Ultrasensitive Electrochemical Biomolecular Detection Using Nanostructured Microelectrodes
Bibliographic record
Abstract
Electrochemical sensors have the potential to achieve sensitive, specific, and low-cost detection of biomolecules--a capability that is ever more relevant to the diagnosis and monitored treatment of disease. The development of devices for clinical diagnostics based on electrochemical detection could provide a powerful solution for the routine use of biomarkers in patient treatment and monitoring and may overcome the many issues created by current methods, including the long sample-to-answer times, high cost, and limited prospects for lab-free use of traditional polymerase chain reaction, microarrays, and gene-sequencing technologies. In this Account, we summarize the advances in electrochemical biomolecular detection, focusing on a new and integrated platform that exploits the bottom-up fabrication of multiplexed electrochemical sensors composed of electrodeposited noble metals. We trace the evolution of these sensors from gold nanoelectrode ensembles to nanostructured microelectrodes (NMEs) and discuss the effects of surface morphology and size on assay performance. The development of a novel electrocatalytic assay based on Ru(3+) adsorption and Fe(3+) amplification at the electrode surface as a means to enable ultrasensitive analyte detection is discussed. Electrochemical measurements of changes in hybridization events at the electrode surface are performed using a simple potentiostat, which enables integration into a portable, cost-effective device. We summarize the strategies for proximal sample processing and detection in addition to those that enable high degrees of sensor multiplexing capable of measuring 100 different analytes on a single chip. By evaluating the cost and performance of various sensor substrates, we explore the development of practical lab-on-a-chip prototype devices. By functionalizing the NMEs with capture probes specific to nucleic acid, small molecule, and protein targets, we can successfully detect a wide variety of analytes at clinically relevant concentrations and speeds. Using this platform, we have achieved attomolar detection levels of nucleic acids with overall assay times as short as 2 min. We also describe the adaptation of the sensing platform to allow for the measurement of uncharged analytes--a challenge for reporter systems that rely on the charge of an analyte. Furthermore, the capabilities of this system have been applied to address the many current and important clinical challenges involving the detection of pathogenic species, including both bacterial and viral infections and cancer biomarkers. This novel electrochemical platform, which achieves large molecular-to-electrical amplification by means of its unique redox-cycling readout strategy combined with rapid and efficient analyte capture that is aided by nanostructured microelectrodes, achieves excellent specificity and sensitivity in clinical samples in which analytes are present at low concentrations in complex matrices.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".