Multifunctional fully biobased aerogels for water remediation: Applications for dye and heavy metal adsorption and oil/water separation.

Water ecosystem contamination from industrial pollutants is an emerging threat to both humans and native species, making it a point of global concern. In this work, fully biobased aerogels (FBAs) were developed by using low-cost cellulose filament (CF), chitosan (CS), citric acid (CA), and a simple and scalable approach, for water remediation applications. The FBAs displayed superior mechanical properties (up to ∼65 kPa m3 kg-1 specific Young's modulus and ∼111 kJ/m3 energy absorption) due to CA acting as a covalent crosslinker in addition to the natural hydrogen bonding and electrostatic interactions between CF and CS. The addition of CS and CA increased the variety of functional groups (carboxylic acid, hydroxyl and amines) on the materials' surface, resulting in super-high dye and heavy metal adsorption capacities (619 mg/g and 206 mg/g for methylene blue and copper, respectively). Further modification of FBAs with a simple approach using methyltrimethoxysilane endowed aerogel oleophilic and hydrophobic properties. The developed FBAs showed a fast performance in water and oil/organic solvents separation with more than 96% efficiency. Besides, the FBA sorbents could be regenerated and reused for multiple cycles without any significant impact on their performance. Moreover, thanks to the presence of amine groups by addition of CS, FBAs also displayed antibacterial properties by preventing the growth of Escherichia coli on their surface. This work demonstrates the preparation of FBAs from abundant, sustainable, and inexpensive natural resources for applications in wastewater purification.

Super-strong and high-performance electrical film heater derived from silver nanowire/aligned bacterial cellulose film.

High-performance electrical Joule heaters with high mechanical properties, low driving voltage, rapid response, and flexibility are highly desirable for portable thermal management. Herein, by using aligned bacterial cellulose (BC) and silver nanowire (AgNW), we fabricated a novel film heater based on Joule heating phenomena. The aligned BC film prepared by stretching BC hydrogel and hot-pressing drying technology showed outstanding mechanical properties and flexibility. The ultrahigh strength of up to 1018 MPa and the toughness of 20 MJ/m3 were obtained for the aligned BC film with 40% wet-stretching (BC-40). In addition, the aligned BC film could be folded into desirable shapes. The AgNW was spray-coated on the surface of aligned BC-40 film and then covered with polydimethylsiloxane to form a P@AgNW@BC heater. P@AgNW@BC heater showed excellent conductivity, which endowed the film heater with an outstanding Joule heating performance. P@AgNW@BC heater could reach ~ 98 â„ƒ at a very low driving voltage of 4 V with a rapid heating response (13 s) and long-term temperature stability. The P@AgNW@BC heater with such an outstanding heating performance can be used as a flexible heating device for different applications in daily life like deicing/defogging device, wearable thermotherapy, etc.Affiliations: Please check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary.yes, we confirmed the affiliations are correct. Article title: Kindly check and confirm the edit made in the article title.Thanks, the title is no problem.

Comparative Analysis of Machine Learning Models for Nanofluids Viscosity Assessment.

The process of selecting a nanofluid for a particular application requires determining the thermophysical properties of nanofluid, such as viscosity. However, the experimental measurement of nanofluid viscosity is expensive. Several closed-form formulas for calculating the viscosity have been proposed by scientists based on theoretical and empirical methods, but these methods produce inaccurate results. Recently, a machine learning model based on the combination of seven baselines, which is called the committee machine intelligent system (CMIS), was proposed to predict the viscosity of nanofluids. CMIS was applied on 3144 experimental data of relative viscosity of 42 different nanofluid systems based on five features (temperature, the viscosity of the base fluid, nanoparticle volume fraction, size, and density) and returned an average absolute relative error (AARE) of 4.036% on the test. In this work, eight models (on the same dataset as the one used in CMIS), including two multilayer perceptron (MLP), each with Nesterov accelerated adaptive moment (Nadam) optimizer; two MLP, each with three hidden layers and Adamax optimizer; a support vector regression (SVR) with radial basis function (RBF) kernel; a decision tree (DT); tree-based ensemble models, including random forest (RF) and extra tree (ET), were proposed. The performance of these models at different ranges of input variables was assessed and compared with the ones presented in the literature. Based on our result, all the eight suggested models outperformed the baselines used in the literature, and five of our presented models outperformed the CMIS, where two of them returned an AARE less than 3% on the test data. Besides, the physical validity of models was studied by examining the physically expected trends of nanofluid viscosity due to changing volume fraction.