Connor J. Taylor

1.3k total citations · 1 hit paper
16 papers, 864 citations indexed

About

Connor J. Taylor is a scholar working on Biomedical Engineering, Materials Chemistry and Organic Chemistry. According to data from OpenAlex, Connor J. Taylor has authored 16 papers receiving a total of 864 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Biomedical Engineering, 9 papers in Materials Chemistry and 4 papers in Organic Chemistry. Recurrent topics in Connor J. Taylor's work include Innovative Microfluidic and Catalytic Techniques Innovation (12 papers), Machine Learning in Materials Science (8 papers) and Microfluidic and Capillary Electrophoresis Applications (4 papers). Connor J. Taylor is often cited by papers focused on Innovative Microfluidic and Catalytic Techniques Innovation (12 papers), Machine Learning in Materials Science (8 papers) and Microfluidic and Capillary Electrophoresis Applications (4 papers). Connor J. Taylor collaborates with scholars based in United Kingdom, Singapore and Germany. Connor J. Taylor's co-authors include Richard A. Bourne, Thomas W. Chamberlain, Alexei A. Lapkin, Graeme Clemens, Jamie A. Manson, Brian Taylor, Rachel Grainger, Christopher N. Johnson, Kobi Felton and Alexander Pomberger and has published in prestigious journals such as Chemical Reviews, Nature Communications and Chemical Communications.

In The Last Decade

Connor J. Taylor

16 papers receiving 848 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.

A Brief Introduction to Chemical Reaction Optimization

2023 238 citations Connor J. Taylor, Alexander Pomberger et al. Chemical Reviews profile →

Peers

Connor J. Taylor

CTM

CSE

Adam D. Clayton United Kingdom

Brandon J. Reizman United States

Nicholas Holmes United Kingdom

Pieter Plehiers Belgium

Travis Hart United States

Lars P. E. Yunker Canada

Yuran Wang China

Vincenza Dragone United Kingdom

Florence H. Vermeire Belgium

Kobi Felton United Kingdom

Adam D. Clayton United Kingdom

Connor J. Taylor

651 ×1.4

379 ×1.2

213 ×1.4

161 ×1.2

CTM

170 ×1.3

CSE

Citations per year, relative to Connor J. Taylor Connor J. Taylor (= 1×) peers Adam D. Clayton

Countries citing papers authored by Connor J. Taylor

Since Specialization

Citations

This map shows the geographic impact of Connor J. Taylor'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 Connor J. Taylor with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Connor J. Taylor more than expected).

Fields of papers citing papers by Connor J. Taylor

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Connor J. Taylor. 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 Connor J. Taylor. The network helps show where Connor J. Taylor may publish in the future.

Co-authorship network of co-authors of Connor J. Taylor

This figure shows the co-authorship network connecting the top 25 collaborators of Connor J. Taylor. A scholar is included among the top collaborators of Connor J. Taylor 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 Connor J. Taylor. Connor J. Taylor is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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16 of 16 papers shown

Bai, Jiaru, Sebastian Mosbach, Connor J. Taylor, et al.. (2024). A dynamic knowledge graph approach to distributed self-driving laboratories. Nature Communications. 15(1). 462–462. 41 indexed citations

Taylor, Connor J., Alexander Pomberger, Kobi Felton, et al.. (2023). A Brief Introduction to Chemical Reaction Optimization. Chemical Reviews. 123(6). 3089–3126. 238 indexed citations breakdown →

Taylor, Connor J., Kobi Felton, Daniel Wigh, et al.. (2023). Accelerated Chemical Reaction Optimization Using Multi-Task Learning. ACS Central Science. 9(5). 957–968. 64 indexed citations

Zakrzewski, J., Polina Yaseneva, Connor J. Taylor, Matthew J. Gaunt, & Alexei A. Lapkin. (2023). Scalable Palladium-Catalyzed C(sp3)–H Carbonylation of Alkylamines in Batch and Continuous Flow. Organic Process Research & Development. 27(4). 649–658. 7 indexed citations

Pomberger, Alexander, Asif Iqbal Khan, Connor J. Taylor, et al.. (2022). The effect of chemical representation on active machine learning towards closed-loop optimization. Reaction Chemistry & Engineering. 7(6). 1368–1379. 34 indexed citations

Taylor, Connor J., Jamie A. Manson, Graeme Clemens, et al.. (2022). Modern advancements in continuous-flow aided kinetic analysis. Reaction Chemistry & Engineering. 7(5). 1037–1046. 26 indexed citations

Taylor, Connor J., Hikaru Seki, Mark J. Willis, et al.. (2021). An automated computational approach to kinetic model discrimination and parameter estimation. Reaction Chemistry & Engineering. 6(8). 1404–1411. 30 indexed citations

Taylor, Connor J., Michael Chapman, William R. Reynolds, et al.. (2021). Flow chemistry for process optimisation using design of experiments. Journal of Flow Chemistry. 11(1). 75–86. 40 indexed citations

Taylor, Connor J., et al.. (2021). Autonomous optimisation of a nanoparticle catalysed reduction reaction in continuous flow. Chemical Communications. 57(40). 4926–4929. 23 indexed citations

10.

Schotten, Christiane, Connor J. Taylor, Richard A. Bourne, et al.. (2020). Alternating polarity for enhanced electrochemical synthesis. Reaction Chemistry & Engineering. 6(1). 147–151. 57 indexed citations

11.

Taylor, Connor J., Jamie A. Manson, Mark J. Willis, et al.. (2020). Rapid, automated determination of reaction models and kinetic parameters. Chemical Engineering Journal. 413. 127017–127017. 53 indexed citations

12.

Taylor, Connor J., et al.. (2020). Imino Diels-Alder/transition metal catalyzed reactions to synthesise fused ring heterocycles. Journal of Organometallic Chemistry. 913. 121197–121197. 2 indexed citations

13.

Kapur, Nikil, et al.. (2020). A universal reactor platform for batch and flow: application to homogeneous and heterogeneous hydrogenation. Reaction Chemistry & Engineering. 5(10). 1903–1908. 11 indexed citations

14.

Blacker, A. John, Adam D. Clayton, Katherine E. Jolley, et al.. (2020). A practical experiment to teach students continuous flow and physico-chemical methods: acetylation of ethylene diamine in liquid bi-phase. Journal of Flow Chemistry. 11(1). 31–36. 2 indexed citations

15.

Clayton, Adam D., Artur M. Schweidtmann, Graeme Clemens, et al.. (2019). Automated self-optimisation of multi-step reaction and separation processes using machine learning. Chemical Engineering Journal. 384. 123340–123340. 122 indexed citations

16.

Clayton, Adam D., Jamie A. Manson, Connor J. Taylor, et al.. (2019). Algorithms for the self-optimisation of chemical reactions. Reaction Chemistry & Engineering. 4(9). 1545–1554. 114 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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