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Aerosol jet printing is a popular digital additive manufacturing method for flexible and hybrid electronics, but it lacks sophisticated real‐time process control schemes that would enable more widespread adoption in manufacturing environments. Here, an optical measurement system is introduced to track the aerosol density upstream of the printhead. The measured optical extinction, combined with the aerosol flow rate, is directly related to deposition rate and accurately predicts functional materials properties such as the electrical resistance of printed graphene films. This real‐time system offers a compelling solution for process drift and batch‐to‐batch variability, rendering it a valuable tool for both real‐time control of aerosol jet printing and fundamental studies of the underlying process science.

 

Chemical sensors based on solution‐processed 2D nanomaterials represent an extremely attractive approach toward scalable and low‐cost devices. Through the implementation of real‐time impedance spectroscopy and development of a three‐element circuit model, redox exfoliated MoS₂nanoflakes demonstrate an ultrasensitive empirical detection limit of NO₂gas at 1 ppb, with an extrapolated ultimate detection limit approaching 63 ppt. This sensor construct reveals a more than three orders of magnitude improvement from conventional direct current sensing approaches as the traditionally dominant interflake interactions are bypassed in favor of selectively extracting intraflake doping effects. This same approach allows for an all solution‐processed, flexible 2D sensor to be fabricated on a polyimide substrate using a combination of graphene contacts and drop‐casted MoS₂nanoflakes, exhibiting similar sensitivity limits. Finally, a thermal annealing strategy is used to explore the tunability of the nanoflake interactions and subsequent circuit model fit, with a demonstrated sensitivity improvement of 2× with thermal annealing at 200 °C.

 

To achieve the high energy densities demanded by emerging technologies, lithium battery electrodes need to approach the volumetric and specific capacity limits of their electrochemically active constituents, which requires minimization of the inactive components of the electrode. However, a reduction in the percentage of inactive conductive additives limits charge transport within the battery electrode, which results in compromised electrochemical performance. Here, an electrode design that achieves efficient electron and lithium‐ion transport kinetics at exceptionally low conductive additive levels and industrially relevant active material areal loadings is introduced. Using a scalable Pickering emulsion approach, Ni‐rich LiNi_0.8Co_0.15Al_0.05O₂(NCA) cathode powders are conformally coated using only 0.5 wt% of solution‐processed graphene, resulting in an electrical conductivity that is comparable to 5 wt% carbon black. Moreover, the conformal graphene coating mitigates degradation at the cathode surface, thus providing improved electrochemical cycle life. The morphology of the electrodes also facilitates rapid lithium‐ion transport kinetics, which provides superlative rate capability. Overall, this electrode design concurrently approaches theoretical volumetric and specific capacity limits without tradeoffs in cycle life, rate capability, or active material areal loading.

 

Abstract Rapid, inexpensive, and easy-to-use coronavirus disease 2019 (COVID-19) home tests are key tools in addition to vaccines in the world wide fight to eliminate national and local shutdowns. However, currently available tests for SARS-CoV-2, the virus that causes COVID-19, are too expensive, painful, and irritating, or not sufficiently sensitive for routine, accurate home testing. Herein, we employ custom-formulated graphene inks and aerosol jet printing to create a rapid electrochemical immunosensor for direct detection of SARS-CoV-2 spike receptor-binding domain (RBD) in saliva samples acquired noninvasively. This sensor demonstrated limits of detection that are considerably lower than most commercial SARS-CoV-2 antigen tests (22.91 ± 4.72 pg ml −1 for spike RBD and 110.38 ± 9.00 pg ml −1 for spike S1) as well as fast response time (∼30 min), which was facilitated by the functionalization of printed graphene electrodes in a single-step with SARS-CoV-2 polyclonal antibody through the carbodiimide reaction without the need for nanoparticle functionalization or secondary antibody or metallic nanoparticle labels. This immunosensor presents a wide linear sensing range from 1 to 1000 ng ml −1 and does not react with other coexisting influenza viruses such as H1N1 hemagglutinin. By combining high-yield graphene ink synthesis, automated printing, high antigen selectivity, and rapid testing capability, this work offers a promising alternative to current SARS-CoV-2 antigen tests. 

Machine learning (ML) is becoming an effective tool for studying 2D materials. Taking as input computed or experimental materials data, ML algorithms predict the structural, electronic, mechanical, and chemical properties of 2D materials that have yet to be discovered. Such predictions expand investigations on how to synthesize 2D materials and use them in various applications, as well as greatly reduce the time and cost to discover and understand 2D materials. This tutorial review focuses on the understanding, discovery, and synthesis of 2D materials enabled by or benefiting from various ML techniques. We introduce the most recent efforts to adopt ML in various fields of study regarding 2D materials and provide an outlook for future research opportunities. The adoption of ML is anticipated to accelerate and transform the study of 2D materials and their heterostructures.