@article{hunter2024covalent,

title = {Covalent Capture of Nanoparticle-Stabilized Oil Droplets via Acetal Chemistry Using a Hydrophilic Polymer Brush},

author = {Saul J Hunter and Evelin Csányi and Joshua JS Tyler and Mark A Newell and Matthew AH Farmer and Camery Ma and George Sanderson and Graham J Leggett and Edwin C Johnson and Steven P Armes},

url = {https://pubs.acs.org/doi/full/10.1021/acs.langmuir.4c03897},

doi = {https://doi.org/10.1021/acs.langmuir.4c03897},

year  = {2024},

date = {2024-12-06},

urldate = {2024-01-01},

journal = {Langmuir},

publisher = {ACS Publications},

abstract = {We report the capture of nanosized oil droplets using a hydrophilic aldehyde-functional polymer brush. The brush was obtained via aqueous ARGET ATRP of a cis-diol-functional methacrylic monomer from a planar silicon wafer. This precursor was then selectively oxidized using an aqueous solution of NaIO4 to introduce aldehyde groups. The oil droplets were prepared by using excess sterically stabilized diblock copolymer nanoparticles to prepare a relatively coarse squalane-in-water Pickering emulsion (mean droplet diameter = 20 μm). This precursor was then further processed via high-pressure microfluidization to produce ∼200 nm squalane droplets. We demonstrate that adsorption of these nanosized oil droplets involves acetal bond formation between the cis-diol groups located on the steric stabilizer chains and the aldehyde groups on the brush. This interaction occurs under relatively mild conditions and can be tuned by adjusting the solution pH. Hence this is a useful model system for understanding oil droplet interactions with soft surfaces.},

keywords = {Adsorption, lipids, Liquids, Nanoemulsions, nanoparticles, openQCM NEXT, QCM, QCM-D, 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}

}