Machine Learning-Mediated Ultrasensitive Detection of Citrinin and Associated Mycotoxins in Real Food Samples Discerned from a Photoluminescent Carbon Dot Barcode Array

Economically viable remote sensing of foodborne contaminants using minimalistic chemical reagents and simultaneous automation calls for a concrete integration of a chemical detection strategy with artificial intelligence. In a first of its kind, we report the ultrasensitive detection of citrinin and associated mycotoxins like aflatoxin B1 and ochratoxin A using an Alizarin Red S (ARS) and cystamine-derived carbon dot (CD) that aptly amalgamate with machine learning algorithms for automation. The photoluminescence response of the CD as a function of various solvents and pH is used to generate array channels that are further modulated in the presence of the mycotoxins whose digital images were acquired to determine pixelation, essentially creating a barcode. The barcode was fed to machine learning algorithms that actualize and intertwine convoluted databases, demonstrating Extreme Gradient Boosting (XGBoost) as the optimized model out of eight algorithms tested. Spiked samples of wheat, rice, gram, maize, coffee, and milk were used to evaluate the testing model where an exemplary accuracy of 100% even at 10 pmol of mycotoxin concentration was achieved. Most importantly, the coexistence of mycotoxins could also be detected through the CD array and XGBoost synergy hinting toward a broader scope of the developed methodology for smart detection of foodborne contaminants.

Electrochemical oxidation of As(iii) on Pd immobilized Pt surface: kinetics and sensing performance

Pd nanoparticles were electrochemically immobilized on a Pt surface in the presence of sodium dodecyl sulfate (SDS) molecules to study the electrokinetics of arsenite oxidation reactions and the corresponding sensing activities. The X-ray photoelectron spectroscopy (XPS) analysis showed that on the Pt surface, Pd atoms exist as adatoms and the contents of Pd(0) and Pd(ii) were 75.72 and 24.28 at%, respectively, and the particle sizes were in the range of 61-145 nm. The experimental results revealed that the catalytic efficiency as well as the charge transfer resistance (at the redox potential of the Fe(ii)/Fe(iii) couple) increased in the order of Pt < Pt-Pd < Pt-Pdsds. A Pt-Pdsds electrode exhibited an open circuit potential (OCP) of 0.65 V in acidic conditions; however, when 50.0 mM NaAsO2 was present, the OCP value shifted to 0.42 V. It has been projected that the As(iii) oxidation proceeds using a sequential pathway: As(iii) → As(iv) → As(v). After optimization of the square wave voltammetric data, the limits of detection of As(iii) were obtained as 1.3 μg L-1 and 0.2 μg L-1 when the surface modification of the Pt surface was executed with Pd particles in the absence and presence of the SDS surfactant, respectively. Finally, real samples were analyzed with excellent recovery performance.

Particle-impact analysis of the degree of cluster formation of rutile nanoparticles in aqueous solution

Cluster formation can profoundly influence the bioavailability and (bio)geochemical activity of nanoparticles in natural aquatic systems. While colloidal properties of nanoparticles are commonly investigated using light-scattering techniques, the requirement to dilute samples can affect the fundamental nature and extent of the cluster size. Hence, an alternative in situ approach that can cover a much higher and wider concentration range of particles is desirable. In this study, particle impact chronoamperometry is employed to probe the degree of cluster formation of Alizarin Red S modified rutile nanoparticles of diameter ca. 167 nm in conditions approximating those existing in the environment. Random collisions of individual clusters of the modified rutile particles with a stationary electrode result in transient current signals during a chronoamperometric measurement, indicative of the reduction of the adsorbed Alizarin Red S dye molecules. The results from the particle-impact analysis reveal that the nanoparticles are heavily clustered with an average 91 monomeric particles per cluster. As the spherical equivalent size of the clusters (ca. 754 nm in diameter) is considerably larger than that from nanoparticle tracking analysis (ca. 117 nm), the present work highlights the impact of the dilution on the fundamental nature of the colloidal suspension and introduces the electrochemical determination of the size distribution of inert mineral nanoparticles in highly concentrated media.