We report a facile strategy to synthesize intermetallic nanoparticle （iNP） electrocatalysts via one-pot pyrolysis of a zeolitic imidazolate framework, ZIF-8, encapsulating precious metal nanoparticles （NPs）. ZIF-8...We report a facile strategy to synthesize intermetallic nanoparticle （iNP） electrocatalysts via one-pot pyrolysis of a zeolitic imidazolate framework, ZIF-8, encapsulating precious metal nanoparticles （NPs）. ZIF-8 serves not only as precursor for N-doped carbon （NC）, but also as Zn source for the formation of intermetallic or alloy NPs with the encapsulated metals. The resulting sub-4 nm PtZn iNPs embedded in NC exhibit high sintering resistance up to 1,000℃. Importantly, the present methodology allows fine-tuning of both composition （e.g., PdZn and RhZn iNPs, as well as AuZn and RuZn alloy NPs） and size （2.4, 3.7, and 5.4 nm PtZn） of the as-formed bimetallic NPs. To the best of our knowledge, this is the first report of a metal-organic framework （MOF） with multiple functionalities, such as secondary metal source, carbon precursor, and size-regulating reagent, which promote the formation of iNPs. This work opens a new avenue for the synthesis of highly uniform and stable iNPs.展开更多
Many few-shot learning approaches have been designed under the meta-learning framework, which learns from a variety of learning tasks and generalizes to new tasks. These meta-learning approaches achieve the expected p...Many few-shot learning approaches have been designed under the meta-learning framework, which learns from a variety of learning tasks and generalizes to new tasks. These meta-learning approaches achieve the expected performance in the scenario where all samples are drawn from the same distributions (i.i.d. observations). However, in real-world applications, few-shot learning paradigm often suffers from data shift, i.e., samples in different tasks, even in the same task, could be drawn from various data distributions. Most existing few-shot learning approaches are not designed with the consideration of data shift, and thus show downgraded performance when data distribution shifts. However, it is non-trivial to address the data shift problem in few-shot learning, due to the limited number of labeled samples in each task. Targeting at addressing this problem, we propose a novel metric-based meta-learning framework to extract task-specific representations and task-shared representations with the help of knowledge graph. The data shift within/between tasks can thus be combated by the combination of task-shared and task-specific representations. The proposed model is evaluated on popular benchmarks and two constructed new challenging datasets. The evaluation results demonstrate its remarkable performance.展开更多
文摘We report a facile strategy to synthesize intermetallic nanoparticle （iNP） electrocatalysts via one-pot pyrolysis of a zeolitic imidazolate framework, ZIF-8, encapsulating precious metal nanoparticles （NPs）. ZIF-8 serves not only as precursor for N-doped carbon （NC）, but also as Zn source for the formation of intermetallic or alloy NPs with the encapsulated metals. The resulting sub-4 nm PtZn iNPs embedded in NC exhibit high sintering resistance up to 1,000℃. Importantly, the present methodology allows fine-tuning of both composition （e.g., PdZn and RhZn iNPs, as well as AuZn and RuZn alloy NPs） and size （2.4, 3.7, and 5.4 nm PtZn） of the as-formed bimetallic NPs. To the best of our knowledge, this is the first report of a metal-organic framework （MOF） with multiple functionalities, such as secondary metal source, carbon precursor, and size-regulating reagent, which promote the formation of iNPs. This work opens a new avenue for the synthesis of highly uniform and stable iNPs.
基金supported by the National Natural Science Foundation of China (Grant Nos. 62176014, U1836206, 61773361, U1811461).
文摘Many few-shot learning approaches have been designed under the meta-learning framework, which learns from a variety of learning tasks and generalizes to new tasks. These meta-learning approaches achieve the expected performance in the scenario where all samples are drawn from the same distributions (i.i.d. observations). However, in real-world applications, few-shot learning paradigm often suffers from data shift, i.e., samples in different tasks, even in the same task, could be drawn from various data distributions. Most existing few-shot learning approaches are not designed with the consideration of data shift, and thus show downgraded performance when data distribution shifts. However, it is non-trivial to address the data shift problem in few-shot learning, due to the limited number of labeled samples in each task. Targeting at addressing this problem, we propose a novel metric-based meta-learning framework to extract task-specific representations and task-shared representations with the help of knowledge graph. The data shift within/between tasks can thus be combated by the combination of task-shared and task-specific representations. The proposed model is evaluated on popular benchmarks and two constructed new challenging datasets. The evaluation results demonstrate its remarkable performance.
