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Record W6891741897 · doi:10.48336/g3nd-gw70

Ultramicroporous metal-organic frameworks and porphyrin linker design toward gas-based applications

2023· article· en· W6891741897 on OpenAlexaff

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2023
Typearticle
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsFlue gasGas separationAdsorptionMetal-organic frameworkMoleculeCarbon fibersCarbon dioxideMicroporous materialPorphyrin

Abstract

fetched live from OpenAlex

Metal-Organic Frameworks (MOFs) are a class of materials characterized by their highly porous nature. MOFs are ordered structures made up of well-defined metal ‘nodes’ that are bridged to each other through coordinating organic ‘linkers’. These frameworks have been an appealing area of research in recent years thanks to their numerous applications. Regarding gas separation in MOFs, a conventional approach is to develop a material that has a high affinity for one gas of interest, and lower affinities for other gases that may appear in a mixture. A typical example is the removal of carbon dioxide from flue gas exhaust. MOFs have been developed that can strongly and selectively bind carbon dioxide while in the presence of gases such as nitrogen, oxygen, and nitrogen oxides, and even water vapour. These separations can be challenging when the gases to be separated are low in abundance (e.g., atmospheric sequestration) or when they cannot be bound selectively over other gases. An alternative approach to separation is a method that relies on the differences in molecular size of the gases in a mixture, so-called molecular sieving. Chapter 2 describes two such MOFs (Zn2M; M = Zn or Cd), whose ultramicropores (pore width < 0.7 nm) make them capable of molecular sieving. The crystal structures of these MOFs were examined at different temperatures (100 and 273 K) and with different solvent molecules in the pores (DMSO and methanol) to help better understand the structural effects on their gas adsorption and separation properties. Critically, changing from Zn to Cd in the trimetallic node of the MOFs results in a sub-˚A change in the pore opening. At the molecular scale, this change resulted in a drastic difference in gas adsorption between the two MOFs. Zn3 only allows carbon dioxide to enter its framework, whereas Zn2Cd permits carbon dioxide, argon, nitrogen, and methane to enter the pores. The data suggest that Zn3 could be an excellent sieve for separating carbon dioxide from mixtures, even at environmental concentrations. Regarding the synthesis of MOFs, one of the ways to obtain MOFs with new topologies and unique properties is to design novel organic linkers. One class of organic molecules that lends itself well to creativity and modification is porphyrins. As the ‘pigments of life’, porphyrins are found everywhere in nature and have been used in applications ranging from catalysis to optics to therapeutics, and of course have been used as linkers in MOFs. Porphyrins in MOFs offer an additional dimension to the tuneability of the framework, as the porphyrin linker itself can coordinate a metal through the central nitrogens, changing the properties of the framework without affecting its structure. To date, porphyrin linkers have been predominantly made to coordinate to MOF nodes through substituents on their meso methine regions. Porphyrin MOF linkers where the linking moieties extend from the β-positions are as of yet unknown, leaving plenty of room for exploration. Chapter 3 discusses the synthetic methods that could give access to these linkers, as well as the progress made towards these linkers. Although ultimately the desired porphyrins could not be isolated and used in MOF synthesis due to the delays associated with the COVID-19 pandemic, Chapter 3 illustrates that the chemistry works and puzzles out the synthetic route necessary to obtain β-subsituted porphyrin linkers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.258
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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