H₂S Stability of Metal–Organic Frameworks: A Computational Assessment

Abstract

The H 2 S stability of a range of metal−organic frameworks (MOFs) was systematically assessed by first-principles calculations. The most likely degradation mechanism was first determined and we identified the rate constant of the degradation reaction as a reliable descriptor for characterizing the H 2 S stability of MOFs. A qualitative H 2 S stability ranking was thus established for the list of investigated materials. Structure−stability relationships were further envisaged considering several variables including the nature of the linkers and their grafted functional groups, the pore size, the nature of metal sites, and the presence/nature of coordinatively unsaturated sites. This knowledge enabled the anticipation of the H 2 S stability of one prototypical MOF, e.g., MIL-91(Ti), which has been previously proposed as a good candidate for CO 2 capture. This computational strategy enables an accurate and easy handling assessment of the H 2 S stability of MOFs and offers a solid alternative to experimental characterizations that require the manipulation of a highly toxic and corrosive molecule.

SO2 Capture by Two Aluminum-Based MOFs: Rigid-like MIL-53(Al)-TDC versus Breathing MIL-53(Al)-BDC

“…The PXRD pattern collected for this sample revealed a retention of the initial crystal structure with a small loss of the crystallinity (Figure S16). This makes MIL-53­(Al)-TDC among the few very MOFs stable under this harsh operating condition. − …”

“…Most of these MOFs are chemically and thermally stable and have aroused tremendous interests in diverse areas including gas separation, − gas storage, − ion exchange, − chemical sensing, − drug delivery, − and catalysis, − among others. More specifically, a series of MOFs was demonstrated to be able to capture corrosive and hazardous gases such as H 2 S and SO 2 ; however, some of them exhibit limited stability toward these contaminants. − Typically, Ba 0.5 [Ni 8 (OH) 3 (EtO) 3 (BPD_NH 2 ) 5.5 ], MIL-125­(Ti)–NH 2 , and MOF-177 show high SO 2 adsorption capacities (5.6, 7.9, and 25.7 mmol g –1 , respectively) but suffer from structural degradation upon exposure to this toxic gas. In particular, the structural stability of MOFs in wet SO 2 conditions [50–65% relative humidity (RH)] is a fundamental concern .…”

Hydrogen sulfide capture and removal technologies: A comprehensive review of recent developments and emerging trends

“…In general, this property should be possible to be tuned by varying the nature of the metal sites. MOFs based on Cu, Zn, and Fe are commonly used yet unstable for H 2 S adsorption, whereas MOFs based on Al, Mg, Ni, V, Zr, and Ti demonstrate robustness to H 2 S environments [340,341]. Furthermore, the organic linkers and their functionalization also has a major effect on the material's stability [342].…”

“…For example, fluorinated MOFs exhibited high stability against H 2 S and H 2 O [343][344][345]. Even though the exact mechanism or cause of structural deformation in MOFs is still unknown, a few studies [341,346] have theoretically evaluated the interactions between MOFs and acid gases (H 2 S and SO 2 ) and proposed strategies to tune the topology and functionalization of MOFs towards stability. In addition to chemical stability, the high price of MOFs is a hurdle for their industrial adoption.…”

A State-of-the-Art of Metal-Organic Frameworks for Chromium Photoreduction vs. Photocatalytic Water Remediation

“…Here is where machine learning could play a disruptive role during the coming years in order to identify, screen, classify, and correlate MOFs’ potentials on the basis of geometric, chemical, topological, energetic, and performance-based descriptors [ 316 , 317 ]. In fact, machine learning is already having a deep impact on unraveling synthesis paths and engineering strategies of MOFs for gas adsorption and separation purposes [ 318 , 319 , 320 , 321 ]. As far as the investigations of MOFs for photo-oxidative and photoreductive processes expand, it is more likely that machine learning could be applied to unravel the underpinning chemical–physical features that make the MOFs feasible for this application.…”