Correction: A Whole-Cell Biosensor for the Detection of Gold
Bibliographic record
Abstract
Geochemical exploration for gold (Au) is becoming increasingly important to the mining industry.Current processes for Au analyses require sampling materials to be taken from often remote localities.Samples are then transported to a laboratory equipped with suitable analytical facilities, such as Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) or Instrumental Neutron Activation Analysis (INAA).Determining the concentration of Au in samples may take several weeks, leading to long delays in exploration campaigns.Hence, a method for the on-site analysis of Au, such as a biosensor, will greatly benefit the exploration industry.The golTSB genes from Salmonella enterica serovar typhimurium are selectively induced by Au(I/III)-complexes.In the present study, the golTSB operon with a reporter gene, lacZ, was introduced into Escherichia coli.The induction of golTSB::lacZ with Au(I/III)-complexes was tested using a colorimetric b-galactosidase and an electrochemical assay.Measurements of the b-galactosidase activity for concentrations of both Au(I)-and Au(III)-complexes ranging from 0.1 to 5 mM (equivalent to 20 to 1000 ng g 21 or parts-per-billion (ppb)) were accurately quantified.When testing the ability of the biosensor to detect Au(I/III)-complexes (aq) in the presence of other metal ions (Ag(I), Cu(II), Fe(III), Ni(II), Co(II), Zn, As(III), Pb(II), Sb(III) or Bi(III)), cross-reactivity was observed, i.e. the amount of Au measured was either underor over-estimated.To assess if the biosensor would work with natural samples, soils with different physiochemical properties were spiked with Au-complexes.Subsequently, a selective extraction using 1 M thiosulfate was applied to extract the Au.The results showed that Au could be measured in these extracts with the same accuracy as ICP-MS (P,0.05).This demonstrates that by combining selective extraction with the biosensor system the concentration of Au can be accurately measured, down to a quantification limit of 20 ppb (0.1 mM) and a detection limit of 2 ppb (0.01 mM).
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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 | 0.004 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.138 | 0.071 |
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".