. XRD, XPS, Raman, SEM and TEM measurements were carried out to characterize the crystalline structure and morphology of these samples. Rotating ring-disk electrode (RRDE) technique has been used to study catalytic activities of the as-prepared catalysts for the ORR in 0.1 M KOH media. RRDE results reveal that carbon-coated La0.6Sr1.4MnO4+δ exhibits better catalytic activity for the ORR. For the carbon-coated La0.6Sr1.4MnO4+δ, the ORR proceeds predominately via a direct four electron process, and a maximum cathodic current density of 6.70 mA cm−2 at 2500 rpm has been obtained, which is close to that of commercial Pt/C electrocatalyst under the same testing conditions

A Video-Based Augmented Reality System for Human-in-the-Loop Muscle Strength Assessment of Juvenile Dermatomyositis

As the most common idiopathic inflammatory myopathy in children, juvenile dermatomyositis (JDM) is characterized by skin rashes and muscle weakness. The childhood myositis assessment scale (CMAS) is commonly used to measure the degree of muscle involvement for diagnosis or rehabilitation monitoring. On the one hand, human diagnosis is not scalable and may be subject to personal bias. On the other hand, automatic action quality assessment (AQA) algorithms cannot guarantee 100% accuracy, making them not suitable for biomedical applications. As a solution, we propose a video-based augmented reality system for human-in-the-loop muscle strength assessment of children with JDM. We first propose an AQA algorithm for muscle strength assessment of JDM using contrastive regression trained by a JDM dataset. Our core insight is to visualize the AQA results as a virtual character facilitated by a 3D animation dataset, so that users can compare the real-world patient and the virtual character to understand and verify the AQA results. To allow effective comparisons, we propose a video-based augmented reality system. Given a feed, we adapt computer vision algorithms for scene understanding, evaluate the optimal way of augmenting the virtual character into the scene, and highlight important parts for effective human verification. The experimental results confirm the effectiveness of our AQA algorithm, and the results of the user study demonstrate that humans can more accurately and quickly assess the muscle strength of children using our system

Dual‐Salt Electrolyte Additives Enabled Stable Lithium Metal Anode/Lithium–Manganese‐Rich Cathode Batteries

Although lithium (Li) metal anode/lithium–manganese-rich (LMR) cathode batteries have an ultrahigh energy density, the highly active Li metal and structural deterioration of LMR can make the usage of these batteries difficult. Herein, a multifunctional electrolyte containing LiBF4 and LiFSI dual-salt additives is designed, which enables the superior cyclability of Li/LMR cells with capacity retentions of ≈83.4%, 80.4%, and 76.6% after 400 cycles at 0.5, 1, and 2 C, respectively. The dual-salt electrolyte can form a thin, uniform, and inorganic species-rich solid electrolyte interphase (SEI) and cathode electrolyte interphase (CEI). In addition, it alleviates the bulk Li corrosion and enhances the structural sustainability of LMR cathode. Moreover, the electrolyte design strategy provides insights to develop other high-voltage lithium metal batteries (HVLMBs) to enhance the cycle stability

Revealing the Various Electrochemical Behaviors of Sn4P3 Binary Alloy Anodes in Alkali Metal Ion Batteries

Sn4P3 binary alloy anode has attracted much attention, not only because of the synergistic effect of P and Sn, but also its universal popularity in alkali metal ion batteries (AIBs), including lithium-ion batteries (LIBs), sodium-ion batteries (SIBs), and potassium-ion batteries (PIBs). However, the alkali metal ion (A+) storage and capacity attenuation mechanism of Sn4P3 anodes in AIBs are not well understood. Herein, a combination of ex situ X-ray diffraction, transmission electron microscopy, and density functional theory calculations reveals that the Sn4P3 anode undergoes segregation of Sn and P, followed by the intercalation of A+ in P and then in Sn. In addition, differential electrochemical curves and ex situ XPS results demonstrate that the deep insertion of A+ in P and Sn, especially in P, contributes to the reduction in capacity of AIBs. Serious sodium metal dendrite growth causes further reduction in the capacity of SIBs, while in PIBs it is the unstable solid electrolyte interphase and sluggish dynamics that lead to capacity decay. Not only the failure mechanism, including structural deterioration, unstable SEI, dendrite growth, and sluggish kinetics, but also the modification strategy and systematic analysis method provide theoretical guidance for the development of other alloy-based anode materials. © 2021 The Authors. Advanced Functional Materials published by Wiley-VCH Gmb

Revealing the various electrochemical behaviors of Sn4P3 binary alloy anodes in alkali metal ion batteries

Sn4P3 binary alloy anode has attracted much attention, not only because of the synergistic effect of P and Sn, but also its universal popularity in alkali metal ion batteries (AIBs), including lithium-ion batteries (LIBs), sodium-ion batteries (SIBs), and potassium-ion batteries (PIBs). However, the alkali metal ion (A(+)) storage and capacity attenuation mechanism of Sn4P3 anodes in AIBs are not well understood. Herein, a combination of ex situ X-ray diffraction, transmission electron microscopy, and density functional theory calculations reveals that the Sn4P3 anode undergoes segregation of Sn and P, followed by the intercalation of A(+) in P and then in Sn. In addition, differential electrochemical curves and ex situ XPS results demonstrate that the deep insertion of A(+) in P and Sn, especially in P, contributes to the reduction in capacity of AIBs. Serious sodium metal dendrite growth causes further reduction in the capacity of SIBs, while in PIBs it is the unstable solid electrolyte interphase and sluggish dynamics that lead to capacity decay. Not only the failure mechanism, including structural deterioration, unstable SEI, dendrite growth, and sluggish kinetics, but also the modification strategy and systematic analysis method provide theoretical guidance for the development of other alloy-based anode materials.Web of Science3131art. no. 210204

Accelerating O-redox kinetics with carbon nanotubes for stable lithium-rich cathodes

Lithium-rich cathodes (LRCs) show great potential to improve the energy density of commercial lithium-ion batteries owing to their cationic and anionic redox characteristics. Herein, a complete conductive network using carbon nanotubes (CNTs) additives to improve the poor kinetics of LRCs is fabricated. Ex situ X-ray photoelectron spectroscopy first demonstrates that the slope at a low potential and the following long platform can be assigned to the transition metal and oxygen redox, respectively. The combination of galvanostatic intermittent titration technique and electrochemical impedance spectroscopy further reveal that a battery with CNTs exhibited accelerated kinetics, especially for the O-redox process. Consequently, LRCs with CNTs exhibit a much better rate and cycling performance (approximate to 89% capacity retention at 2 C for over 200 cycles) than the Super P case. Eventually, TEM results imply that the improved electrochemical performance of the CNTs case also benefits from its more stable bulk and surface structures. Such a facile conductive additive modification strategy also provides a universal approach for the enhancement of the electron diffusion properties of other electrode materials.Web of Science67art. no. 220044

Mutations in TUBB8 and Human Oocyte Meiotic Arrest

BACKGROUND Human reproduction depends on the fusion of a mature oocyte with a sperm cell to form a fertilized egg. The genetic events that lead to the arrest of human oocyte maturation are unknown. METHODS We sequenced the exomes of five members of a four-generation family, three of whom had infertility due to oocyte meiosis I arrest. We performed Sanger sequencing of a candidate gene, TUBB8, in DNA samples from these members, additional family members, and members of 23 other affected families. The expression of TUBB8 and all other β-tubulin isotypes was assessed in human oocytes, early embryos, sperm cells, and several somatic tissues by means of a quantitative reverse- transcriptase–polymerase-chain-reaction assay. We evaluated the effect of the TUBB8 mutations on the assembly of the heterodimer consisting of one α-tubulin polypeptide and one β-tubulin polypeptide (α/β-tubulin heterodimer) in vitro, on microtubule architecture in HeLa cells, on microtubule dynamics in yeast cells, and on spindle assembly in mouse and human oocytes. RESULTS We identified seven mutations in the primate-specific gene TUBB8 that were responsible for oocyte meiosis I arrest in 7 of the 24 families. TUBB8 expression is unique to oocytes and the early embryo, in which this gene accounts for almost all the expressed β-tubulin. The mutations affect chaperone-dependent folding and assembly of the α/β-tubulin heterodimer, disrupt microtubule behavior on expression in cultured cells, alter microtubule dynamics in vivo, and cause catastrophic spindle-assembly defects and maturation arrest on expression in mouse and human oocytes. CONCLUSIONS TUBB8 mutations have dominant-negative effects that disrupt microtubule behavior and oocyte meiotic spindle assembly and maturation, causing female infertility. (Funded by the National Basic Research Program of China and others.

Off-line evaluation of indoor positioning systems in different scenarios: the experiences from IPIN 2020 competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001