Bibliographic record
Abstract
The ability of microfluidics to rapidly mix reagent; provide homogeneous reaction environments; continuously vary reaction conditions; and add reagents at precise time intervals during reaction progression, has made it an attractive technology for a myriad of applications (DeMello J., 2004 ; deMello A. J., 2006). Over the past decade, microfluidic devices have enabled screening of a variety of reaction conditions by systematically varying flow rates, temperature, and reactant concentrations in order to optimize the quality of the resulting products using very small amounts of reagents (deMello A. J., 2006 ; Yen B. K., 2003). In parallel, there has been an increasing interest in the development of novel nanoparticle and microparticle technologies for drug delivery, imaging, bioanalysis, photonics and optoelectronic applications. The convergence of microfluidic and particle technologies has shown considerable promise allowing for the development of inorganic nanoparticles (Yen B. K., 2005 ; Wagner J., 2005 ; Shestopalov I., 2004 ; Krishnadasan S., 2007 ; Edel J. B., 2002 ; Chan E. M., 2005) and microparticles (Xu S. ; 2005), and in some cases with narrow size distribution or distinct shapes, addressing an important challenge for their maximal exploitation. Relatively little has been done to harness the benefits of microfluidics for the synthesis of organic nanoparticles. This is particularly important since the synthesis of biodegradable polymeric nanoparticles by bulk mixing and nanoprecipitation (Quintanar-Guerrero D., 1998) of drugs and biodegradable polymeric precursors typically lacks control over the mixing processes, which may compromise the properties of the resulting nanoparticles. Rapid and tunable mixing in microfluidics may allow for better control over the process of nanoprecipitation, and also enable screening of various formulation conditions on a single platform by varying parameters such as flow rates, precursor composition, and mixing time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".