Advanced one-dimensional nanostructures for high performance catalyst electrodes in polymer electrolyte fuel cells

In the past decades, the study of nanotechnology has brought in tremendous progress to the development of polymer electrolyte fuel cells (PEFC) and many advanced catalyst approaches have been developed. However, many of these still remain at ‘test-tube’ level and have not been implemented in practical fuel cells. The concerns about the gap between the pure material research and fuel cells are increasing, and a study focusing on the electrode structures is required to help address this issue. In this thesis, the in-situ growing process of one-dimensional (1D) Pt-based nanostructures on gas diffusion layers (GDLs) was systematically studied to help understand the structure-property relationship of the gas diffusion electrodes (GDEs). The crystal nucleation and growth, coupled with the distribution of the produced nanostructures were investigated based on the corresponding GDE performance in PEFCs. The influence of the in-situ growing temperature, the hybridizing Pd metal and the structures of the GDL itself were comprehensively investigated for a further understanding of the in-situ nanowire growing process. This work demonstrates that besides the intrinsic catalytic activities of the catalysts themselves, their optimal implementation in electrodes, i.e. the electrode structure, play an important role in the power performance of PEFCs than we initially expected

Temperature-controlled growth of single-crystal Pt nanowire arrays for high performance catalyst electrodes in polymer electrolyte fuel cells

AbstractThe anisotropic structure and unique surface properties of one-dimensional (1D) Pt-nanowire (PtNW) make it a promising new type of electrocatalyst for various catalyst applications, especially for fuel cells. However, due to the critical synthesis process, a finely tuning of the synthesis temperature for precisely controlling the morphology and distribution of PtNWs in catalyst electrodes still remains a grand challenge. In this work, we present the temperature-controlled growth of PtNWs with large-area 16cm2 carbon paper piece as a direct support. The relationship between the growth temperature and PtNW behavior is studied by physical characterization, and their catalytic activity is measured towards oxygen reduction reaction (ORR) by testing as the cathode in a hydrogen-air fuel cell. The results show that the growth temperature plays a vital role on the behavior of PtNWs thus influencing their properties. The catalyst electrode with PtNWs grown at 40°C shows the best power performance. A possible mechanism for the influence of temperature on PtNW growth is suggested. The comparison with the state-of-the-art commercial TKK catalyst also shows a better performance and durability. The understanding gained in our work from PtNW catalyst electrode could aid in the design of other novel nanostructures in practical applications

Application of Entity-BERT model based on neuroscience and brain-like cognition in electronic medical record entity recognition

IntroductionIn the medical field, electronic medical records contain a large amount of textual information, and the unstructured nature of this information makes data extraction and analysis challenging. Therefore, automatic extraction of entity information from electronic medical records has become a significant issue in the healthcare domain.MethodsTo address this problem, this paper proposes a deep learning-based entity information extraction model called Entity-BERT. The model aims to leverage the powerful feature extraction capabilities of deep learning and the pre-training language representation learning of BERT(Bidirectional Encoder Representations from Transformers), enabling it to automatically learn and recognize various entity types in medical electronic records, including medical terminologies, disease names, drug information, and more, providing more effective support for medical research and clinical practices. The Entity-BERT model utilizes a multi-layer neural network and cross-attention mechanism to process and fuse information at different levels and types, resembling the hierarchical and distributed processing of the human brain. Additionally, the model employs pre-trained language and sequence models to process and learn textual data, sharing similarities with the language processing and semantic understanding of the human brain. Furthermore, the Entity-BERT model can capture contextual information and long-term dependencies, combining the cross-attention mechanism to handle the complex and diverse language expressions in electronic medical records, resembling the information processing method of the human brain in many aspects. Additionally, exploring how to utilize competitive learning, adaptive regulation, and synaptic plasticity to optimize the model's prediction results, automatically adjust its parameters, and achieve adaptive learning and dynamic adjustments from the perspective of neuroscience and brain-like cognition is of interest.Results and discussionExperimental results demonstrate that the Entity-BERT model achieves outstanding performance in entity recognition tasks within electronic medical records, surpassing other existing entity recognition models. This research not only provides more efficient and accurate natural language processing technology for the medical and health field but also introduces new ideas and directions for the design and optimization of deep learning models

Robust 3.7 V-Na2/3_{2/3}[Cu1/3_{1/3}Mn2/3_{2/3}]O2_2 Cathode for Na-ion Batteries

Na-ion batteries (NIBs), which are recognized as a next-generation alternative technology for energy storage, still suffer from commercialization constraints due to the lack of low-cost, high-performance cathode materials. Since our first discovery of Cu$^{3+}$/Cu$^{2+}$ electrochemistry in 2014, numerous Cu-substituted/doped materials have been designed for NIBs. However for almost ten years, the potential of Cu$^{3+}$/Cu$^{2+}$ electrochemistry has been grossly underappreciated and normally regarded as a semielectrochemically active redox. Here, we re-synthesized P2-Na$_{2/3}$[Cu$_{1/3}$Mn$_{2/3}$]O$_2$ and reinterpreted it as a high-voltage, cost-efficient, air-stable, long-life, and high-rate cathode material for NIBs, which demonstrates a high operating voltage of 3.7 V and a completely active Cu$^{3+}$/Cu$^{2+}$ redox reaction. The 2.3 Ah cylindrical cells exhibit excellent cycling (93.1% capacity after 2000 cycles), high rate (97.2% capacity at 10C rate), good low-temperature performance (86.6% capacity at -30$^\circ$C), and high safety, based on which, a 56 V-11.5 Ah battery pack for E-bikes is successfully constructed, exhibiting stable cycling (96.5% capacity at the 800th cycle) and a long driving distance (36 km, tester weight 65 kg). This work offers a commercially feasible cathode material for low-cost, high-voltage NIBs, paving the way for advanced NIBs in power and stationary energy storage applications.Comment: 15 pages, 3 figures, 1 tabl