Triclosan ELISA Kits (DEIA6845)

Regulatory status: For research use only, not for use in diagnostic procedures.

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groundwater, surface water, well water, effluent, soil
Species Reactivity
Intended Use
The Triclosan ELISA Kit is an immunoassay for the quantitative and sensitive detection of Triclosan and Triclosan methyl in water (groundwater, surface water, well water, effluent), and soil.
Contents of Kit
1. Microtiter Plate coated with Goat -Anti Rabbit Antibody: 96 wells
2. Triclosan Antibody Solution: 1 x 6 mL
3. Triclosan Enzyme Conjugate: 1 x 6 mL
4. Triclosan Standards: 1 x 1.0 mL
5. Control: 1 x 1.0 mL
6. Diluent/Zero Standard (Sample Diluent): 1 x 30 mL
7. Color Solution: 1 x 16 mL
8. Stopping Solution
9. Washing Buffer (5X): 1 x 100 mL
Store all reagents at 2-8°C. Do not freeze. Reagents may be used until the expiration date on the box. For more detailed information, please download the following document on our website.
The Triclosan Plate Assay has an estimated minimum detectable concentration, based on a 90% B/Bo of 20 ppt (0.020 ppb).


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Continuous process applied to degradation of triclosan and 2.8-dichlorodibenzene-p-dioxin


Authors: Fidelis, Michel Zampieri; Abreu, Eduardo; Josue, Tatiana Gulminie; de Almeida, Lariana Negrao Beraldo; Lenzi, Giane Goncalves; Dos Santos, Onelia Aparecida Andreo

This study describes the use of a prototype for the continuous photocatalytic reaction process using Fe/Nb2O5-immobilized catalyst for triclosan and 2.8-dichlorodibenzene-p-dioxin (2.8-DCDD)'s degradation. The experiments were carried out with different parameters and matrices in a steady state. In addition, photolysis and photocatalytic tests were performed. The results indicated that the generation of 2.8-DCDD was observed in matrices with Cl-. The Fe/Nb2O5-immobilized catalysts were efficient in the degradation of triclosan and 2.8-dichlorodibenzene-p-dioxin. However, 2.8-DCDD formation was not observed in the ultra-pure water matrix, which indicated influence of ions. The photocatalysis was more efficient than the photolysis when comparing both matrices and radiation. Even with a radiation oscillation, the solar process showed positive results.

Exposure to selected preservatives in personal care products: case study comparison of exposure models and observational biomonitoring data


Authors: Aylward, Lesa; Vilone, Giulia; Cowan-Ellsberry, Christina; Arnot, Jon A.; Westgate, John N.; O'Mahony, Cian; Hays, Sean M.

Exposure models provide critical information for risk assessment of personal care product ingredients, but there have been limited opportunities to compare exposure model predictions to observational exposure data. Urinary excretion data from a biomonitoring study in eight individuals were used to estimate minimum absorbed doses for triclosan and methyl-, ethyl-, and n-propyl- parabens (TCS, MP, EP, PP). Three screening exposure models (European Commission Scientific Commission on Consumer Safety [SCCS] algorithms, ConsExpo in deterministic mode, and RAIDAR-ICE) and two higher-tier probabilistic models (SHEDS-HT, and Creme Care & Cosmetics) were used to model participant exposures. Average urinary excretion rates of TCS, MP, EP, and PP for participants using products with those ingredients were 16.9, 3.32, 1.9, and 0.91 mu g/kg-d, respectively. The SCCS default aggregate and RAIDAR-ICE screening models generally resulted in the highest predictions compared to other models. Approximately 60-90% of the model predictions for most of the models were within a factor of 10 of the observed exposures; similar to 30-40% of the predictions were within a factor of 3. Estimated exposures from urinary data tended to fall in the upper range of predictions from the probabilistic models. This analysis indicates that currently available exposure models provide estimates that are generally realistic. Uncertainties in preservative product concentrations and dermal absorption parameters as well as degree of metabolism following dermal absorption influence interpretation of the modeled vs. measured exposures. Use of multiple models may help characterize potential exposures more fully than reliance on a single model.

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