Recent advances in nanoprecipitation: from mechanistic insights to applications in nanomaterial synthesis
Bibliographic record
Abstract
Nanoprecipitation is a versatile, low-energy technique for synthesizing nanomaterials through controlled precipitation, enabling precise tuning of material properties. This review offers a comprehensive and up-to-date perspective on nanoprecipitation, focusing on its role in nanoparticle synthesis and its adaptability in designing diverse nanostructures. The review begins with the foundational principles of nanoprecipitation, emphasizing the impact of key parameters such as flow rate, mixing approach, injection rate, and Reynolds number on nanomaterial characteristics. It also discusses the influence of physicochemical factors, including solvent choice, polymer type, and drug properties. Various nanoprecipitation configurations-batch, flash, and microfluidic are examined for their specific advantages in controlling particle size, morphology, and internal structure. The review further explores the potential of nanoprecipitation to create complex nanostructures, such as core-shell particles, Janus nanoparticles, and porous and semiconducting polymer nanoparticles. Applications in biomedicine and other fields highlight nanoprecipitation's promise as a sustainable and tunable method for fabricating advanced nanomaterials. Finally, the review identifies future directions, including scaling microfluidic techniques, expanding compatibility with hydrophilic compounds, and integrating machine learning to further enhance the development of nanoprecipitation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".