Rational Design of a Necklace-like ZIF-67/Poly(vinylidene fluoride) Electrospun Nanofiber Hybrid Membrane for Simultaneous Removal of PM0.3 and SO2.

Growing concerns over poor air quality, especially in urban and industrial regions, have led to increased global demands for advanced air-purification technologies. However, the stability and airborne pollutant control abilities of the available air-purification materials under diverse environmental conditions are limited. Thus, the advanced development of filtration materials that can effectively control different types of pollutants, such as particulate matter (PM) and gaseous pollutants, simultaneously has attracted attention. The zeolitic imidazolate framework (ZIF), a type of porous metal-organic framework (MOF), is a promising material for capturing weakly acidic toxic gases such as SO2 owing to its excellent adsorption performance and high thermal and chemical stability. In this study, we successfully developed an ultrastable necklace-like multifunctional hybrid membrane via the cetyltrimethylammonium bromide-assisted in situ growth of zeolitic imidazolate framework (ZIF)-67 crystals on electrospun Co2+-doped poly(vinylidene fluoride) nanofibers (70 nm) that can be used in different moisture environments to achieve sustainable air-filtration performance. The hybrid nanocomposite membrane demonstrated excellent performance for the simultaneous control of intractable fine PM0.3 (filtration efficiency, 99.461%) and SO2 (adsorption capacity, 1476.5 mg g-1) under different humidity conditions. This study contributes to the optimal synergistic integration of the advanced metal-organic framework (MOF)-nanofiber nanocomposite membranes and can guide the rational design and conceptualization of a facile and novel membrane for various applications in the environmental science and energy fields.

Equilibrium Optimization Algorithm with Deep Learning Enabled Prostate Cancer Detection on MRI Images.

The enlargement of the prostate gland in the reproductive system of males is considered a form of prostate cancer (PrC). The survival rate is considerably improved with earlier diagnosis of cancer; thus, timely intervention should be administered. In this study, a new automatic approach combining several deep learning (DL) techniques was introduced to detect PrC from MRI and ultrasound (US) images. Furthermore, the presented method describes why a certain decision was made given the input MRI or US images. Many pretrained custom-developed layers were added to the pretrained model and employed in the dataset. The study presents an Equilibrium Optimization Algorithm with Deep Learning-based Prostate Cancer Detection and Classification (EOADL-PCDC) technique on MRIs. The main goal of the EOADL-PCDC method lies in the detection and classification of PrC. To achieve this, the EOADL-PCDC technique applies image preprocessing to improve the image quality. In addition, the EOADL-PCDC technique follows the CapsNet (capsule network) model for the feature extraction model. The EOA is based on hyperparameter tuning used to increase the efficiency of CapsNet. The EOADL-PCDC algorithm makes use of the stacked bidirectional long short-term memory (SBiLSTM) model for prostate cancer classification. A comprehensive set of simulations of the EOADL-PCDC algorithm was tested on the benchmark MRI dataset. The experimental outcome revealed the superior performance of the EOADL-PCDC approach over existing methods in terms of different metrics.

Bioinspired Garra Rufa Optimization-Assisted Deep Learning Model for Object Classification on Pedestrian Walkways.

Object detection in pedestrian walkways is a crucial area of research that is widely used to improve the safety of pedestrians. It is not only challenging but also a tedious process to manually examine the labeling of abnormal actions, owing to its broad applications in video surveillance systems and the larger number of videos captured. Thus, an automatic surveillance system that identifies the anomalies has become indispensable for computer vision (CV) researcher workers. The recent advancements in deep learning (DL) algorithms have attracted wide attention for CV processes such as object detection and object classification based on supervised learning that requires labels. The current research study designs the bioinspired Garra rufa optimization-assisted deep learning model for object classification (BGRODL-OC) technique on pedestrian walkways. The objective of the BGRODL-OC technique is to recognize the presence of pedestrians and objects in the surveillance video. To achieve this goal, the BGRODL-OC technique primarily applies the GhostNet feature extractors to produce a set of feature vectors. In addition to this, the BGRODL-OC technique makes use of the GRO algorithm for hyperparameter tuning process. Finally, the object classification is performed via the attention-based long short-term memory (ALSTM) network. A wide range of experimental analysis was conducted to validate the superior performance of the BGRODL-OC technique. The experimental values established the superior performance of the BGRODL-OC algorithm over other existing approaches.

Fabrication and Investigation of Acid Functionalized CNT Blended Nanocomposite Hollow Fiber Membrane for High Filtration and Antifouling Performance in Ultrafiltration Process.

In this study, we fabricated a nanocomposite polyethersulfone (PES) HF membrane by blending acid functionalized carbon nanotubes (FCNT) to address the issue of reduced membrane life, increased energy consumption, and operating costs due to low permeability and membrane fouling in the ultrafiltration process. Additionally, we investigated the effect of FCNT blending on the membrane in terms of the physicochemical properties of the membrane and the filtration and antifouling performance. The FCNT/PES nanocomposite HF membrane exhibited increased water permeance from 110.1 to 194.3 LMH/bar without sacrificing rejection performance and increased the flux recovery ratio from 89.0 to 95.4%, compared to a pristine PES HF membrane. This study successfully developed a high filtration and antifouling polymer-based HF membrane by blending FCNT. Furthermore, it was validated that blending FCNT into the membrane enhances the filtration and antifouling performance in the ultrafiltration process.

A Hash-Based Quantum-Resistant Chameleon Signature Scheme.

As a standard digital signature may be verified by anybody, it is unsuitable for personal or economically sensitive applications. The chameleon signature system was presented by Krawczyk and Rabin as a solution to this problem. It is based on a hash then sign model. The chameleon hash function enables the trapdoor information holder to compute a message digest collision. The holder of a chameleon signature is the recipient of a chameleon signature. He could compute collision on the hash value using the trapdoor information. This keeps the recipient from disclosing his conviction to a third party and ensures the privacy of the signature. The majority of the extant chameleon signature methods are built on the computationally infeasible number theory problems, like integer factorization and discrete log. Unfortunately, the construction of quantum computers would be rendered insecure to those schemes. This creates a solid requirement for construct chameleon signatures for the quantum world. Hence, this paper proposes a novel quantum secure chameleon signature scheme based on hash functions. As a hash-based cryptosystem is an essential candidate of a post-quantum cryptosystem, the proposed hash-based chameleon signature scheme would be a promising alternative to the number of theoretic-based methods. Furthermore, the proposed method is key exposure-free and satisfies the security requirements such as semantic security, non-transferability, and unforgeability.

Electrically Polarized Graphene-Blended Spacers for Organic Fouling Reduction in Forward Osmosis.

In membrane processes, a spacer is known to play a key role in the mitigation of membrane fouling. In this study, the effect of electric polarization on a graphene-blended polymer spacer (e.g., poly(lactic acid), PLA) for organic fouling on membrane surfaces was investigated. A pristine PLA spacer (P-S), a graphene-blended spacer (G-S), and an electrically polarized graphene-blended spacer (EG-S) were successfully fabricated by 3D printing. Organic fouling tests were conducted by the 5-h filtration of CaCl2 and a sodium alginate solution through commercially available membranes, which were placed together with the fabricated spacers. Membranes utilizing P-S, G-S, and EG-S were characterized in terms of the fouling amount on the membrane surface and fouling roughness. Electrostatic forces of EG-S provided 70% less and 90% smoother fouling on the membrane surface, leading to an only 14% less water flux reduction after 5 h of fouling. The importance of nanomaterial blending and polarization was successfully demonstrated herein.

Boron Nitride Nanotube (BNNT) Membranes for Energy and Environmental Applications.

Owing to their extraordinary thermal, mechanical, optical, and electrical properties, boron nitride nanotubes (BNNTs) have been attracting considerable attention in various scientific fields, making it more promising as a nanomaterial compared to other nanotubes. Recent studies reported that BNNTs exhibit better properties than carbon nanotubes, which have been extensively investigated for most environment-energy applications. Irrespective of its chirality, BNNT is a constant wide-bandgap insulator, exhibiting thermal oxidation resistance, piezoelectric properties, high hydrogen adsorption, ultraviolet luminescence, cytocompatibility, and stability. These unique properties of BNNT render it an exceptional material for separation applications, e.g., membranes. Recent studies reported that water filtration, gas separation, sensing, and battery separator membranes can considerably benefit from these properties. That is, flux, rejection, anti-fouling, sensing, structural, thermal, electrical, and optical properties of membranes can be enhanced by the contribution of BNNTs. Thus far, a majority of studies have focused on molecular simulation. Hence, the requirement of an extensive review has emerged. In this perspective article, advanced properties of BNNTs are analyzed, followed by a discussion on the advantages of these properties for membrane science with an overview of the current literature. We hope to provide insights into BNNT materials and accelerate research for environment-energy applications.

Omni-Directional Protected Nanofiber Membranes by Surface Segregation of PDMS-Terminated Triblock Copolymer for High-Efficiency Oil/Water Emulsion Separation.

An excellent antifouling membrane with high permeate flux is required for oil/water emulsion separation due to ever-increasing oily industrial wastewater. Thus, an intriguing integration of the Omni-directional protected porous membrane that combines a high porosity nanofiber membrane with a surface segregation mechanism is established for the first time. By applying polydimethylsiloxane(PDMS)-terminated triblock copolymer, the enrichment of the hydrophilic poly(ethylene oxide) (PEO) segment and the nonpolar PDMS segment on the surface of the nanofiber endowed the nanofiber membrane with underwater oleophobicity and low oil adhesion force, exhibiting oil resistance as well as oil release property. An ultrahigh permeate flux of ∼7115 L m-2 h-1 with a separation efficiency of ∼97.88% is achieved under the driving force of gravity (∼0.9 kPa), which is the highest permeate flux ever reported under similar conditions. Moreover, the surface segregation nanofiber membrane shows excellent reusability and ultrahigh permeate flux with the assistance of stirring in a long-term test, revealing the promising performances for the further particular application of oily wastewater.

Anti-biofouling effect of a thin film nanocomposite membrane with a functionalized-carbon-nanotube-blended polymeric support for the pressure-retarded osmosis process.

In this study, the anti-biofouling effect of a thin film nanocomposite (TFN) membrane with a functionalized-carbon-nanotube-blended polymeric support layer was analyzed to determine the applicability of this membrane for the pressure-retarded osmosis (PRO) process. The anti-biofouling property of TFN membranes for the PRO process was characterized by SEM, FTIR, and AFM, as well as contact angle measurements and zeta potential analysis of the bottom side of the support layer. The anti-biofouling effect of the fabricated membrane for the PRO process was analyzed by bacterial attachment tests on the bottom surface of the support layer and biofouling tests in a cross-flow operation system in the PRO mode (AL-DS). The TFN membrane with 0.5 wt% fCNTs exhibited enhanced anti-biofouling properties of the bottom surface of the support layer compared to the bare TFC membrane due to the low roughness, high negative surface charge, and hydrophilicity. Compared to the bare TFC membrane, the support layer of the fCNT0.5-TFN membrane exhibited a 35% decrease in bacterial attachment. In a laboratory-scale biofouling test, the water flux of the fCNT0.5-TFN membrane was ∼10% less than that of the bare TFC membrane in the PRO mode.

Investigation of the performance behavior of a forward osmosis membrane system using various feed spacer materials fabricated by 3D printing technique.

Recently, feed spacer research for improving the performance of a membrane module has adopted three-dimensional (3D) printing technology. This study aims to improve the performance of membrane feed spacers by using various materials and incorporating 3D printing. The samples were fabricated after modeling with 3D computer-aided design (CAD) software to investigate the mechanical strength, water flux, reverse solute flux, and fouling performances. This research was performed using acrylonitrile butadiene styrene (ABS), polypropylene (PP), and natural polylactic acid (PLA) as printing material, and the spacer model was produced using a diamond-shaped feed spacer, with a commercially available product as a reference. The 3D printed samples were initially compared in terms of size and precision with the 3D CAD model, and deviations were observed between the products and the CAD model. Then, the spacers were tested in terms of mechanical strength, water flux, reverse solute flux, and fouling (alginate-based waste water was used as a model foulant). Although there was not much difference among the samples regarding the water flux, better performances than the commercial product were obtained for reverse solute flux and fouling resistance. When comparing the prominent performance of natural PLA with the commercial product, PLA was found to have approximately 10% less fouling (based on foulant volume per unit area and root mean square roughness values), although it showed similar water flux. Thus, another approach has been introduced for using bio-degradable materials for membrane spacers.