@article{haldar2024steering,

title = {Steering diffusion selectivity of chemical isomers within aligned nanochannels of metal-organic framework thin film},

author = {Ritesh Haldar and Tanmoy Maity and Susmita Sarkar and Susmita Kundu and Suvendu Panda and Arighna Sarkar and Kalyaneswar Mandal and Soumya Ghosh and Jagannath Mondal},

url = {https://www.researchsquare.com/article/rs-4046811/v1},

doi = {https://doi.org/10.21203/rs.3.rs-4046811/v1},

year  = {2024},

date = {2024-03-21},

urldate = {2024-03-21},

abstract = {The movement of molecules (i.e. diffusion) within angstrom-scale pores of porous materials such as metal-organic frameworks (MOFs) and zeolites is influenced by multiple complex factors that can be challenging to assess and manipulate. Nevertheless, understanding and controlling this diffusion phenomenon is crucial for advancing energy-economic membrane-based chemical separation technologies, as well as for heterogeneous catalysis and sensing applications. Through precise assessment of the factors influencing diffusion within a porous metal-organic framework (MOF) thin film, we have developed a chemical strategy to manipulate and reverse chemical isomer diffusion selectivity. In the process of cognizing the molecular diffusion within oriented, angstrom-scale channels of MOF thin film, we have unveiled a dynamic chemical interaction between the adsorbate (chemical isomers) and the MOF using a combination of kinetic mass uptake experiments and molecular simulation. Leveraging the dynamic chemical interactions, we have reversed the haloalkane (positional) isomer diffusion selectivity, forging a novel chemical pathway to elevate the overall efficacy of membrane-based chemical separation and selective catalytic reactions.},

keywords = {Metal organic frameworks, MOFs, molecular diffusion, nanoporous materials, openQCM sensors, QCM, Quartz Crystal Microbalance},

pubstate = {published},

tppubtype = {article}

}

@article{okur2024optimized,

title = {Optimized Detection of Volatile Organic Compounds Utilizing Durable and Selective Arrays of Tailored UiO-66-X SURMOF Sensors},

author = {Salih Okur and Tawheed Hashem and Evgenia Bogdanova and Patrick Hodapp and Lars Heinke and Stefan Bräse and Christof Wöll},

url = {https://pubs.acs.org/doi/abs/10.1021/acssensors.3c01575},

doi = {https://doi.org/10.1021/acssensors.3c01575},

year  = {2024},

date = {2024-02-06},

urldate = {2024-01-01},

journal = {ACS sensors},

publisher = {ACS Publications},

abstract = {Metal–organic frameworks (MOFs), with their well-defined and highly flexible nanoporous architectures, provide a material platform ideal for fabricating sensors. We demonstrate that the efficacy and specificity of detecting and differentiating volatile organic compounds (VOCs) can be significantly enhanced using a range of slightly varied MOFs. These variations are obtained via postsynthetic modification (PSM) of a primary framework. We alter the original MOF’s guest adsorption affinities by incorporating functional groups into the MOF linkers, which yields subtle changes in responses. These responses are subsequently evaluated by using machine learning (ML) techniques. Under severe conditions, such as high humidity and acidic environments, sensor stability and lifespan are of utmost importance. The UiO-66-X MOFs demonstrate the necessary durability in acidic, neutral, and basic environments with pH values ranging from 2 to 11, thus surpassing most other similar materials. The UiO-66-NH2 thin films were deposited on quartz-crystal microbalance (QCM) sensors in a high-temperature QCM liquid cell using a layer-by-layer pump method. Three different, highly stable surface-anchored MOFs (SURMOFs) of UiO-66-X obtained via the PSM approach (X: NH2, Cl, and N3) were employed to fabricate arrays suitable for electronic nose applications. These fabricated sensors were tested for their capability to distinguish between eight VOCs. Data from the sensor array were processed using three distinct ML techniques: linear discriminant (LDA), nearest neighbor (k-NN), and neural network analysis methods. The discrimination accuracies achieved were nearly 100% at high concentrations and over 95% at lower concentrations (50–100 ppm).},

keywords = {Adsorption, Liquids, Metal organic frameworks, openQCM Q-1, QCM-D, sensors, Volatile organic compounds},

pubstate = {published},

tppubtype = {article}

}