Palladium-Doped Single-Walled Carbon Nanotubes as a New Adsorbent for Detecting and Trapping Volatile Organic Compounds: A First Principle Study

Design, synthesis, and density functional theory studies of a new selective chemosensor for Pb2

Herein, we have focused on a new colorimetric ligand synthesized from the reaction of 2-hydroxy-5-methylbenzene-1,3-dialdehyde with 2-amino-thiophenol, and investigated its activity as a sensor. In this regard, the sensory activity of the ligand towards different ions (Mn2+, Cu2+, Co2+, Fe2+, Fe3+, Zn2+, Ni2+, Cd2+, Ag+, Na+, Cs+, Mg2+, Al3+, Ba2+, K+, and Pb2+) was studied. The specificity of ion bindings is discussed through UV-Vis analysis. The ligand that was synthesized showed remarkable sensitivity, with a detection limit of 0.001 ppb. Additionally, the presence of Pb2+ ions can be visually detected through a color change from colorless to yellow. In the last part of this work, we seek to predict the available experimental measurements. Density functional theory (DFT) and quantum theory of atoms in molecules (QTAIM) are employed to examine the bonding between the ligand and the Pb2+ ion. The effect of water solvent was thoroughly examined for all the steps via the conductor-like Polarizable Continuum Model (CPCM). The theoretical findings revealed that electronic properties, including energy gap, adsorption energy, charge/energy transfer, and optical characteristics, undergo significant changes when Pb2+ cations are present. Hence, it can be inferred that the newly synthesized chemosensor (NC) is highly efficient in detecting Pb2+.

Nitrogen-phosphorus codoped carbon nanotube sponges for detecting volatile organic compounds: experimental and DFT calculations

The sensing of harmful gases and vapors is of fundamental interest to control the industrial emissions and environmental contamination. Nitrogen/phosphorus codoped carbon nanotube sponges (NP-CNTSs) were used to detect ethanol, acetone, cyclohexane, isopropanol, and methanol. The NP-CNTSs were produced through the aerosol-assisted chemical vapor deposition (AACVD) method using acetonitrile and triphenylphosphine as precursors at 1020 °C. The sensors based on NP-CNTSs were tested with varying operating temperatures (25-100 °C) and gas vapor concentrations (5-50 ppm). For instance, for a gas vapor concentration of 30 ppm and an operating temperature of 65 °C, the sensors showed changes in the electrical resistance of 1.12%, 1.21%, 1.09%, 2.4%, and 1.34% for ethanol, acetone, cyclohexane, isopropanol, and methanol, respectively. We found that the response and recovery times for isopropanol gas vapor are up to 43.7 s and 95 s, respectively. The current sensor outperformed the sensors reported in the literature by at least two times in the response measurement. Additionally, we performed van der Waals density functional theory calculations to elucidate the role of nitrogen and phosphorous codoped single-walled carbon nanotubes (SWCNTs) and their interaction with the considered gas molecule. We analyzed the molecular adsorption energy, optimized structures, and the density of states and calculated the electrostatic potential surface for N-doped, P-doped, NP-codoped, and OH-functionalized NP-codoped metallic SWCNTs-(6,6) and semiconducting SWCNTs-(10,0). Adsorption energy calculations revealed that in most cases the molecules are adsorbed to carbon nanotubes via physisorption. The codoping in SWCNTs-(6,6) promoted structural changes in the surface nanotube and marked chemisorption for acetone molecules.

Automated Recognition of Nanoparticles in Electron Microscopy Images of Nanoscale Palladium Catalysts

Automated computational analysis of nanoparticles is the key approach urgently required to achieve further progress in catalysis, the development of new nanoscale materials, and applications. Analysis of nanoscale objects on the surface relies heavily on scanning electron microscopy (SEM) as the experimental analytic method, allowing direct observation of nanoscale structures and morphology. One of the important examples of such objects is palladium on carbon catalysts, allowing access to various chemical reactions in laboratories and industry. SEM images of Pd/C catalysts show a large number of nanoparticles that are usually analyzed manually. Manual analysis of a statistically significant number of nanoparticles is a tedious and highly time-consuming task that is impossible to perform in a reasonable amount of time for practically needed large amounts of samples. This work provides a comprehensive comparison of various computer vision methods for the detection of metal nanoparticles. In addition, multiple new types of data representations were developed, and their applicability in practice was assessed.