Optimization of Heavy Metals Biosorption via Artificial Neural Network: A Case Study of Cobalt (II) Sorption by Pseudomonas alcaliphila NEWG-2

Abstract

The definitive screening design (DSD) and artificial neural network (ANN) were conducted for modeling the biosorption of Co(II) by Pseudomonas alcaliphila NEWG-2. Factors such as peptone, incubation time, pH, glycerol, glucose, K2HPO4, and initial cobalt had a significant effect on the biosorption process. MgSO4 was the only insignificant factor. The DSD model was invalid and could not forecast the prediction of Co(II) removal, owing to the significant lack-of-fit (P < 0.0001). Decisively, the prediction ability of ANN was accurate with a prominent response for training (R2 = 0.9779) and validation (R2 = 0.9773) and lower errors. Applying the optimal levels of the tested variables obtained by the ANN model led to 96.32 ± 2.1% of cobalt bioremoval. During the biosorption process, Fourier transform infrared spectroscopy (FTIR), energy-dispersive X-ray spectroscopy, and scanning electron microscopy confirmed the sorption of Co(II) ions by P. alcaliphila. FTIR indicated the appearance of a new stretching vibration band formed with Co(II) ions at wavenumbers of 562, 530, and 531 cm–1. The symmetric amino (NH2) binding was also formed due to Co(II) sorption. Interestingly, throughout the revision of publications so far, no attempt has been conducted to optimize the biosorption of Co(II) by P. alcaliphila via DSD or ANN paradigm.

Clinical characteristics and risk factors associated with ICU-acquired infections in sepsis: A retrospective cohort study

“…Almost 40% of patients undergoing craniotomy develop infection at least once, such as pneumonia independent of mechanical ventilation (15%), ventilator-associated pneumonia (VAP; 23%), or urinary tract infection (UTI; 9%), and postoperative surgical site infections (SSIs) occur in 9% ( Kourbeti et al., 2015 ). Moreover, invasive procedures, including central venous catheter, urinary catheter, mechanical ventilation, and tracheostomy, are potential risk factors during the first 48 h in the ICU, predicting an ICU-acquired infection in our study due to the fact that these tubes provide an ideal opportunity for bacterial adhesion and biofilm formation ( Sottile et al., 1986 ; Elsayed et al., 2022 ). The rapid colonization of microbiota contributes to the infection.…”

Turmeric extract-mediated biogenic synthesis of Ag@SeO2 magnetic nanoparticles: characterization, optimization, antibacterial and antioxidant activities

“…67 At this threshold, a signicant coefficient indicates a less than 5% chance of wrongly concluding a link between a variable and the response when none exists. 66,68 Unfortunately, our model exhibits a signicant lack-of-t (p-value < 0.001), hinting at unaccounted-for factors or potential experimental errors. This highlights the need for further optimization and exploration of additional variables to rene the model's accuracy and predictive power.…”

“…Only when accompanied by a similarly high adjusted-R 2 and an insignicant lack-of-t can truly infer a robust regression model. 66 Regarding the tested variables, two (X 3 , and X 4 ) of the four investigated factors exhibit statistically signicant individual effects on Ag@SeO 2 bmNP production. Notably, turmeric concentration has the most signicant impact, with the highest F value and a low p-value of less than 0.001, highlighting its crucial role in the biosynthesis process.…”

Detection and removal of emerging contaminants from water bodies: A statistical approach

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