Direct Patterning of Perovskite Nanocrystals on Nanophotonic Cavities with Electrohydrodynamic Inkjet Printing.

Overcoming the challenges of patterning luminescent materials will unlock additive and more sustainable paths for the manufacturing of next-generation on-chip photonic devices. Electrohydrodynamic (EHD) inkjet printing is a promising method for deterministically placing emitters on these photonic devices. However, the use of this technique to pattern luminescent lead halide perovskite nanocrystals (NCs), notable for their defect tolerance and impressive optical and spin coherence properties, for integration with optoelectronic devices remains unexplored. In this work, we additively deposit nanoscale CsPbBr3 NC features on photonic structures via EHD inkjet printing. We perform transmission electron microscopy of EHD inkjet printed NCs to demonstrate that the NCs' structural integrity is maintained throughout the printing process. Finally, NCs are deposited with sub-micrometer control on an array of parallel silicon nitride nanophotonic cavities and demonstrate cavity-emitter coupling via photoluminescence spectroscopy. These results demonstrate EHD inkjet printing as a scalable, precise method to pattern luminescent nanomaterials for photonic applications.

Monolithic 3D printing of embeddable and highly stretchable strain sensors using conductive ionogels.

Medical training simulations that utilize 3D-printed, patient-specific tissue models improve practitioner and patient understanding of individualized procedures and capacitate pre-operative, patient-specific rehearsals. The impact of these novel constructs in medical training and pre-procedure rehearsals has been limited, however, by the lack of effectively embedded sensors that detect the location, direction, and amplitude of strains applied by the practitioner on the simulated structures. The monolithic fabrication of strain sensors embedded into lifelike tissue models with customizable orientation and placement could address this limitation. The demonstration of 3D printing of an ionogel as a stretchable, piezoresistive strain sensor embedded in an elastomer is presented as a proof-of-concept of this integrated fabrication for the first time. The significant hysteresis and drift inherent to solid-phase piezoresistive composites and the dimensional instability of low-hysteresis piezoresistive liquids inspired the adoption of a 3D-printable piezoresistive ionogel composed of reduced graphene oxide and an ionic liquid. The shear-thinning rheology of the ionogel obviates the need to fabricate additional structures that define or contain the geometry of the sensing channel. Sensors are printed on and subsequently encapsulated in polydimethylsiloxane (PDMS), a thermoset elastomer commonly used for analog tissue models, to demonstrate seamless fabrication. Strain sensors demonstrate geometry- and strain-dependent gauge factors of 0.54-2.41, a high dynamic strain range of 350% that surpasses the failure strain of most dermal and viscus tissue, low hysteresis (<3.5% degree of hysteresis up to 300% strain) and baseline drift, a single-value response, and excellent fatigue stability (5000 stretching cycles). In addition, we fabricate sensors with stencil-printed silver/PDMS electrodes in place of wires to highlight the potential of seamless integration with printed electrodes. The compositional tunability of ionic liquid/graphene-based composites and the shear-thinning rheology of this class of conductive gels endows an expansive combination of customized sensor geometry and performance that can be tailored to patient-specific, high-fidelity, monolithically fabricated tissue models.

Data-driven design of a multiplexed, peptide-sensitized transistor to detect breath VOC markers of COVID-19.

Exhaled human breath contains a rich mixture of volatile organic compounds (VOCs) whose concentration can vary in response to disease or other stressors. Using simulated odorant-binding proteins (OBPs) and machine learning methods, we designed a multiplex of short VOC- and carbon-binding peptide probes that detect a characteristic "VOC fingerprint". Specifically, we target VOCs associated with COVID-19 in a compact, molecular sensor array that directly transduces vapor composition into multi-channel electrical signals. Rapidly synthesizable, chimeric VOC- and solid-binding peptides were derived from selected OBPs using multi-sequence alignment with protein database structures. Selective peptide binding to targeted VOCs and sensor surfaces was validated using surface plasmon resonance spectroscopy and quartz crystal microbalance. VOC sensing was demonstrated by peptide-sensitized, exposed-channel carbon nanotube transistors. The data-to-device pipeline enables the development of novel devices for non-invasive monitoring, diagnostics of diseases, and environmental exposure assessment.

Attaching perylene dyes to polyfluorene: three simple, efficient methods for facile color tuning of light-emitting polymers.

The emission color of fluorene-based polymers can be facilely tuned across the whole visible spectrum by copolymerization with perylene dyes. Methods are demonstrated for incorporation of the dyes in the polymer mainchain, at the chain termini, or as side chains. Efficient energy transfer causes the emission to come solely from the dye units. Efficient LEDs have been made from the copolymers with dyes in the mainchain.