Bacterial Production of Selenium and Tellurium Nanostructures and their Biophysical-Chemical Characterization
Bibliographic record
Abstract
The demand for green and “low carbon” technologies has led to the exploitation of various metal or metalloid – metal(loid) – elements in various applications, among which renewable energy production is one of the most pressing. This tendency is resulting in a rapid accumulation of metal(loid) compounds in the environment, posing the emergency of the decontamination of polluted areas. Since most of the metal(loid)s of high technological and economic interest are scarcely present on our Earth’s crust, their recent increased use has also underlined the need of their reclamation to avoid their depletion. Selenium and tellurium are considered to date “energy-critical elements”, as they are of paramount importance to produce sustainable energy and to advance in the technological and biomedical fields. In view of the need to reclaim metalloids from different environmental matrices, the ability of several microorganisms to bioprocess selenium or tellurium compounds into their less bioavailable elemental states producing metalloid based nanomaterials acquires a double importance in terms of bioremediation strategy and technological relevance. This PhD project presents a deep study regarding the exploration of three different bacterial strains, i.e., Rhodococcus aetherivorans BCP1, Ochrobactrum sp. MPV1 and Stenotrophomonas maltophilia SeITE02, as cell factories for the biosynthesis of selenium or tellurium nanostructures. Key parameters influencing fundamental features of the material at the nanoscale, such as size and morphology, and their recovery (e.g., localization with respect to the bacterial cells) are analyzed in this thesis to lay the groundwork for the optimization of metalloid nanomaterial biosynthesis and the development of ad hoc strategies for their recovery from bacterial cultures. Moreover, the bioprocess strategies used by Ochrobactrum sp. MPV1 and S. maltophilia SeITE02 to cope with selenium toxicity are here explored to shed light into the mechanisms used by different microorganisms to thrive under metalloid pressure. The obtained biogenic selenium or tellurium nanostructures were then physically-chemically characterized, bringing to light (i) the structural similarities between chemogenic and biogenic nanomaterials, (ii) the unique feature of an organic material acting as stabilizing agent of the nanostructures, as well as (iii) their photoluminescence and electrical properties, which can be exploited for their future applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".