Green and Cost-Effective Synthesis of Metallic Nanoparticles by Algae: Safe Methods for Translational Medicine
Bibliographic record
Abstract
Metal nanoparticles (NPs) have received much attention for potential applications in medicine (mainly in oncology, radiology and infectiology), due to their intriguing chemical, electronical, catalytical, and optical properties such as surface plasmon resonance (SPR) effect. They also offer ease in controlled synthesis and surface modification (e.g., tailored properties conferred by capping/protecting agents including N-, P-, COOH-, SH-containing molecules and polymers such as thiol, disulfide, ammonium, amine, and multidentate carboxylate), which allows (i) tuning their size and shape (e.g., star-shaped and/or branched) (ii) improving their stability, monodispersity, chemical miscibility, and activity, (iii) avoiding their aggregation and oxidation over time, (iv) increasing their yield and purity. The bottom-up approach, where the metal ions are reduced in the NPs grown in the presence of capping ligands, has been widely used compared to the top-down approach. Besides the physical and chemical synthesis methods, the biological method is gaining much consideration. Indeed, several drawbacks have been reported for the synthesis of NPs via physical (e.g., irradiation, ultrasonication) and chemical (e.g., electrochemisty, reduction by chemicals such as trisodium citrate or ascorbic acid) methods (e.g., cost, and/ortoxicity due to use of hazardous solvents, low production rate, use of huge amount of energy). However, (organic or inorganic) eco-friendly NPs synthesis exhibits a sustainable, safe, and economical solution. Thereby, a relatively new trend for fast and valuable NPs synthesis from (live or dead) algae (i.e., microalgae, macroalgae and cyanobacteria) has been observed, especially because of its massive presence on the Earth's crust and their unique properties (e.g., capacity to accumulate and reduce metallic ions, fast propagation). This article discusses the algal-mediated synthesis methods (either intracellularly or extracellularly) of inorganic NPs with special emphasis on the noblest metals, i.e., silver (Ag)- and gold (Au)-derived NPs. The key factors (e.g., pH, temperature, reaction time) that affect their biosynthesis process, stability, size, and shape are highlighted. Eventually, underlying molecular mechanisms, nanotoxicity and examples of major biomedical applications of these algal-derived NPs are presented.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".