MétaCan
Menu
Back to cohort
Record W2280804143 · doi:10.1149/ma2014-02/49/2188

Designing of a Novel Potentiometric Sensor for Selective Determination of Chromium (III) Ion in Real Samples

2014· article· en· W2280804143 on OpenAlexaff
Maryam Hariri, Sylvie Morin

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsYork University
Fundersnot available
KeywordsChromiumPotentiometric titrationAnalyteIon selective electrodePotentiometric sensorChemistryElectrodeIonophoreMetal ions in aqueous solutionMembraneInorganic chemistryMetalChromatographySelectivity

Abstract

fetched live from OpenAlex

The purpose of this work is to design and develop a sensitive and selective electrochemical sensor for quantification analysis of Chromium (III) in different samples, such as drinking water, seawater, pharmaceutical products, soil and food. Chromium is an essential metal in small amounts while toxic in larger quantities. Recommended chromium intakes are provided in the dietary reference intakes (DRIs) developed by the institute of Medicine of the National Academy of science. Serum chromium levels normally range from less than 0.05 up to 0.5 micrograms/milliliter (mcg/mL) in humans. Chromium blood test is performed in laboratories to diagnose chromium poisoning or deficiency. For medical and environmental reasons, it is of great importance to determine how much of this metal ion is present in such media. To date, many Cr (III) selective electrodes with PVC membrane, based on various ionophores, have been introduced; however, these sensors suffer from the disadvantages of significant interferences from other cations, deviation from Nernstian behavior, small linear range and narrow pH range of operation [1]. The main goal of this project is to fabricate a potentiometric sensor which is selective towards Cr (III) ions, and does not have the disadvantages of the above-mentioned Cr (III)-selective membrane sensors. In this regards, TiO2/FTO glasses modified with glyoxal bis (2-hydroxyanil) (GBHA) as the ionophore (analyte recognition part) [2] were prepared, and investigated as Cr (III)-selective electrodes. The first component of the proposed potentiometric sensor is the working electrode. TiO2/FTO glass substrate was chosen as the working electrode material. The TiO2 film was prepared following a standard method described elsewhere [3]. The morphology of the TiO2 films was investigated by scanning electron microscopy (SEM) to assure that the TiO2 particles formed a homogenous porous film and to monitor film thickness. The TiO2/FTO substrates were subsequently modified with the ionophore of interest (GBHA) and the potentials of the varying concentration solutions of Cr (III) were read form the voltmeter upon increasing the concentration of the test solutions. We are also planning to apply other substrates as the working electrode, such as gold electrodes using thiol chemistry to chemically immobilise the ionophores. Our results demonstrated that the proposed potentiometric sensor exhibits a Nernstian response for Cr (III) ions over a wide concentration range (1.0×10-7 M-1.0×10-2 M); it also showed a fast response time, and its potential response remained unaffected of PH in a wide range. Moreover, the performance of the sensor is discussed in terms of stability, response time, and possible interferences from other ions. The proposed sensor is to be tested for the analysis of some water samples and food materials for the determination of Cr (III) ions. The fabricated sensor offered simplicity, rapidity, and reliability as an analytical tool. The second component of the constructed Cr (III)-selective potentiometric sensor is the ionophore or the ion receptor (GBHA), which is responsible for interacting with the Cr (III) ions and therefore producing a signal (voltage). We tried to optimize the ways to immobilize the ionophore on the working electrode, using different linkers, in order to increase the stability as well as the life time of our fabricated sensor. Distribution of the ion receptor on the surface is important to prevent aggregation prior or upon binding. In addition, various surfaces’ chemistry will be explored to prevent physical adsorption of other components of the samples. References: [1] Shamsipur, M.; Soleymanpour, A.; Akhond, M.; Sharghi, H. Electroanalysis. 2005, 17 (9), 776. [2] Milligan, C.W.; Lindstrom, F. Anal. Chem., 1972, 44 (11), 1822. [3] Sepehrifard, A.; Stublla, A.; Haftchenary, S.; ChenS.; Potvin, P.G.; Morin.; Journal of New Materials for Electrochemical Systems., 2008 , 11, 281.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.028
GPT teacher head0.264
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 source (direct Gemma or distilled Codex), 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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicAnalytical Chemistry and SensorsFrench-language works237,207