Visualization and Analysis of Mapping Knowledge Domain of Fluid Flow Related to Microfluidic Chip
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Microfluidic chips are important tools to study the microscopic flow of fluid. To better understand the research clues and development trends related to microfluidic chips, a bibliometric analysis of microfluidic chips was conducted based on 1115 paper records retrieved from the Web of Science Core Collection database. CiteSpace and VOSviewer software were used to analyze the distribution of annual paper quantity, country/region distribution, subject distribution, institution distribution, major source journals distribution, highly cited papers, coauthor cooperation relationship, research knowledge domain, research focuses, and research frontiers, and a knowledge domain map was drawn. The results show that the number of papers published on microfluidic chips increased from 2010 to 2023, among which China, the United States, Iran, Canada, and Japan were the most active countries in this field. The United States was the most influential country. Nanoscience, energy, and chemical industry and multidisciplinary materials science were the main fields of microfluidic chip research. Lab on a Chip, Microfluidics and Nanofluidics, and Journal of Petroleum Science and Engineering were the main sources of papers published. The fabrication of chips, as well as their applications in porous media flow and multiphase flow, is the main knowledge domain of microfluidic chips. Micromodeling, fluid displacement, wettability, and multiphase flow are the research focuses in this field currently. The research frontiers in this field are enhanced oil recovery, interfacial tension, and stability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".