Accelerated
non-enzymatic and metal-free “reaction and extraction”
of sugar esters at the interface of two immiscible liquid microdroplets
is demonstrated. The bimolecular reaction occurs by collision of microdroplets
originating from two home-built electrospray sonic ion sources, carrying
sugar molecules in water and long-chain fatty acids in toluene, respectively.
Our method shows that the rate of reaction is enhanced ∼107 times in comparison to the bulk, initiated by ultrasonic
activation. Such a high rate of reaction in the microdroplets can
be attributed to factors such as surface activity, concentration enhancement,
partial solvation, and temperature-assisted dehydration of the species
occurring in microdroplets. We provide evidence for an interfacial
nucleophilic addition–elimination reaction mechanism. This
method of synthesis is extended to 18 similar reactions. Microdroplet
synthesis offers a sustainable method for biphasic reactions, eliminating
the need for phase transfer reagents and activating agents such as
acids/bases, metals, or enzymes.
Imprinted charged aqueous droplets of micrometer dimensions containing spherical gold and silver nanoparticles, gold nanorods, proteins and simple molecules were visualized using dark-field and transmission electron microscopies. With such studies,...
Molecular de-aggregation was observed at the air/water interface of aqueous microdroplets. We probed this phenomenon using dyes such as Rhodamine 6G (R6G), Rhodamine B, Acridine orange, and Fluorescein, which show...
A frugal humidity sensor that can detect changes in the humidity of exhaled breath of individuals has been fabricated. The sensor comprises a humiditysensitive conducting polymer that is in situ formed on a cloth that acts as a substrate. Interdigitated silver electrodes were screen-printed on the modified cloth, and conducting threads connected the electrodes to the measurement circuit. The sensor's response to changing humidity was measured as a voltage drop across the sensor using a microcontroller. The sensor was capable of discerning between fast, normal, and slow breathing based on the response time. A response time of ∼1.3 s was observed for fast breathing. An Android-based mobile application was designed to collect sensor data via Bluetooth for analysis. A time series classification algorithm was implemented to analyze patterns in breathing. The sensor was later stitched onto a face mask, transforming it into a smart mask that can monitor changes in the breathing pattern at work, play, and sleep.
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