High-entropy effect with hollow (ZnCdFeMnCu)xS nanocubes for photoelectrochemical immunoassay

Dendritic quinary PtRhMoCoFe high-entropy alloy as a robust immunosensing nanoplatform for ultrasensitive detection of biomarker

Recently, high-entropy alloys have superior physicochemical properties as compared to conventional alloys for their glamorous "cocktail effect". Nevertheless, they are scarcely applied to electrochemical immunoassays until now. Herein, uniform PtRhMoCoFe high-entropy alloyed nanodendrites (HEANDs) were synthesized by a wet-chemical co-reduction method, where glucose and oleylamine behaved as the co-reducing agents. Then, a series of characterizations were conducted to illustrate the synergistic effect among multiple metals and fascinating structural characteristics of PtRhMoCoFe HEANDs. The obtained high-entropy alloy was adopted to build a electrochemical label-free biosensor for ultrasensitive bioassay of biomarker cTnI. In the optimized analytical system, the resultant sensor exhibited a dynamic linear range of 0.0001-200 ng mL-1 and a low detection limit of 0.0095 pg mL-1 (S/N = 3). Eventually, this sensing platform was further explored in serum samples with satisfied recovery (102.0 %). This research renders some constructive insights for synthesis of high-entropy alloys and their expanded applications in bioassays and bio-devices.

Integrating machine learning models with cross-validation and bootstrapping for evaluating groundwater quality in Kanchanaburi province, Thailand

Exploring the potential of new models for mapping groundwater quality presents a major challenge in water resource management, particularly in Kanchanaburi Province, Thailand, where groundwater faces contamination risks. This study aimed to explore the applicability of random forest (RF) and artificial neural networks (ANN) models to predict groundwater quality. Particularly, these two models were integrated into cross-validation (CV) and bootstrapping (B) techniques to build predictive models, including RF-CV, RF-B, ANN-CV, and ANN-B. Entropy groundwater quality index (EWQI) was converted to normalized EWQI which was then classified into five levels from very poor to very good. A total of twelve physicochemical parameters from 180 groundwater wells, including potassium, sodium, calcium, magnesium, chloride, sulfate, bicarbonate, nitrate, pH, electrical conductivity, total dissolved solids, and total hardness, were investigated to decipher groundwater quality in the eastern part of Kanchanaburi Province, Thailand. Our results indicated that groundwater quality in the study area was primarily polluted by calcium, magnesium, and bicarbonate and that the RF-CV model (RMSE = 0.06, R2 = 0.87, MAE = 0.04) outperformed the RF-B (RMSE = 0.07, R2 = 0.80, MAE = 0.04), ANN-CV (RMSE = 0.09, R2 = 0.70, MAE = 0.06), and ANN-B (RMSE = 0.10, R2 = 0.67, MAE = 0.06). Our findings highlight the superiority of the RF models over the ANN models based on the CV and B techniques. In addition, the role of groundwater parameters to the normalized EWQI in various machine learning models was found. The groundwater quality map created by the RF-CV model can be applied to orient groundwater use.

Self-assembled p-n Ag2O@Bi2O2S nanoflower heterojunctions for sensitive photoelectrochemical immunoassay

A new photoelectrochemical immunoassay based on self-assembled p-n Ag2O@Bi2O2S nanoflower heterojunction was designed and developed for quantitative monitoring of prostate-specific antigen (PSA) in biological fluids. Primarily, self-assembled p-n Ag2O@Bi2O2S nanoflower heterojunctions were served as the photoactive materials and coated onto the surface of electrodes. Subsequently, the glucose oxidase (GOx) was bound to the detection antibody (mAb2) labeled gold nanoparticles (Au NPs) and then were employed to accomplish a sandwich-like immunoreaction to generate H2O2 on a microplate incubated with monoclonal anti-PSA antibodies. In the presence of PSA, the product (H2O2) was catalyzed by the substrate, which was used as an electron sacrificial agent to improve signal conversion and capture of photogenerated electrons. Under optimum conditions, a wide linear range of 0.01-50 ng mL-1 and a low detection limit of 5.3 pg mL-1 were accomplished with the sensor, exhibiting an excellent photocurrent response. Moreover, the proposed sensor revealed satisfactory reproducibility, high selectivity, and acceptable accuracy for the real sample testing. Importantly, our work provides a novel strategy for high sensitivity detection of disease-associated biomarkers for the early diagnosis of cancers.