Programming hydrogel adhesion with engineered polymer network topology.

Hydrogel adhesion that can be easily modulated in magnitude, space, and time is desirable in many emerging applications ranging from tissue engineering and soft robotics to wearable devices. In synthetic materials, these complex adhesion behaviors are often achieved individually with mechanisms and apparatus that are difficult to integrate. Here, we report a universal strategy to embody multifaceted adhesion programmability in synthetic hydrogels. By designing the surface network topology of a hydrogel, supramolecular linkages that result in contrasting adhesion behaviors are formed on the hydrogel interface. The incorporation of different topological linkages leads to dynamically tunable adhesion with high-resolution spatial programmability without alteration of bulk mechanics and chemistry. Further, the association of linkages enables stable and tunable adhesion kinetics that can be tailored to suit different applications. We rationalize the physics of polymer chain slippage, rupture, and diffusion at play in the emergence of the programmable behaviors. With the understanding, we design and fabricate various soft devices such as smart wound patches, fluidic channels, drug-eluting devices, and reconfigurable soft robotics. Our study presents a simple and robust platform in which adhesion controllability in multiple aspects can be easily integrated into a single design of a hydrogel network.

Metamaterial adhesives for programmable adhesion through reverse crack propagation.

Adhesives are typically either strong and permanent or reversible with limited strength. However, current strategies to create strong yet reversible adhesives needed for wearable devices, robotics and material disassembly lack independent control of strength and release, require complex fabrication or only work in specific conditions. Here we report metamaterial adhesives that simultaneously achieve strong and releasable adhesion with spatially selectable adhesion strength through programmed cut architectures. Nonlinear cuts uniquely suppress crack propagation by forcing cracks to propagate backwards for 60× enhancement in adhesion, while allowing crack growth in the opposite direction for easy release and reusability. This mechanism functions in numerous adhesives on diverse substrates in wet and dry conditions and enables highly tunable adhesion with independently programmable adhesion strength in two directions simultaneously at any location. We create these multifunctional materials in a maskless, digital fabrication framework to rapidly customize adhesive characteristics with deterministic control for next-generation adhesives.

Peri-lesion regions in differentiating suspicious breast calcification-only lesions specifically on contrast enhanced mammography.

PURPOSE: The explore the added value of peri-calcification regions on contrast-enhanced mammography (CEM) in the differential diagnosis of breast lesions presenting as only calcification on routine mammogram. METHODS: Patients who underwent CEM because of suspicious calcification-only lesions were included. The test set included patients between March 2017 and March 2019, while the validation set was collected between April 2019 and October 2019. The calcifications were automatically detected and grouped by a machine learning-based computer-aided system. In addition to extracting radiomic features on both low-energy (LE) and recombined (RC) images from the calcification areas, the peri-calcification regions, which is generated by extending the annotation margin radially with gradients from 1 mm to 9 mm, were attempted. Machine learning (ML) models were built to classify calcifications into malignant and benign groups. The diagnostic matrices were also evaluated by combing ML models with subjective reading. RESULTS: Models for LE (significant features: wavelet-LLL_glcm_Imc2_MLO; wavelet-HLL_firstorder_Entropy_MLO; wavelet-LHH_glcm_DifferenceVariance_CC; wavelet-HLL_glcm_SumEntropy_MLO;wavelet-HLH_glrlm_ShortRunLowGray LevelEmphasis_MLO; original_firstorder_Entropy_MLO; original_shape_Elongation_MLO) and RC (significant features: wavelet-HLH_glszm_GrayLevelNonUniformityNormalized_MLO; wavelet-LLH_firstorder_10Percentile_CC; original_firstorder_Maximum_MLO; wavelet-HHH_glcm_Autocorrelation_MLO; original_shape_Elongation_MLO; wavelet-LHL_glszm_GrayLevelNonUniformityNormalized_MLO; wavelet-LLH_firstorder_RootMeanSquared_MLO) images were set up with 7 features. Areas under the curve (AUCs) of RC models are significantly better than those of LE models with compact and expanded boundary (RC v.s. LE, compact: 0.81 v.s. 0.73, p < 0.05; expanded: 0.89 v.s. 0.81, p < 0.05) and RC models with 3 mm boundary extension yielded the best performance compared to those with other sizes (AUC = 0.89). Combining with radiologists' reading, the 3mm-boundary RC model achieved a sensitivity of 0.871 and negative predictive value of 0.937 with similar accuracy of 0.843 in predicting malignancy. CONCLUSIONS: The machine learning model integrating intra- and peri-calcification regions on CEM has the potential to aid radiologists' performance in predicting malignancy of suspicious breast calcifications.

Comparing the diagnostic performance of QuantiFERON-TB Gold Plus with QFT-GIT, T-SPOT.TB and TST: a systematic review and meta-analysis.

BACKGROUND: QuantiFERON-TB Gold Plus (QFT-Plus) is an important test that has emerged in recent years for detecting TB infection. We conducted a review to compare the sensitivity, specificity and positive rate of QFT-Plus with that of QuantiFERON-TB Gold In-Tube (QFT-GIT), T-cell spot of tuberculosis assay (T-SPOT.TB) and Tuberculin test (TST). METHODS: PubMed and Embase were searched, without language restrictions, from 1 January 2015 to 31 March 2022 using "Mycobacterium tuberculosis Infections" and "QuantiFERON-TB-Plus" as search phrases. We estimated the sensitivity from studies of patients with active tuberculosis, specificity from studies of populations with very low risk of TB exposure, and positive rate from studies of high-risk populations. The methodological quality of the eligible studies was assessed, and a random-effects model meta-analysis was used to determine the risk difference (RD). We assessed the pooled rate by using a random-effects model. This study was registered in PROSPERO (CRD 42021267432). RESULTS: Of 3996 studies, 83 were eligible for full-text screening and 41 were included in the meta-analysis. In patients with active TB, the sensitivity of QFT-Plus was compared to that of QFT-GIT and T-SPOT.TB, respectively, and no statistically differences were found. In populations with a very low risk of TB exposure, the specificity of QFT-Plus was compared with that of QFT-GTI and T-SPOT.TB, respectively, and no statistically differences were found. Two studies were eligible to compare the specificity of the QFT-Plus test with that of the TST test, and the pooled RD was 0.12 (95% CI 0.02 to 0.22). In high-risk populations, 18 studies were eligible to compare the positive rate of the QFT-Plus test with that of the QFT-GIT test, and the pooled RD was 0.02 (95% CI 0.01 to 0.03). The positive rate of QFT-Plus was compared with that of T-SPOT.TB and TST groups, and no statistically differences were found. CONCLUSIONS: The diagnostic performance of QFT-Plus was similar to that of QFT-GIT and T-SPOT.TB, but was slightly more specific than TST.

Diabetes mellitus and latent tuberculosis infection: an updated meta-analysis and systematic review.

BACKGROUND: Previous studies have demonstrated an association between diabetes mellitus (DM) and latent tuberculosis infection (LTBI). This study was conducted to update the current understanding of the association between DM and LTBI. By conducting a systematic review and meta-analysis using adjusted odds ratios (aOR) or risk ratios (aRR), we aimed to further explore the association between DM and LTBI and provide essential reference for future research. METHODS: We conducted comprehensive searches in Embase, Cochrane Library, and PubMed without imposing any start date or language restrictions, up to July 19, 2022. Our study selection encompassed observational research that compared from LTBI positive rates in both DM and non-DM groups and reported aRR or aOR results. The quality of the included studies was assessed utilizing the Newcastle-Ottawa Scale. Pooled effect estimates were calculated using random-effects models, along with their associated 95% confidence intervals (CI). RESULTS: We included 22 studies involving 68,256 subjects. Three cohort studies were eligible, with a pooled aRR of 1.26 (95% CI: 0.71-2.23). Nineteen cross-sectional studies were eligible, with a pooled aOR of 1.21 (95% CI: 1.14-1.29). The crude RR (cRR) pooled estimate for three cohort studies was 1.62 (95% CI: 1.03-2.57). Among the cross-sectional studies we included, sixteen studies provided crude ORs, and the crude OR (cOR) pooled estimate was 1.64 (95% CI: 1.36-1.97). In the diagnosis of diabetes, the pooled aOR of the HbA1c group was higher than that of self-reported group (pooled aOR: 1.56, 95% CI: 1.24-1.96 vs. 1.17, 95% CI: 1.06-1.28). CONCLUSION: Our systematic review and meta-analysis suggest a positive association between DM and LTBI. Individuals with DM may have a higher risk of LTBI compared to those without DM. These findings provide important insights for future research and public health interventions in managing LTBI in diabetic populations.

Super-multiplex vibrational imaging.

The ability to visualize directly a large number of distinct molecular species inside cells is increasingly essential for understanding complex systems and processes. Even though existing methods have successfully been used to explore structure-function relationships in nervous systems, to profile RNA in situ, to reveal the heterogeneity of tumour microenvironments and to study dynamic macromolecular assembly, it remains challenging to image many species with high selectivity and sensitivity under biological conditions. For instance, fluorescence microscopy faces a 'colour barrier', owing to the intrinsically broad (about 1,500 inverse centimetres) and featureless nature of fluorescence spectra that limits the number of resolvable colours to two to five (or seven to nine if using complicated instrumentation and analysis). Spontaneous Raman microscopy probes vibrational transitions with much narrower resonances (peak width of about 10 inverse centimetres) and so does not suffer from this problem, but weak signals make many bio-imaging applications impossible. Although surface-enhanced Raman scattering offers high sensitivity and multiplicity, it cannot be readily used to image specific molecular targets quantitatively inside live cells. Here we use stimulated Raman scattering under electronic pre-resonance conditions to image target molecules inside living cells with very high vibrational selectivity and sensitivity (down to 250 nanomolar with a time constant of 1 millisecond). We create a palette of triple-bond-conjugated near-infrared dyes that each displays a single peak in the cell-silent Raman spectral window; when combined with available fluorescent probes, this palette provides 24 resolvable colours, with the potential for further expansion. Proof-of-principle experiments on neuronal co-cultures and brain tissues reveal cell-type-dependent heterogeneities in DNA and protein metabolism under physiological and pathological conditions, underscoring the potential of this 24-colour (super-multiplex) optical imaging approach for elucidating intricate interactions in complex biological systems.

Clinical value of contrast-enhanced ultrasound enhancement patterns for differentiating solid pancreatic lesions.

OBJECTIVES: To explore the diagnostic value of contrast-enhanced ultrasound (CEUS) enhancement patterns for differentiating solid pancreatic lesions and compare them with conventional ultrasound (US) and enhanced computed tomography (CT). METHODS: A total of 210 patients with solid pancreatic lesions who had definite pathological or clinical diagnoses were enrolled. Six CEUS enhancement patterns were proposed for solid pancreatic lesions. Two US doctors blindly observed the CEUS patterns of solid pancreatic lesions and the interrater agreement was analyzed. The diagnostic value of CEUS enhancement patterns for differentiating solid pancreatic lesions was evaluated, and the diagnostic accuracy was compared with that of US and enhanced CT. RESULTS: There was good concordance for six CEUS enhancement patterns of solid pancreatic lesions between the two doctors, with a kappa value of 0.767. Hypo-enhancement (Hypo-E) or centripetal enhancement (Centri-E) as the diagnostic criteria for pancreatic carcinoma had an accuracy of 87.62%; hyper-enhancement (Hyper-E) for neuroendocrine tumors had an accuracy of 92.89%; capsular enhancement with low or uneven enhancement inside the tumor (Capsular-E) for solid pseudopapillary tumors had an accuracy of 97.63%; and iso-enhancement (Iso-E) or iso-enhancement with focal hypo-enhancement (Iso-fhypo-E) for focal pancreatitis had an accuracy of 89.10%. The diagnostic accuracy of CEUS was significantly different from that of US for 210 cases of solid pancreatic lesions (p < 0.05) and was not significantly different from that of enhanced CT for 146 cases of solid pancreatic lesions (p > 0.05). CONCLUSIONS: The different enhancement patterns of solid pancreatic lesions on CEUS were clinically valuable for differentiation. KEY POINTS: • Six CEUS enhancement (E) patterns, including Hyper-E, Iso-E, Iso-fhypo-E, Hypo-E, Centri-E, and Capsular-E, are proposed for the characterization of solid pancreatic lesions. • Using Hypo-E or Centri-E as the diagnostic criteria for pancreatic carcinoma, Hyper-E for neuroendocrine tumors, Capsular-E for solid pseudopapillary tumors, and Iso-E or Iso-fhypo-E for focal pancreatitis on CEUS had relatively high diagnostic accuracy. • The diagnostic accuracy of CEUS was greatly increased over that of US and was not different from that of enhanced CT.

Supermultiplexed optical imaging and barcoding with engineered polyynes.

Optical multiplexing has a large impact in photonics, the life sciences and biomedicine. However, current technology is limited by a 'multiplexing ceiling' from existing optical materials. Here we engineered a class of polyyne-based materials for optical supermultiplexing. We achieved 20 distinct Raman frequencies, as 'Carbon rainbow', through rational engineering of conjugation length, bond-selective isotope doping and end-capping substitution of polyynes. With further probe functionalization, we demonstrated ten-color organelle imaging in individual living cells with high specificity, sensitivity and photostability. Moreover, we realized optical data storage and identification by combinatorial barcoding, yielding to our knowledge the largest number of distinct spectral barcodes to date. Therefore, these polyynes hold great promise in live-cell imaging and sorting as well as in high-throughput diagnostics and screening.

Stimulation Modulates Adhesion and Mechanics of Hydrogel Adhesives.

The ability to modulate the adhesion of soft materials on-demand is desired for broad applications ranging from tissue repair to soft robotics. Research effort has been focused on the chemistry and architecture of interfaces, leaving the mechanics of soft adhesives overlooked. Stimuli-responsive mechanisms of smart hydrogels could be leveraged for achieving stimuli-responsive hydrogel adhesives that respond mechanically to external stimuli. Such stimuli-responsive hydrogel adhesives involve complex chemomechanical coupling and interfacial fracture phenomena, calling for mechanistic understanding to enable rational design. Here, we combine experimental, computational, and analytical approaches to study a thermo-responsive hydrogel adhesive. Experimentally, we show that the adhesion and mechanical properties of a stimuli-responsive hydrogel adhesive are both enhanced by the application of a stimulus. Our analysis further reveals that the enhanced adhesion stems from the increased fracture energy of the bulk hydrogel and the insignificant residual stress on the adhesive-tissue interface. This study presents a framework for designing stimuli-responsive hydrogel adhesives based on the modulation of bulk properties and sheds light on the development of smart adhesives with tunable mechanics.

Deep learning model improves radiologists' performance in detection and classification of breast lesions.

OBJECTIVE: Computer-aided diagnosis using deep learning algorithms has been initially applied in the field of mammography, but there is no large-scale clinical application. METHODS: This study proposed to develop and verify an artificial intelligence model based on mammography. Firstly, mammograms retrospectively collected from six centers were randomized to a training dataset and a validation dataset for establishing the model. Secondly, the model was tested by comparing 12 radiologists' performance with and without it. Finally, prospectively enrolled women with mammograms from six centers were diagnosed by radiologists with the model. The detection and diagnostic capabilities were evaluated using the free-response receiver operating characteristic (FROC) curve and ROC curve. RESULTS: The sensitivity of model for detecting lesions after matching was 0.908 for false positive rate of 0.25 in unilateral images. The area under ROC curve (AUC) to distinguish the benign lesions from malignant lesions was 0.855 [95% confidence interval (95% CI): 0.830, 0.880]. The performance of 12 radiologists with the model was higher than that of radiologists alone (AUC: 0.852 vs. 0.805, P=0.005). The mean reading time of with the model was shorter than that of reading alone (80.18 s vs. 62.28 s, P=0.032). In prospective application, the sensitivity of detection reached 0.887 at false positive rate of 0.25; the AUC of radiologists with the model was 0.983 (95% CI: 0.978, 0.988), with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 94.36%, 98.07%, 87.76%, and 99.09%, respectively. CONCLUSIONS: The artificial intelligence model exhibits high accuracy for detecting and diagnosing breast lesions, improves diagnostic accuracy and saves time.

Microfluidic Cytometry for High-Throughput Characterization of Single Cell Cytoplasmic Viscosity Using Crossing Constriction Channels.

This article presents an approach of microfluidic flow cytometry capable of continuously characterizing cytoplasmic viscosities of single cells. The microfluidic system consists of a major constriction channel and a side constriction channel perpendicularly crossing each other. Cells are forced to rapidly travel through the major channel and are partially aspirated into the side channel when passing the channel junction. Numerical simulations were conducted to model the time dependence of the aspiration length into the side channel, which enables the measurement of cytoplasmic viscosity by fitting the model results to experimental data. As a demonstration for high-throughput measurement, the cytoplasmic viscosities of HL-60 cells that were native or treated by N-Formylmethionine-leucyl-phenylalanine (fMLP) were quantified with sample sizes as large as thousands of cells. Both the average and median cytoplasmic viscosities of native HL-60 cells were found to be about 10% smaller than those of fMLP-treated HL-60 cells, consistent with previous observations that fMLP treatment can increase the rigidity of white blood cells. Furthermore, the microfluidic system was used to process granulocytes from three donors (sample size >1,000 cells for each donor). The results revealed that the cytoplasmic viscosity of granulocytes from one donor was significantly higher than the other two, which may result from the fact that this donor just recovered from an inflammation. In summary, the developed microfluidic system can collect cytoplasmic viscosities from thousands of cells and may function as an enabling tool in the field of single-cell analysis. © 2019 International Society for Advancement of Cytometry.

Enhanced recovery after surgery program in the patients undergoing hepatectomy for benign liver lesions.

BACKGROUND: Enhanced recovery after surgery (ERAS) has shown effectiveness in terms of reducing the hospital stay and cost. However, the benefit of ERAS in patients undergoing hepatectomy for benign liver lesions is still unclear. METHODS: ERAS was implemented in our center since March 1st, 2018. From September 2016 to February 2018, 109 patients were enrolled into the control group, and from March 2018 to June 2019, 124 patients were enrolled into the ERAS group. All the indicators related to operation, liver functions, and postoperative outcomes were included in the analysis. RESULTS: The clinicopathologic baselines were similar in these two groups. A significantly higher proportion of patients underwent laparoscopic surgery in the ERAS group. On the whole, intraoperative blood loss (100.00 mL vs. 200.00 mL, P < 0.001), blood transfusion (3.23% vs. 10.09%, P = 0.033), total bilirubin (17.10 µmol/L vs. 21.00 µmol/L, P = 0.041), D-dimer (2.08 µg/mL vs. 2.57 µg/mL, P = 0.031), postoperative hospital stay (5.00 d vs. 6.00 d, P < 0.001), and postoperative morbidity (16.13% vs. 32.11%, P = 0.008) were significantly shorter or less in the ERAS group than those in the control group. After stratified by operation methods, ERAS group showed significantly shorter postoperative hospital stay in both open and laparoscopic operation (both P < 0.001). In patients underwent open surgery, ERAS group demonstrated significantly shorter operative duration (131.76 ± 8.75 min vs. 160.73 ± 7.23 min, P = 0.016), less intraoperative blood loss (200.00 mL vs. 450.00 mL, P = 0.008) and less postoperative morbidity (16.00% vs. 44.44%, P = 0.040). CONCLUSIONS: ERAS program may be safe and effective for the patients underwent hepatectomy, especially open surgery, for benign liver lesions.

Three-Dimensional Microscale Imaging and Measurement of Soft Material Contact Interfaces under Quasi-Static Normal Indentation and Shear.

Understanding the contact and friction between soft materials is vital to a wide variety of engineering applications including soft sealants and medical devices such as catheters and stents. Although the mechanisms of friction between stiff materials have been extensively studied, the mechanisms of friction between soft materials are much less understood. Time-dependent material responses, large deformations, and fluid layers at the contact interface, common in soft materials, pose new challenges toward understanding the friction between soft materials. This article aims to characterize the three-dimensional (3D) contact interfaces in soft materials under large deformations and complex contact conditions. Specifically, we introduce a microindentation and visualization (MIV) system capable of investigating soft material contact interfaces with combined normal and shear loading. When combined with a laser scanning confocal microscope, the MIV system enables the acquisition of 3D image stacks of the deformed substrate and the indenter under fixed normal and shear displacements. The 3D imaging data allows us to quantify the 3D contact profiles and correlate them with the applied normal and shear displacements. Using a spherical indenter and a hydrogel substrate as a model system, we demonstrate that the MIV system and the associated analysis techniques accurately measure the contact area under combined normal and shear loading. Although the limited speed of confocal scanning implies that this method is most suitable for quasi-static loading conditions, potential methods to increase the imaging speed and the corresponding trade-off in image resolution are discussed. The method presented here will be useful for the future investigation of soft material contact and friction involving complex surface geometries.

A ratiometric Raman probe for live-cell imaging of hydrogen sulfide in mitochondria by stimulated Raman scattering.

Stimulated Raman Scattering (SRS) coupled with alkyne tags has been an emerging imaging technique to visualize small-molecule species with high sensitivity and specificity. Here we describe the development of a ratiometric Raman probe for visualizing hydrogen sulfide (H2S) species in living cells as the first alkyne-based sensor for SRS microscopy. This probe uses an azide unit as a selective reactive site, and it targets mitochondria with high specificity. The SRS ratiometric images show a strong response to H2S level changes in living cells.

Adhesion, Stiffness, and Instability in Atomically Thin MoS2 Bubbles.

We measured the work of separation of single and few-layer MoS2 membranes from a SiOx substrate using a mechanical blister test and found a value of 220 ± 35 mJ/m2. Our measurements were also used to determine the 2D Young's modulus (E2D) of a single MoS2 layer to be 160 ± 40 N/m. We then studied the delamination mechanics of pressurized MoS2 bubbles, demonstrating both stable and unstable transitions between the bubbles' laminated and delaminated states as the bubbles were inflated. When they were deflated, we observed edge pinning and a snap-in transition that are not accounted for by the previously reported models. We attribute this result to adhesion hysteresis and use our results to estimate the work of adhesion of our membranes to be 42 ± 20 mJ/m2.

Characterizing Adhesion between a Micropatterned Surface and a Soft Synthetic Tissue.

The work of adhesion and work of separation are characteristic properties of a contact interface that describe the amount of energy per unit area required to adhere or separate two contacting substrates, respectively. In this work, the authors present experimental and data analysis procedures that allow the contact interface between a soft synthetic tissue and a smooth or micropatterned poly(dimethylsiloxane) (PDMS) substrate to be characterized in terms of these characteristic parameters. Because of physical geometry limitations, the experimental contact geometry chosen for this study differs from conventional test geometries. Therefore, the authors used finite element modeling to develop correction factors specific to the experimental contact geometry used in this work. A work of adhesion was directly extracted from experimental data while the work of separation was estimated on the basis of experimental results. These values are compared to other theoretical calculations for validation. The results of this work indicate that the micropatterned PDMS substrate significantly decreases both the work of adhesion and work of separation as compared to a smooth PDMS substrate when in contact with a soft synthetic tissue substrate.

The Instrumentation of a Microfluidic Analyzer Enabling the Characterization of the Specific Membrane Capacitance, Cytoplasm Conductivity, and Instantaneous Young's Modulus of Single Cells.

This paper presents the instrumentation of a microfluidic analyzer enabling the characterization of single-cell biophysical properties, which includes seven key components: a microfluidic module, a pressure module, an imaging module, an impedance module, two LabVIEW platforms for instrument operation and raw data processing, respectively, and a Python code for data translation. Under the control of the LabVIEW platform for instrument operation, the pressure module flushes single cells into the microfluidic module with raw biophysical parameters sampled by the imaging and impedance modules and processed by the LabVIEW platform for raw data processing, which were further translated into intrinsic cellular biophysical parameters using the code developed in Python. Based on this system, specific membrane capacitance, cytoplasm conductivity, and instantaneous Young's modulus of three cell types were quantified as 2.76 ± 0.57 µF/cm², 1.00 ± 0.14 S/m, and 3.79 ± 1.11 kPa for A549 cells (ncell = 202); 1.88 ± 0.31 µF/cm², 1.05 ± 0.16 S/m, and 3.74 ± 0.75 kPa for 95D cells (ncell = 257); 2.11 ± 0.38 µF/cm², 0.87 ± 0.11 S/m, and 5.39 ± 0.89 kPa for H460 cells (ncell = 246). As a semi-automatic instrument with a throughput of roughly 1 cell per second, this prototype instrument can be potentially used for the characterization of cellular biophysical properties.

Influences of Substrate Adhesion and Particle Size on the Shape Memory Effect of Polystyrene Particles.

Formulations and applications of micro- and nanoscale polymer particles have proliferated rapidly in recent years, yet knowledge of their mechanical behavior has not grown accordingly. In this study, we examine the ways that compressive strain, substrate surface energy, and particle size influence the shape memory cycle of polystyrene particles. Using nanoimprint lithography, differently sized particles are programmed into highly deformed, temporary shapes in contact with substrates of differing surface energies. Atomic force microscopy is used to obtain in situ measurements of particle shape recovery kinetics, and scanning electron microscopy is employed to assess differences in the profiles of particles at the conclusion of the shape memory cycle. Finally, finite element models are used to investigate the growing impact of surface energies at smaller length scales. Results reveal that the influence of substrate adhesion on particle recovery is size-dependent and can become dominating at submicron length scales.

Fracture toughness of hydrogels: measurement and interpretation.

The fracture mechanics of hydrogels, especially those with significantly enhanced toughness, has attracted extensive research interests. In this article we discuss the experimental measurement and theoretical interpretation of the fracture toughness for soft hydrogels. We first review the definition of fracture toughness for elastic materials, and the commonly used experimental configurations to measure it. In reality most gels are inelastic. For gels that are rate insensitive, we discuss how to interpret the fracture toughness associated with two distinct scenarios: crack initiation and steady-state crack propagation. A formulation to estimate energy dissipation during steady-state crack propagation is developed, and connections to previous models in the literature are made. For gels with rate-dependent behaviors, we review the physical mechanisms responsible for the rate-dependence, and outline the difficulties to rigorously define the fracture toughness for both crack initiation and propagation. We conclude by discussing a few fundamental questions on the fracture of tough gels that are yet to be answered.

Thermomechanics of a temperature sensitive covalent adaptable polymer with bond exchange reactions.

We study a covalent adaptable polymer that can rearrange its network topology through thermally activated bond exchange reactions. When the polymer is deformed, such a network rearrangement leads to macroscopic stress relaxation, which allows the polymer to be thermoformed without a mold. Based on a previously developed constitutive model, we investigate thermal-mechanical behaviors of this material under a non-uniform and evolving temperature field through numerical simulations. Our focus is on the complex coupling between mechanical deformation, heat conduction and bond exchange reactions. Several examples are presented to illustrate the effects of non-uniform heating: uniaxial tension under heat conduction, torsion of a thin strip with local heating and thermal imprinting. Our results show that during non-uniform heating the material in the high temperature region creeps. This causes a redistribution of the deformation field and thus results in a final shape that deviates from the prescribed shape. The final shapes after thermoforming can be tuned by controlling the extent of heat conduction through different combinations of heating temperature and time. For example, with high temperature and a short heating time, it is possible to approximately confine stress relaxation and thus shape fixity within the local heating region. This is not the case if low temperature and a long heating time are used. These results can be utilized to design the temporal and spatial sequences of local heating during thermoforming to achieve various complex final shapes.