It is commonly believed that the overall permeation resistance of thin film composite (TFC) membranes is dictated by the crosslinked, ultrathin polyamide barrier layer, while the porous support ...merely serves as the mechanical support. Although this assumption might be the case under low transmembrane pressure, it becomes questionable under high transmembrane pressure. A highly porous support normally yields under a pressure of a few MPa, which can result in a significant level of compressive strain that may significantly increase the resistance to permeation. However, quantifying the influence of porous support deformation on the overall resistance of the TFC membrane is challenging. In particular, it is difficult to determine the deformation/strain of the membrane during active separation. In this study, we use nanoimprint lithography (NIL) to achieve precise compressive deformation in commercial TFC membranes. By adjusting the NIL conditions, membranes were compressed to strain levels up to 60%. SEM and AFM measurements showed that the compression had minimal impact on the barrier-layer surface morphology and total surface area with most of the deformation occurring in the support layer. DI water permeation measurements revealed that the water flux reduction decreases with an increase of strain level. Most significantly, the intrinsic membrane resistance showed negligible changes at strain levels lower than 30%–40%, but increased exponentially at higher strain levels, reaching 250%–500% of pristine (unstrained) membrane values. Using a resistance-in-series model, the strain dependency of the TFC membrane resistance can be described.
•Different levels of compressive strain were imposed on TFC membranes using Nanoimprint Lithography.•The intrinsic membrane resistance of the compressed TFC membranes was determined.•The intrinsic membrane resistance increases significantly when compressive strain in the support layer is above 40%.•The influence of support layer deformation on the TFC membrane resistance can be described using a resistance-in-series model.
Low-total-force contact resonance force microscopy (LTF-CRFM), an atomic force microscopy method, is introduced as a non-destructive means to quantify the local viscoelastic loss tangent (
tan
δ
)
of ...supported cellulose nanofibrils (CNFs). The method limits static and dynamic forces during measurement to minimize substrate and geometry effects and to reduce the potential for stress-induced CNF damage. LTF-CRFM uses Brownian motion to achieve the thermally-limited lowest dynamic force, while approaching adhesive pull-off to achieve the low static force. LTF-CRFM measurements were shown to generate analyzable data without evidence of nonlinear artifacts and without damage to the CNF over static forces ranging from 11.6 to 84.6 nN. The measured
tan
δ
of CNFs was 0.015 ± 0.0094, which is the first reported
tan
δ
measurement of an isolated CNF. Finally, LTF-CRFM successfully mapped
tan
δ
along the length of CNFs to determine that kink defects along the CNF do not impart a local viscoelastic property change at the spatial resolution of the measurement.
Background Cardiac fibroblasts (CFs) have the ability to sense stiffness changes and respond to biochemical cues to modulate their states as either quiescent or activated myofibroblasts. Given the ...potential for secretion of bioactive molecules to modulate the cardiac microenvironment, we sought to determine how the CF secretome changes with matrix stiffness and biochemical cues and how this affects cardiac myocytes via paracrine signaling. Methods and Results Myofibroblast activation was modulated in vitro by combining stiffness cues with TGFβ1 (transforming growth factor β 1) treatment using engineered poly (ethylene glycol) hydrogels, and in vivo with isoproterenol treatment. Stiffness, TGFβ1, and isoproterenol treatment increased AKT (protein kinase B) phosphorylation, indicating that this pathway may be central to myofibroblast activation regardless of the treatment. Although activation of AKT was shared, different activating cues had distinct effects on downstream cytokine secretion, indicating that not all activated myofibroblasts share the same secretome. To test the effect of cytokines present in the CF secretome on paracrine signaling, neonatal rat ventricular cardiomyocytes were treated with CF conditioned media. Conditioned media from myofibroblasts cultured on stiff substrates and activated by TGFβ1 caused hypertrophy, and one of the cytokines in that media was insulin growth factor 1, which is a known mediator of cardiac myocyte hypertrophy. Conclusions Culturing CFs on stiff substrates, treating with TGFβ1, and in vivo treatment with isoproterenol all caused myofibroblast activation. Each cue had distinct effects on the secretome or genes encoding the secretome, but only the secretome of activated myofibroblasts on stiff substrates treated with TGFβ1 caused myocyte hypertrophy, most likely through insulin growth factor 1.
Data‐driven U‐net machine learning (ML) models, including the pix2pix conditional generative adversarial network (cGAN), are shown to predict 3D printed voxel geometry in digital light processing ...(DLP) additive manufacturing. A confocal microscopy‐based workflow allows for the high‐throughput acquisition of data on thousands of voxel interactions arising from randomly gray‐scaled digital photomasks. Validation between prints and predictions shows accurate predictions with sub‐pixel scale resolution. The trained cGAN performs virtual DLP experiments such as feature size‐dependent cure depth, anti‐aliasing, and sub‐pixel geometry control. The pix2pix model is also applicable to larger masks than it is trained on. To this end, the model can qualitatively inform layer‐scale and voxel‐scale print failures in real 3D‐printed parts. Overall, machine learning models and the data‐driven methodology, exemplified by U‐nets and cGANs, show considerable promise for predicting and correcting photomasks to achieve increased precision in DLP additive manufacturing.
A high‐throughput, data‐driven microscopy workflow enables machine learning (ML) predictions of high‐resolution voxel patterns in vat photopolymerization (VP) additive manufacturing. Various U‐net models, such as pix2pix conditional generative adversarial network (cGAN), are investigated and compared. Overall, all U‐net models can provide excellent predictions of voxel geometry.
The presence of electrostatic forces and associated artifacts complicates the interpretation of piezoresponse force microscopy (PFM) and electrochemical strain microscopy (ESM). Eliminating these ...artifacts provides an opportunity for precisely mapping domain wall structures and dynamics, accurately quantifying local piezoelectric coupling coefficients, and reliably investigating hysteretic processes at the single nanometer scale to determine properties and mechanisms which underly important applications including computing, batteries and biology. Here we exploit the existence of an electrostatic blind spot (ESBS) along the length of the cantilever, due to the distributed nature of the electrostatic force, which can be universally used to separate unwanted long range electrostatic contributions from short range electromechanical responses of interest. The results of ESBS-PFM are compared to state-of-the-art interferometric displacement sensing PFM, showing excellent agreement above their respective noise floors. Ultimately, ESBS-PFM allows for absolute quantification of piezoelectric coupling coefficients independent of probe, lab or experimental conditions. As such, we expect the widespread adoption of EBSB-PFM to be a paradigm shift in the quantification of nanoscale electromechanics.
Electrostatic forces complicate the interpretation of piezoresponse force microscopy (PFM). Electrostatic blind spot (ESBS) PFM overcomes these complications by placing the detection laser where it is sensitive piezoresponse but not electrostatics.
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