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Record W2108404701 · doi:10.1039/c4nr06093h

Poly (<i>N</i>-isopropylacrylamide)-<i>co</i>-(acrylic acid) microgel/Ag nanoparticle hybrids for the colorimetric sensing of H<sub>2</sub>O<sub>2</sub>

2015· article· en· W2108404701 on OpenAlexafffund
Deman Han, Qiang Zhang, Michael J. Serpe

Bibliographic record

VenueNanoscale · 2015
Typearticle
Languageen
FieldEngineering
TopicElectrochemical sensors and biosensors
Canadian institutionsUniversity of Alberta
FundersChina Postdoctoral Science FoundationNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaUniversity of Alberta
KeywordsPoly(N-isopropylacrylamide)Acrylic acidNanoparticleMaterials sciencePolymer chemistryChemical engineeringNanotechnologyNuclear chemistryChemistryPolymerCopolymerComposite materialEngineering

Abstract

fetched live from OpenAlex

Poly (N-isopropylacrylamide)-co-(acrylic acid) (pNIPAm-co-AAc) microgels composed of Ag nanoparticles (Ag NPs) have been synthesized and employed for the colorimetric sensing of H2O2. Each pNIPAm-co-AAc microgel, which exhibited a diameter of ∼800 nm, contained multiple Ag NPs (diameter of ∼5 nm), and solutions of these hybrid materials showed a UV-vis absorption band at ∼400 nm. This is due to the excitation of the Ag NP surface plasmon. We go on to show that the intensity of this absorption band is dependent on the concentration of H2O2 in solution. Specifically, in the presence of H2O2 the magnitude of the absorption peak dramatically decreases in a linear fashion over the concentration range of 0.30 to 3.00 μM H2O2 (r(2) = 0.9918). We go on to show that the response is selective for H2O2 and can still function in complex mixtures, e.g., we showed that the response is still robust in milk samples. While Ag NPs themselves can exhibit similar responses, this system has many benefits including sample processing and long term stability - i.e., Ag NPs are destabilized in solutions of a certain pH, and aggregate readily. Our microgel/Ag NP hybrids have been shown to be extremely stable and are easily purified prior to use by simple centrifugation/washing protocols. This system is simple and straightforward to use, is low cost, and can be used in complex media, which makes it practical for analyzing complex biological and environmental samples.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.216
Teacher spread0.203 · 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.

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".

Quick stats

Citations68
Published2015
Admission routes2
Has abstractyes

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