Interfacial Doping of Heteroatom in Porous SnO₂ for Highly Sensitive Surface Properties

The design and synthesis of heteroatom-doping porous materials with unique surface/interfaces are of great significance for enhancing the sensitive surface performance in the fields of catalytic energy, especially gas sensor, CO oxidation, and ammonium perchlorate decomposition. Usually, the template method followed by a high-temperature calcination process is considered as the routes of choice in preparing ion-doped porous materials, but it requires extra templates and will undergo complicated steps. Here, we present a simple fusion/diffusion-controlled intermetallics-transformation method to synthesize various heteroatom-doping porous SnO₂ only by changing the species of intermetallics. By this new method, Ni-doped popcornlike SnO₂ with plenty of ∼30 nm pores and two kinds of Cu-doped SnO₂ nanocages was successfully constructed. Phase-evolution investigations demonstrated that growth kinetics, diffusion, and solubility of the intermediates are highly related to the architecture of final products. Moreover, low-solid-solution limit of MO_<i>x</i> (M: Ni, Cu) in SnO₂ made the ion dope close to the surface to form a special surface/interfaces structure, and selective removal of MO_<i>x</i> produce abundant pores to increase the surface area. As a consequence, Ni-doped composite exhibits higher sensitivity in formaldehyde detection with a relative low-operating temperature in a short response time (i.e., 23.7–50 ppm formaldehyde, 170 °C, and 5 s) and Cu-doped composites show excellent activity in decreasing the catalytic temperature of CO oxidation and ammonium perchlorate decomposition. The fusion/diffusion-controlled intermetallics-transformation method reported in this work could be readily adopted for the synthesis of other active heteroatom-doping porous materials for multipurpose uses

Mean of VDs driven by high KE nodes in different valid layers.

Mean of VDs driven by high KE nodes in different valid layers.</p

Six fixed evolution patterns of publication’s idea.

(a,c,e,h,k,n) The evolution of VD of corresponding publications. (b,d,g,j,m,o) The evolution of idea trees of corresponding publications. All idea trees are pruned to ensure the visibility of the skeleton structure. All idea trees are visualized by the DOT algorithm [50]. Node size is rescaled in every idea tree and positively related to its KE. (f,i,l) The evolution of nodes’ KE in corresponding idea trees. These six patterns exist in a wide range of scientific fields and are not limited to geographic information system, ecology and climate change, computer vision, natural language processing, deep learning and geology.</p

The rules and results of the classification of idea trees’ evolution patterns.

The rules and results of the classification of idea trees’ evolution patterns.</p

The verification of scientific X-ray’s VD and DPI by prize data.

(a) The structure of the idea tree led by ‘A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity’ in 2021 (The Champion of science’s 2015 breakthrough). (b-j) The idea trees of runner-up papers of science’s 2015 breakthrough in 2021. The VD of the idea tree of the champion CRISPR is deeper than other publications, and more high KE nodes appear in the idea tree of CRISPR. (k) Identify the development potential of Nobel topics before they are awarded. Each point represents an award-winning article under the corresponding Nobel Prize topic. The ordinate is the maximum DPI within 1–8 years after the corresponding article was published. Points above the red dotted line are papers with DPI ≥ 1 in the time window. Since the average time interval from publication to awarding of prize-winning papers is 17 years, for a Nobel Prize-winning topic, if the maximum DPI of one of the prize-winning articles is over one within 1–8 years after its publication, it is considered that Scientific X-ray has successfully identified corresponding topic’s development potential.</p

The distribution of high-impact publications’ VD and the distribution of the VD inspired by a single article within idea trees.

The VD of 99% of high-impact publications is difficult to exceed six-hop. (b) 99% of the articles in the idea tree have difficulty contributing more than three-hop to the VD.</p

In Situ Growth of MoS₂ Nanosheet Arrays and TS₂ (T = Fe, Co, and Ni) Nanocubes onto Molybdate for Efficient Oxygen Evolution Reaction and Improved Hydrogen Evolution Reaction

Rationally designing efficient and low-price bifunctional electrocatalysts for oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) are vitally important to bring solar/electrical-to-hydrogen energy conversion processes into reality. Herein, we report on a synthetic method that leads to an in situ growth of ultrathin MoS₂ nanosheets and transition metal disulfide nanocubes onto the surface of Fe_1/3Co_1/3Ni_1/3MoO₄ nanorods for the first time. Such hybrids are found to serve as a bifunctional electrocatalyst with high activities for OER and HER, as represented by an impressive anodic and cathodic current density of 10 mA cm^–2 at 1.53 and −0.25 V, respectively. More importantly, the performance for OER is even better than that of IrO₂, the conventional noble metal electrocatalyst. These striking observations were interpreted in terms of the combination of strongly synergistic effect of multimetal components, large amount of exposed active site, and superaerophobia. The present methodology has been confirmed universal for synthesizing other molybdate solid solutions, which would open up new possibilities for designing novel non-noble bifunctional electrocatalysts for OER and HER

The framework of scientific X-ray.

We illustrate the pipeline utilizing two publications with different citation structures. (a) Retrieve the citing papers of the target publication, all links among the target publication and citing articles and all links among citing papers in the database. (b) Construct the citation network of the target publication based on the retrieval results. (c) Extract the idea tree from the citation network to reveal the flow of the target publication’s idea. (d) Utilize Knowledge Entropy (KE) to quantify the knowledge quality of nodes in the idea tree to highlight the powerful inheritor of the target idea. (e) Reproduce the evolution of the idea tree and quantify the degree of development of the target publication’s idea utilizing the Valid Depth (VD). (f) Utilize the Development Potential Index (DPI) to quantify the potential of the target publication and assess whether it is worth continuing to follow.</p

Top ten development potential publications in the field of deep learning, geoscience and Covid-19.

Top ten development potential publications in the field of deep learning, geoscience and Covid-19.</p

Detailed data descriptions, detailed model descriptions and experiment results.

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