S.M. Low

432 citations
12 papers · 324 indexed · 1 hit paper · h-index 6

Impact in

Papers in

    • Advanced Polymer Synthesis and Characterization 9
    • Polymer crystallization and properties 5
    • Polymer Science and PVC 4
    • Polymer Nanocomposites and Properties 3

S.M. Low

10 papers receiving 321 citations

Hit Papers

Applied Machine Learning for Prediction of CO2 Adsorption on Biomass Waste-Derived Porous Carbons 2021 · 273 citations
2730+1+3Years since publication50100150200250

Peers

S.M. Low
Comparison fields: 5 of 65
  • Industrial and Manufacturing Engineering 64
  • Water Science and Technology 52
  • Pollution 34
  • Mechanical Engineering 108
  • Catalysis 18
Replace Haenam Jang with:
Haenam Jang South Korea
Tudor Sajin Romania
Chi-Hyeon Lee South Korea
Gábor Muránszky Hungary
Kwinam Park South Korea
Shally Gupta India
Tao Tan China
Antoine Leybros France
Ruosong Xie China
Linlin Yi China
S.M. Low relative to Haenam Jang South Korea Haenam Jang's profile →
Citations per field
00.5×5.4×
Haenam Jang · 1×
Citations per year

Countries citing papers authored by S.M. Low

Since Specialization
Citations

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

Fields of papers citing papers by S.M. Low

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by S.M. Low. 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 S.M. Low. The network helps show where S.M. Low may publish in the future.

Co-authors

The 9 scholars most cited alongside S.M. Low, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with S.M. Low Line = papers co-authored together S.M. Low links everyone, so they are left out of the graph.

All Works

12 of 12 papers shown
#Work
1
Applied Machine Learning for Prediction of CO2 Adsorption on Biomass Waste-Derived Porous Carbons
Hit paper breakdown →
2021273
2 199410
3 19977
4 19937
5 19947
6 19947
7 19935
8 19943
9 19943
10 19921
11 19931
12 19930

About S.M. Low

S.M. Low is a scholar working on Organic Chemistry, Polymers and Plastics, Materials Chemistry, Biomaterials and Physical and Theoretical Chemistry, having authored 12 papers that have together received 324 indexed citations. Recurring topics across this work include Advanced Polymer Synthesis and Characterization (9 papers), Polymer crystallization and properties (5 papers), Polymer Science and PVC (4 papers), Polymer Nanocomposites and Properties (3 papers), Advanced Physical and Chemical Molecular Interactions (2 papers), biodegradable polymer synthesis and properties (2 papers), Adsorption, diffusion, and thermodynamic properties of materials (1 paper) and Epoxy Resin Curing Processes (1 paper). The work is most often cited by research in Industrial and Manufacturing Engineering (64 citations), Water Science and Technology (52 citations), Pollution (34 citations), Mechanical Engineering (108 citations) and Catalysis (18 citations). S.M. Low has collaborated with scholars based in Singapore and South Korea. Frequent co-authors include Yong Sik Ok, Jie Li, Ki Bong Lee, Pavani Dulanja Dissanayake, Xiaonan Wang, Manu Suvarna, Xiangzhou Yuan, S. H. Goh and Jiebin Peng. Their work appears in journals such as European Polymer Journal, Macromolecules, Polymers for Advanced Technologies, Polymer Bulletin and Environmental Science & Technology.

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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