Electrochemical Biosensors for Express Analysis of the Integral Toxicity of Polymer Materials.
Natalia Yu YudinaTatyana N KozlovaDaniil A BogachikhinMaria M KosareninaArlyapov Vyacheslav AlekseevichSergey Valerievich AlferovPublished in: Biosensors (2023)
Biosensors based on an oxygen electrode, a mediator electrode, and a mediator microbial biofuel cell (MFC) using the bacteria Gluconobacter oxydans B-1280 were formed and tested to determine the integral toxicity. G. oxydans bacteria exhibited high sensitivity to the toxic effects of phenol, 2,4-dinitrophenol, salicylic and trichloroacetic acid, and a number of heavy metal ions. The system " G. oxydans bacteria-ferrocene-graphite-paste electrode" was superior in sensitivity to biosensors formed using an oxygen electrode and MFC, in particular regarding heavy metal ions (EC 50 of Cr 3+ , Mn 2+, and Cd 2+ was 0.8 mg/dm 3 , 0.3 mg/dm 3 and 1.6 mg/dm 3 , respectively). It was determined that the period of stable functioning of electrochemical systems during measurements was reduced by half (from 30 to 15 days) due to changes in the enzyme system of microbial cells when exposed to toxicants. Samples of the products made from polymeric materials were analyzed using developed biosensor systems and standard biotesting methods based on inhibiting the growth of duckweed Lemna minor , reducing the motility of bull sperm, and quenching the luminescence of the commercial test system "Ecolum". The developed bioelectrocatalytic systems were comparable in sensitivity to commercial biosensors, which made it possible to correlate the results and identify, by all methods, a highly toxic sample containing diphenylmethane-4,4'-diisocyanate according to GC-MS data.
Keyphrases
- label free
- heavy metals
- quantum dots
- gold nanoparticles
- carbon nanotubes
- microbial community
- molecularly imprinted
- induced apoptosis
- oxidative stress
- solid state
- ionic liquid
- health risk assessment
- health risk
- glycemic control
- cell cycle arrest
- electronic health record
- sensitive detection
- energy transfer
- cell therapy
- stem cells
- type diabetes
- escherichia coli
- cell proliferation
- cancer therapy
- staphylococcus aureus
- mass spectrometry
- metabolic syndrome
- machine learning
- drinking water
- cystic fibrosis
- deep learning
- skeletal muscle
- solid phase extraction