Surface modified Acacia Senegal Gum based spherical hydrogel; fabrication, characterization, and kinetically optimized waste water treatment with remarkable adsorption efficiency.

Acacia Senegal Gum hydrogel (HASG) with swollen dimension less than 50 µm were fabricated, and chemically modified with versatile diethylenetriamine (d-amine) to tailor the surface properties for environmental remediation. Negatively charged metal ions, for example, chromate (Cr(III)), dichromate (Cr(VI)), and arsenate (As(V)) were removed from aqueous media by using modified hydrogels (m-HASG). The FT-IR spectra revealed some new peaks due to d-amine treatment. The zeta potential measurements confirm a positively charged surface of HASG upon d-amine modification at ambient conditions. The absorption studies revealed that 0.05 g feed of m-(HASG) possesses 69.8, 99.3, and 40.00% cleaning potential against As(V), Cr(VI), and Cr(III), respectively with 2 h contact time in deionized water. Almost comparable adsorption efficiency was achieved by the prepared hydrogels towards the targeted analytes dissolved in real water samples. Adsorption isotherms, for example, Langmuir, Freundlich and modified Freundlich isotherms were applied to the collected data. Briefly, Modified Freundlich isotherm manifested comparatively agreeable line for the all adsorbents pollutants with highest R2 figure. In addition, maximum adsorption capacity (Qm) with 217, 256, and 271 mg g-1 numerical values were obtained against As(V), Cr(VI), and Cr(III), respectively. In real water samples, 217, 256, and 271 mg g-1 adsorption capacity was represented by m-(HASG). In brief, m-(HASG) is a brilliant material for environmental application as cleaner candidate towards toxic metal ions.

Synergistic effect of gold nanoparticles anchored on conductive carbon black as an efficient electrochemical sensor for sensitive detection of anti-COVID-19 drug Favipiravir in absence and presence of co-administered drug Paracetamol.

Favipiravir (FVP) is introduced as a promising newly developed antiviral drug against the coronavirus disease 2019 (COVID-19). Therefore, the accurate determination of FVP is of great significance for quality assessment and clinical diagnosis. Herein, a novel electrochemical sensing platform for FVP based on gold nanoparticles anchored conductive carbon black (Au@CCB) modified graphite nanopowder flakes paste electrode (GNFPE) was constructed. Morphological and nanostructure properties of Au@CCB have been investigated by TEM, HRTEM, and EDX methods. The morphology and electrochemical properties of Au@CCB/GNFPE were characterized by SEM, cyclic voltammetry (CV), and EIS. The Au@CCB nanostructured modified GNFPE exhibited strong electro-catalytic ability towards the oxidation of FVP. The performance of the fabricated Au@CCB/GNFPE was examined by monitoring FVP concentrations in the absence and presence of co-administered drug paracetamol (PCT) by AdS-SWV. It was demonstrated that the proposed sensor exhibited superior sensitivity, stability, and anti-interference capability for the detection of FVP. The simultaneous determination of a binary mixture containing FVP and the co-administered drug PCT using Au@CCB/GNFPE sensor is reported for the first time. Under optimized conditions, the developed sensor exhibited sensitive voltammetric responses to FVP and PCT with low detection limits of 7.5 nM and 4.3 nM, respectively. The sensing electrode was successfully used to determine FVP and PCT simultaneously in spiked human plasma and pharmaceutical preparations, and the findings were satisfactory. Finally, the fabricated sensor exhibited high sensitivity for simultaneous detection of FVP and PCT in the presence of ascorbic acid in a real sample.

Tailoring confined CdS quantum dots in polysulfone membrane for efficiently durable performance in solar-driven wastewater remediating systems.

In this work, CdS quantum dots (QDs) were successfully confined in polysulfone membrane (PSM) to develop a photoactive membrane under solar illumination that was suited in wastewater remediating system. The CdS@PSM membranes were prepared using the nonsolvent induced phase separation (NIPS) approach. Optical measurements show the confinement of CdS quantum dots (QDs) in the PS matrix within the narrowest band gap (2.41 eV) at 5 wt% loading. PS has two strong emission peaks at 411 and 432 nm due to photoelectron-hole recombination on pure PSM's surface. Adding 1 wt% CdS QDs to PSM reduced the earlier peak and blue-shifted the latter, within the appearance of three emission peaks attributed to the near band-edge emission of confined CdS QDs. Overloading CdS reduced all emission peaks. Moreover, fluorimetric monitoring of •OH radicals indicates that PSM produces the least amount of photogenerated •OH radicals while CdS@PSM(5 wt%) achieved the highest productivity. Examining the developed membranes in detoxifying methylene blue (MB) from aqueous solution of natural pH 8.1 showed weak adsorption in dark over 90 min of contact while switching to solar illumination significantly photodegrade MB where the degradation efficiency starts from 49% for pure PSM to 79% for CdS@PSM(5 wt%). Influence of pH was found crucial on photodegradation efficacy. Acidic pH 3 showed the weakest photodegradation efficacy, while the alkaline pH 12 was 18.88 times more effective. The used CdS@PSM (5 wt%) was successfully photo-renovated by soaking in 10 mL of NaOH solution under Solar illumination for 15 min to be used in 4 consecutive photodegradation cycles with insignificant decrease in efficacy. These findings are promising and could lead to a high-efficiency, sustainable photocatalytic suite.

Bifunctional ratiometric sensor based on highly fluorescent nitrogen and sulfur biomass-derived carbon nanodots fabricated from manufactured dairy product as a precursor.

Novel biomass-derived carbon dots co-doped with nitrogen and sulfur were fabricated through facile and simple synthetic method from manufactured milk powder and methionine as precursors. The as-fabricated platform was used for ratiometric fluorescence sensing of Cu (II) and bisphosphonate drug risedronate sodium. The sensing platform is based on oxidation of o-phenylenediamine by Cu (II) to form 2, 3-diaminophenazine (oxidized product) with an emission peak at 557 nm. The resultant product quenched the fluorescence emission of as-fabricated carbon dots at 470 nm through Förster resonance energy transfer (FRET) and inner-filter effect (IFE). Upon addition of risedronate sodium, the formation of 2, 3-diaminophenazine was decreased as a result of Cu (II) chelation with risedronate sodium, recovering the fluorescence emission of carbon dots. The ratio of fluorescence at 470 nm and 557 nm was measured as a function of Cu (II) and risedronate sodium concentrations. The proposed sensing platform sensitively detected Cu (II) and risedronate sodium in the range of 0.01-55 µM and 5.02-883 µM with LODs (S/N = 3) of 0.003 µM and 1.48 µM, respectively. The sensing platform exhibited a good selectivity towards Cu (II) and risedronate sodium. The sensing system was used to determine Cu (II) and risedronate sodium in different sample matrices with recoveries % in the range of 99-103 % and 97.4-103.8 %, and RSDs % in the range of 1.5-3.0 % and 1.8-3.6 %, respectively.

Analyzing the Patient Behavior for Improving the Medical Treatment Using Smart Healthcare and IoT-Based Deep Belief Network.

Patient behavioral analysis is a critical component in treating patients with a variety of issues, with head trauma, neurological disease, and mental illness. The analysis of the patient's behavior aids in establishing the disease's core cause. Patient behavioral analysis has a number of contests that are much more problematic in traditional healthcare. With the advancement of smart healthcare, patient behavior may be simply analyzed. A new generation of information technologies, particularly the Internet of Things (IoT), is being utilized to transform the traditional healthcare system in a variety of ways. The Internet of Things (IoT) in healthcare is a crucial role in offering improved medical facilities to people as well as assisting doctors and hospitals. The proposed system comprises of a variety of medical equipment, such as mobile-based apps and sensors, which is useful in collecting and monitoring the medical information and health data of patient and interact to the doctor via network connected devices. This research may provide key information on the impact of smart healthcare and the Internet of Things in patient beavior and treatment. Patient data are exchanged via the Internet, where it is viewed and analyzed using machine learning algorithms. The deep belief neural network evaluates the patient's particulars from health data in order to determine the patient's exact health state. The developed system proved the average error rate of about 0.04 and ensured accuracy about 99% in analyzing the patient behavior.