A Highly Sensitive Surface-Modified Porous Carbon Nanotube-Based Sensor for Ammonia Gas Detection.

In this work, we compared the gas sensing behaviors of pristine and decorated multi-walled carbon nanotubes (MWCNTs) and examined the response behavior of bare and adorned MWCNTs in gas sensing. According to the data, the decorated response was 144%, which is higher than the bare CNT response of 117% in terms of the sensing response. The RF-sputtering method is used to decorate the carbon nanotubes by pure Indium (In) metal nanoparticles. Every measurement was performed in a temperature-controlled environment. Tests of the entire procedure were conducted at a 10 ppm concentration of ammonia gas. We have observed the quick reaction time (1-10 s) in pristine and (1-7 s) in decorated MWCNTs. The response was obtained 117% for the pristine and 144, 115, and 73% for the second (3 min decoration), third (6 min decoration), and fourth (9 min decoration) MWCNTs, respectively. The as-prepared pristine samples and all the decorated sensors had sensitivity values of 0.45, 0.50, 0.51, and 0.57 for time intervals of 0, 3, 6, and 9 min, respectively. It amounted to 45% for the pure and 50, 51, and 57% for the remaining as-prepared decorated sensors, respectively. Based on the measured sensor response graph, a recovery of between 80 and 85% was achieved. For a period of 10 days at a constant concentration, the stability was also assessed and we have analyzed the structural, electrical, and elemental composition of the prepared CNTs by FESEM, EDX, Raman spectroscopy, FTIR, and XRD.

A multitier virtual screening study of phytoconstituents as Myeloid Cell Leukemias 1 inhibitors.

Myeloid Cell Leukemia 1 (MCL1) is an anti-apoptotic protein that plays a critical role in regulating cell survival, particularly in cancer cells. It is a member of the BCL-2 family of proteins, which control the intrinsic pathway of apoptosis. MCL1 has emerged as a promising target for cancer therapy because it is overexpressed in a wide range of cancers, including breast, lung, prostate, and hematologic malignancies. Due to its remarkable role in cancer progression, it has been reflected as a promising drug target for cancer therapy. A few MCL1 inhibitors have been identified previously, but further research is needed to develop novel, effective and safe MCL1 inhibitors that can overcome resistance mechanisms and minimize toxicity in normal cells. In this study, we aim to search for compounds that target the critical binding site of MCL1 from phytoconstituent library from the IMPPAT database. To accomplish this, a multitier virtual screening approach involving molecular docking and molecular dynamics simulations (MDS) were used to evaluate their suitability for the receptor. Notably, certain screened phytoconstituents have appreciable docking scores and stable interactions toward the binding pocket of MCL1. The screened compounds underwent ADMET and bioactivity analysis to establish their anticancer properties. One phytoconstituent, Isopongaflavone, was identified that exhibiting higher docking and drug-likeness than the already reported MCL1 inhibitor, Tapotoclax. Isopongaflavone and and Tapotoclax, along with MCL1, were subjected to 100 nanoseconds (ns) MDS study to verify their stability inside the binding site of MCL1. The MDS findings demonstrated a strong binding affinity between Isopongaflavone and the MCL1 binding pocket, resulting in reduced conformational fluctuations. This investigation proposes Isopongaflavone as a promising candidate for the development of innovative anticancer therapeutics, pending the necessary validation procedures. Also, the findings provide valuable information for designing MCL1 inhibitors based on the protein's structure.Communicated by Ramaswamy H. Sarma.

SCS-Net: An efficient and practical approach towards Face Mask Detection.

Much work has been done in the computer vision domain for the problem of facial mask detection to curb the spread of the Coronavirus disease (COVID-19). Preventive measures developed using deep learning-based models have got enormous attention. With the state-of-the-art results touching perfect accuracies on various models and datasets, two very practical problems are still not addressed - the deployability of the model in the real world and the crucial cases of incorrectly worn masks. To this end, our method proposes a lightweight deep learning model with just 0.12M parameters having up to 496 times reduction as compared to some of the existing models. Our novel architecture of the deep learning model is designed for practical implications in the real world. We also augment an existing dataset with a large set of incorrectly masked face images leading to a more balanced three-class classification problem. A large collection of 25296 synthetically designed incorrect face mask images are provided. This is the first of its kind of data to be proposed with equal diversity and quantity. The proposed model achieves a competitive accuracy of 95.41% on two class classification and 95.54% on the extended three class classification with minimum number of parameters in comparison. The performance of the proposed system is assessed with various state-of-the-art literature and experimental results indicate that our solution is more realistic and rational than many existing works which use overly massive models unsuitable for practical deployability.

Enhancement of Electrochemical Stability Window and Electrical Properties of CNT-Based PVA-PEG Polymer Blend Composites.

New polymer blend composite electrolytes (PBCEs) were prepared by the solution casting technique using poly(vinyl alcohol) (PVA)-polyethylene glycol (PEG), sodium nitrate (NaNO3) as a doping salt and multiwalled carbon nanotubes (MWCNTs) as fillers. The X-ray diffraction pattern confirms the structural properties of the polymer blend composite films. FTIR investigations were carried out to understand the chemical properties and their band assignments. The ionic conductivity of the 10 wt % MWCNTs incorporated PVA-PEG polymer blend was measured as 4.32 × 10-6 S cm-1 at 20 °C and increased to 2.253 × 10-4 S/cm at 100 °C. The dependence of its conductivity on temperature suggests Arrhenius behavior. The equivalent circuit models that represent the R s(Q1(R1(Q2(R2(CR3))))) were used to interpret EIS data. The dielectric behavior of the samples was investigated by utilizing their AC conductance spectra, dielectric permittivity, dielectric constant (εi and εr), electric modulus (Mi and Mr), and loss tangent tan δ. The dielectric permittivity of the samples increases due to electrode polarization effects in low frequency region. The loss tangent's maxima shift with increasing temperature; hence, the peak height rises in the high frequency region. MWCNTs-based polymer blend composite electrolytes show an enhanced electrochemical stability window (4.0 V), better transference number (0.968), and improved ionic conductivity for use in energy storage device applications.

Investigations on Structural, Optical Properties, Electrical Properties and Electrochemical Stability Window of the Reduced Graphene Oxides Incorporated Blend Polymer Nanocomposite Films.

The incorporation of reduced Graphene oxides (rGO) as a nanofiller in the blend polymer nanocomposite (BPNC) based on Polyvinylpyrrolidone (PVP)-Polyvinylalcohol (PVA) and sodium bicarbonate (NaHCO3) are presented. The blend polymer electrolytes films are prepared by the standard solution cast technique, and it is characterized to investigate the structural, morphological, thermal, optical and electrochemical property. The X-ray diffraction confirms the formation of polymer nanocomposite and is agreed with FESEM analysis. The FTIR confirms the presence of various interactions between the polymer, salt and rGO, and indicates the composite formation. The DSC examines the thermal property of the blend polymer nanocomposite electrolytes system. The bandgap energy has been obtained from the UV-spectroscopy and examines the direct and indirect gap, both offer the decreases bandgap with the addition of a higher concentration of rGO as nanofillers. The highest value of ionic conductivity of the film is obtained ~1.39×10-6 S cm-1 at 15 wt.% of rGO content in polymer blend nanocomposite (BPNC) films. For these BPNC films, the electrochemical stability window (ESW) is ~4.0 V at 25 wt.% of rGO content and ionic transport number (tion) is ~0.98, for 10 wt.% of rGO content at the room temperature. These highly stable blend polymer nanocomposite electrolyte films offer the excellent properties for utilized as a separator for solid-state devices e.g., battery, supercapacitors, electrochromic display devices and other electrochemical energy storage/ conversion devices respectively.

Enhancement of Electron Emission Properties of Carbon Nanotubes by the Decoration with Low Work Function Metal Oxide Nanoparticles.

In the present report, the properties of the field emission devices of carbon nanotubes (CNTs) were remarkably improved by decorating their surface with magnesium oxide nanoparticles (MgO NPs). The MgO NPs were attached effectively on the surface of CNTs via thermal evaporation. The Raman spectra confirm the graphitic order of as-grown pristine CNTs with RBM (radial breathing mode), D band and G band peaks at the 282 cm-1, 1347 cm-1 and 1594 cm-1 respectively. The peak at 471 cm-1 indicates successful attachment of MgO NPs to the CNTs. The enhanced field emission properties of CNTs were mainly attributed to the MgO NPs which increased the field enhancement factor and the density of emission sites. The decreased work function and increased field enhancement factor were responsible for the improved FE properties of the CNTs. Our results indicate that the MgO decorated CNTs can be used as an effective field emitter for various electron emission devices. The turn-on field decrease from 1.6 V/µm to 1.3 V/µm and the maximum current density increases from 1.581 to 3.678 mA/cm2 after the decoration of CNTs with MgO NPs. The value of field enhancement factor (ß) also increases from 2.814×10³ to 9.823×10³.