Dylan J. Kirsch

2.1k total citations · 1 hit paper
17 papers, 1.8k citations indexed

About

Dylan J. Kirsch is a scholar working on Electrical and Electronic Engineering, Materials Chemistry and Automotive Engineering. According to data from OpenAlex, Dylan J. Kirsch has authored 17 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Electrical and Electronic Engineering, 9 papers in Materials Chemistry and 5 papers in Automotive Engineering. Recurrent topics in Dylan J. Kirsch's work include Advancements in Battery Materials (7 papers), Advanced Battery Materials and Technologies (5 papers) and Machine Learning in Materials Science (4 papers). Dylan J. Kirsch is often cited by papers focused on Advancements in Battery Materials (7 papers), Advanced Battery Materials and Technologies (5 papers) and Machine Learning in Materials Science (4 papers). Dylan J. Kirsch collaborates with scholars based in United States, China and Germany. Dylan J. Kirsch's co-authors include Liangbing Hu, Yudi Kuang, Chaoji Chen, Tingting Gao, Steven D. Lacey, Yiju Li, Jianwei Song, Boyang Liu, John W. Connell and Yi Lin and has published in prestigious journals such as Nature, Advanced Materials and Nano Letters.

In The Last Decade

Dylan J. Kirsch

16 papers receiving 1.8k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Thick Electrode Batteries: Principles, Opportunities, and Challenges

2019 651 citations Yudi Kuang, Chaoji Chen et al. Advanced Energy Materials profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Dylan J. Kirsch United States	10	1.1k	617	565	445	297	17	1.8k
Jihai Zhang China	24	1.1k 1.0×	598 1.0×	294 0.5×	660 1.5×	501 1.7×	66	2.2k
Younghyun Cho South Korea	29	2.2k 2.0×	733 1.2×	540 1.0×	938 2.1×	379 1.3×	107	3.0k
Xi Xu China	22	766 0.7×	507 0.8×	247 0.4×	425 1.0×	248 0.8×	44	1.4k
Rui Sun China	27	1.8k 1.7×	603 1.0×	457 0.8×	199 0.4×	415 1.4×	69	2.2k
Yixia Zhao China	22	922 0.8×	244 0.4×	244 0.4×	532 1.2×	263 0.9×	43	1.6k
Yuxiang Zhu United States	22	841 0.8×	387 0.6×	607 1.1×	595 1.3×	544 1.8×	44	1.9k
Qiuwei Shi China	22	1.5k 1.4×	327 0.5×	528 0.9×	446 1.0×	486 1.6×	39	2.1k
Hao Jia China	29	2.7k 2.4×	1.0k 1.7×	778 1.4×	345 0.8×	363 1.2×	84	3.3k
Ruijie Zhu Japan	25	968 0.9×	332 0.5×	214 0.4×	349 0.8×	452 1.5×	49	1.9k
Baigang An China	33	2.0k 1.9×	577 0.9×	724 1.3×	711 1.6×	694 2.3×	109	3.3k

Countries citing papers authored by Dylan J. Kirsch

Since Specialization

Citations

This map shows the geographic impact of Dylan J. Kirsch's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dylan J. Kirsch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dylan J. Kirsch more than expected).

Fields of papers citing papers by Dylan J. Kirsch

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dylan J. Kirsch. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dylan J. Kirsch. The network helps show where Dylan J. Kirsch may publish in the future.

Co-authorship network of co-authors of Dylan J. Kirsch

This figure shows the co-authorship network connecting the top 25 collaborators of Dylan J. Kirsch. A scholar is included among the top collaborators of Dylan J. Kirsch based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dylan J. Kirsch. Dylan J. Kirsch is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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17 of 17 papers shown

Yu, Heshan, Dylan J. Kirsch, Rohit Pant, et al.. (2025). Real-time experiment-theory closed-loop interaction for autonomous materials science. Science Advances. 11(27). eadu7426–eadu7426. 2 indexed citations

Park, Ji Hun, Dylan J. Kirsch, Rohit Pant, et al.. (2024). Superconducting phase diagram in BixNi1–x thin films: The effects of Bi stoichiometry on superconductivity. Physical Review Materials. 8(7).

Allec, Sarah I., Eric S. Muckley, Nathan S. Johnson, et al.. (2024). A Case Study of Multimodal, Multi-institutional Data Management for the Combinatorial Materials Science Community. Integrating materials and manufacturing innovation. 13(2). 406–419. 2 indexed citations

Johnson, Nathan S., Aashwin Mishra, Dylan J. Kirsch, & Apurva Mehta. (2024). Active Learning for Rapid Targeted Synthesis of Compositionally Complex Alloys. Materials. 17(16). 4038–4038. 1 indexed citations

Kirsch, Dylan J., et al.. (2024). An instrumentation guide to measuring thermal conductivity using frequency domain thermoreflectance (FDTR). Review of Scientific Instruments. 95(10). 5 indexed citations

Ma, Ji, et al.. (2024). The development of an augmented machine learning approach for the additive manufacturing of thermoelectric materials. Journal of Manufacturing Processes. 116. 165–175. 8 indexed citations

Zeng, Minxiang, Yipu Du, Qiang Jiang, et al.. (2023). High-throughput printing of combinatorial materials from aerosols. Nature. 617(7960). 292–298. 106 indexed citations

Wang, Hsin, et al.. (2021). Synthesis, structure, electronic and thermal properties of sphalerite CuZn₂InS₄. Dalton Transactions. 50(47). 17611–17617. 10 indexed citations

Ren, Yaoyu, Drew Stasak, Huilong Hou, et al.. (2020). High-Throughput Exploration of Lithium-Alloy Protection Layers for High-Performance Lithium-Metal Batteries. ACS Applied Energy Materials. 3(3). 2547–2555. 4 indexed citations

10.

Kuang, Yudi, Chaoji Chen, Dylan J. Kirsch, & Liangbing Hu. (2019). Thick Electrode Batteries: Principles, Opportunities, and Challenges. Advanced Energy Materials. 9(33). 651 indexed citations breakdown →

11.

Lacey, Steven D., Qi Dong, Zhennan Huang, et al.. (2019). Stable Multimetallic Nanoparticles for Oxygen Electrocatalysis. Nano Letters. 19(8). 5149–5158. 117 indexed citations

12.

Kirsch, Dylan J., Steven D. Lacey, Yudi Kuang, et al.. (2019). Scalable Dry Processing of Binder-Free Lithium-Ion Battery Electrodes Enabled by Holey Graphene. ACS Applied Energy Materials. 2(5). 2990–2997. 79 indexed citations

13.

Lacey, Steven D., Dylan J. Kirsch, Yiju Li, et al.. (2018). Extrusion‐Based 3D Printing of Hierarchically Porous Advanced Battery Electrodes. Advanced Materials. 30(12). e1705651–e1705651. 281 indexed citations

14.

Chen, Chaoji, Jianwei Song, Shuze Zhu, et al.. (2018). Scalable and Sustainable Approach toward Highly Compressible, Anisotropic, Lamellar Carbon Sponge. Chem. 4(3). 544–554. 311 indexed citations

15.

Xie, Hua, Kun Fu, Chunpeng Yang, et al.. (2018). Necklace‐Like Silicon Carbide and Carbon Nanocomposites Formed by Steady Joule Heating. Small Methods. 2(4). 21 indexed citations

16.

Wan, Jiayu, Jianwei Song, Zhi Yang, et al.. (2017). Highly Anisotropic Conductors. Advanced Materials. 29(41). 103 indexed citations

17.

Liu, Yang, Yun Qiao, Ying Zhang, et al.. (2017). 3D printed separator for the thermal management of high-performance Li metal anodes. Energy storage materials. 12. 197–203. 117 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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