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Record W4414741322 · doi:10.1093/clinchem/hvaf086.378

A-394 Scalable and continuous production of monodispersed gold nanospheres capped by citrate species for advanced lateral flow immunoassays

2025· article· en· W4414741322 on OpenAlexaff
Kei Kwan Li, Jianlong He, Younan Xia, Seth Kinoshita, Robert Greene, Christine Buerki

Bibliographic record

VenueClinical Chemistry · 2025
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsInstitute of Indigenous Peoples' HealthBiogate Laboratories (Canada)
Fundersnot available
KeywordsColloidal goldNanoparticleDeposition (geology)AnalyteContinuous flowNanoscopic scaleLimitingMicrofluidics

Abstract

fetched live from OpenAlex

Abstract Background Lateral flow immunoassays (LFI) based on gold nanoparticles have emerged as a reliable tool for point-of-care applications. The conventional particles prepared using the Turkevich method are poorly defined in shape and size, making them unsuitable for advanced analysis. Previously, we had developed a method based upon seed-mediated growth for the synthesis of gold nanoparticles with a perfectly spherical shape. The nanospheres exhibit the narrowest optical absorption peak, making them ideal for multiplexing analysis. However, the nanospheres are capped by cetyltrimethylammonium chloride/bromide (CTAC/B), toxic ligands that also impede the binding of analytes. In general, the formation of nanospheres requires a dedicated balance between the atom deposition and surface diffusion rate. As such, dropwise addition of the precursor must be used, limiting the scale of production needed for commercial application. Here, we address these limitations by demonstrating a scalable and continuous method for the synthesis of citrate-capped gold nanospheres for advanced lateral flow immunoassays. Methods The synthesis comprises two steps. Firstly, we developed a scalable method for the synthesis of gold nanocubes with uniform edges length by introducing the precursor in one shot, followed by incubation at an elevated temperature to transform the shape from cubic to spherical. Secondly, we used a simple method to exchange the CTAC/B on the nanospheres with citrate species. It involves the deposition of an ultrathin shell of fresh gold on the nanospheres in the presence of citrate, which can serve as a reducing agent for the precursor and a ligand for further surface modification. Results The gold nanospheres synthesized in the first step have a monodispersed size from 10-35 nm. Significantly, our recent study demonstrates that this protocol can be extended to a flow reactor with a throughput of 5 × 10?6 moles of Au per minute, achieving a tenfold increase over the batch process based on the Turkevich method. It enables the large-scale production of gold colloids to meet the requirements for commercial application. In the second step, the surface-bound CTAC/B are replaced with citrate species, as confirmed by Fourier-transform infrared spectroscopy. During gold deposition, the CTAC/B desorbs while citrate species adsorb on the surface. Ultraviolet–visible spectroscopy further confirms that colloidal stability is retained after ligand exchange. In addition, our recent study demonstrates that this approach is also adaptable to a flow reactor for scalable operation. Conclusion We have developed methods for the continuous and scalable production of monodispersed gold nanospheres sought for use as color markers in diagnostic applications such as LFI. The surface of the nanospheres can be made with citrate to match that of the conventional gold colloids. As such, one can directly incorporate the gold nanospheres into current commercial devices to achieve new capabilities such as enhanced sensitivity and quantitative analysis. The successful transformation of the two-step synthesis methodology into a continuous flow reactor production process demonstrates the robustness of the innovation for commercial-scale industrial use. The transition from batch to continuous synthesis represents a step change in large-scale manufacturing simplicity, cost and quality.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.245
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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