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Printed flexible electronics have received extensive attention due to their significant potential for advancing wearable technologies, such as for monitoring human physiological health and biomechanics. However, current manufacturing techniques (e.g., inkjet printing and screen printing) of these electronics are typically limited by high cost, lengthy fabrication times, and types of print materials. Thus, this study investigates a novel manufacturing technique, namely corona-enabled electrostatic printing (CEP), which leverages high voltage discharged in the air to attract feedstock material particles onto substrates. The CEP technique can potentially fabricate various functional materials in milliseconds, forming binder-free microstructures. This study focuses on optimizing the CEP technique to produce high-performance, flexible, piezoresistive strain sensors. Here, the strain sensors will be fabricated with carbon nanotubes (CNTs) using different discharge voltages. The effect of the discharge voltage (i.e., a critical fabrication parameter) on the sensing performance will be characterized via electromechanical testing. In addition, to better understand the sensing mechanism of the samples, finite element analysis will be performed to investigate the electromechanical response of the CEP-fabricated binder-free CNT networks. Here, computational material models will be established based on microstructures of the CNT networks, which will be acquired from experimental microscopic imaging. Overall, this study will fundamentally advance the CEP manufacturing process for flexible electronics. 

The mode of star formation that results in the formation of globular clusters and young massive clusters is difficult to constrain through observations. We present models of massive star cluster formation using the TORCHframework, which uses the Astrophysical MUltipurpose Software Environment (AMUSE) to couple distinct multi-physics codes that handle star formation, stellar evolution and dynamics, radiative transfer, and magnetohydrodynamics. We upgraded TORCHby implementing the N-body code PETAR, thereby enabling TORCHto handle massive clusters forming from 10⁶M_⊙clouds with ≥10⁵individual stars. We present results from TORCHsimulations of star clusters forming from 10⁴,  10⁵, and 10⁶M_⊙turbulent spherical gas clouds (named M4, M5, M6) of radiusR= 11.7 pc. We find that star formation is highly efficient and becomes more so at a higher cloud mass and surface density. For M4, M5, and M6 with initial surface densities 2.325 × 10^1,2,3M_⊙pc⁻², after a free-fall time oft_ff= 6.7,2.1,0.67 Myr, we find that ∼30%, 40%, and 60% of the cloud mass has formed into stars, respectively. The end of simulation-integrated star formation efficiencies for M4, M5, and M6 areϵ_⋆ = M_⋆/M_cloud = 36%, 65%, and 85%. Observations of nearby clusters similar in mass and size to M4 have instantaneous star formation efficiencies ofϵ_inst ≤ 30%, which is slightly lower than the integrated star formation efficiency of M4. The M5 and M6 models represent a different regime of cluster formation that is more appropriate for the conditions in starburst galaxies and gas-rich galaxies at high redshift, and that leads to a significantly higher efficiency of star formation. We argue that young massive clusters build up through short efficient bursts of star formation in regions that are sufficiently dense (Σ ≥ 10²M_⊙pc⁻²) and massive (M_cloud≥ 10⁵M_⊙). In such environments, stellar feedback from winds and radiation is not strong enough to counteract the gravity from gas and stars until a majority of the gas has formed into stars. 

Global food shortage demands significant progress in crop production. Greenhouses offer a solution for higher crop production by providing a controllable environment. Excess levels of humidity, however, encourage pests and diseases, drastically reducing crop yields. Traditional humidity control methods for greenhouses are expensive and energy-intensive. In addition to this, nonbiodegradable plastic covers cause massive white pollution. To tackle these concerns, we present smart glazing and sensors for greenhouse humidity regulation through both passive and active paths. We created biodegradable humidity-sensitive films by blending poly(ethylene glycol) (PEG) with cellulose acetate (CA). PEG/CA covers can automatically open for air circulation at high humidity, successfully demonstrating repeatable greenhouse humidity regulation to as low as 60% relative humidity. PEG/CA-based humidity sensors can actively accelerate air circulation and humidity reduction with repeated cycles at an even higher efficiency. Overall, our research introduces a low-cost, all-in-one, sustainable, and environmentally conscious solution for addressing the greenhouse humidity control challenges. Approximately, this solution can potentially achieve annual energy savings of up to 56.6 GWh for the U.S. if fully applied.