Machine learning and DFT investigation of CO, CO2 and CH4 adsorption on pristine and defective two-dimensional magnesene

Excellent Gas-Sensitive Capabilities of Co3(HXTP)2 for Detecting Combustion Process Gases: A Theoretical Study

Combustion processes produce noxious gases, causing environmental pollution and health risks, which require high-performance sensing materials. Herein, metal-organic framework structures (MOFs) Co3(HXTP)2 (X = H, I, T) with superior conductivity and sensing properties are employed based on density functional theory (DFT) calculations. Adsorption energies of Co3(HXTP)2@gas are exceptionally outstanding in the realm of gas-sensitive material. Co3(HXTP)2 bases exhibit a responsive behavior toward the gas by comparing the work function and have a short recovery time (τ). Our results demonstrate that Co3(HHTP)2 (τ = 1.226 s) and Co3(HITP)2 (τ = 19.441 s) can serve as gas-sensitive materials for detecting O2 at 298 K, whereas Co3(HTTP)2 (τ = 694.226 s) can be used for CO at 498 K. Moreover, excellent gas-sensitive properties arise from chemical interactions, such as the electron "donation-backdonation" mechanism between gas and substrate (σ → 3d z 2 and 3d xz , 3d yz → π*), and the simultaneous refilling of the d-suborbitals (3d z 2 → 3d xz , 3d yz ) within Co atoms. The descriptor φ demonstrates excellent predictive capability for both the adsorption and response of gas-sensitive materials. Our findings provide valuable insights into the design of gas-sensitive materials in this class of TM3(HXTP)2 structures.

DFT Study on the Janus ZrSSe Monolayer for Its Potential Application in NO Gas Sensing

The growth of industry has resulted in increased global air pollution, necessitating the urgent development of highly sensitive gas detectors. In this work, the adsorption of the Janus ZrSSe monolayer for CO, CO2, NH3, NO, NO2, and O2 was studied by first-principles calculations. First, the stability of the ZrSSe monolayer is confirmed through calculations of cohesive energy and AIMD simulations. Furthermore, the calculations indicate that the Se layer exhibits higher selectivity and sensitivity toward gas molecules compared to the S layer. Specifically, among the gases adsorbed on the Se layer, NO has the shortest adsorption distance (1.804 Å), the lowest adsorption energy (-0.424 eV), and the greatest electron transfer (0.098 e). Additionally, density of states analysis reveals that adsorption of NO, NO2, and O2 on the Janus ZrSSe monolayer can induce a transition from a nonmagnetic to a magnetic state. The adsorption of NO not only alters the magnetic state but also induces a transition from a semiconductor to metal, which is highly advantageous for gas sensing applications. There results suggest that the Janus ZrSSe monolayer has the potential to serve as a highly sensitive detector for NO gas.

Cobalt group transition metals (TM: Co, Rh, Ir) coordination of S-doped porphyrins (TM_S@PPR) as sensors for molecular SO2 gas adsorption: a DFT and QTAIM study

CONTEXT

The intricate challenges posed by SO2 gas underscore the imperative for meticulous monitoring and detection due to its adverse effects on health, the environment, and equipment integrity. Hence, this research endeavors to delve deeply into the intricate realm of transition-metals functionalized sulfur-doped porphyrins (S@PPR) surfaces through a comprehensive computational study. The electronic properties revealed that upon adsorption, Ir_S@PPR surface reflects the least energy gap of 0.109 eV at the O-site of adsorptions, indicating an increase in electrical conductivity which is a better adsorption trait. Owing to the negative adsorption energy observed, the adsorption behavior is described as chemisorption, with the greatest adsorption energy of - 10.306 eV for Ir_S@PPR surface at the S-site of adsorption. Based on the mechanistic attributes, iridium-functionalized S@PPR surface is a promising detecting material towards the sensing of SO2 gas. This report will provide useful insight for experimental researchers in selecting and engineering materials to be used as detectors for SO2 gas pollutant.

METHOD

All theoretical investigations were carried out using density functional theory (DFT), calculated at PW6B95-D3/GenECP/Def2svp/LanL2DZ computational method.

Efficient exploration of transition-metal decorated MXene for carbon monoxide sensing using integrated active learning and density functional theory

The urgent demand for chemical safety necessitates the real-time detection of carbon monoxide (CO), a highly toxic gas. MXene, a 2D material, has shown potential for gas sensing applications (e.g., NH3, NO, SO2, CO2) due to its high surface accessibility, electrical conductivity, stability, and flexibility in surface functionalization. However, the pristine MXene generally exhibits poor interaction with CO; still, transition metal decoration can strengthen the interaction between CO and MXene. This study presents a high-throughput screening of 450 combinations of transition-metal (TM) decorated MXene (TM@MXene) for CO sensing applications using an integrated active learning (AL) and density functional theory (DFT) screening pipeline. Our AL pipeline, adopting a crystal graph convolutional neural network (CGCNN) as a surrogate model, successfully accelerates the screening of CO sensor candidates with minimal computational resources. This study identifies Sc@Zr3C2O2 and Y@Zr3C2O2 as the optimal TM@MXene candidates with promising CO sensing performance regarding the screening criteria of recovery time, surface stability, charge transfer, and sensitivity to CO. The proposed AL framework can be extended for property finetuning in the combinatorial chemical space.