Isatuximab, Bortezomib, Lenalidomide, and Dexamethasone for Multiple Myeloma.

BACKGROUND: Bortezomib, lenalidomide, and dexamethasone (VRd) is a preferred first-line treatment option for patients with newly diagnosed multiple myeloma. Whether the addition of the anti-CD38 monoclonal antibody isatuximab to the VRd regimen would reduce the risk of disease progression or death among patients ineligible to undergo transplantation is unclear. METHODS: In an international, open-label, phase 3 trial, we randomly assigned, in a 3:2 ratio, patients 18 to 80 years of age with newly diagnosed multiple myeloma who were ineligible to undergo transplantation to receive either isatuximab plus VRd or VRd alone. The primary efficacy end point was progression-free survival. Key secondary end points included a complete response or better and minimal residual disease (MRD)-negative status in patients with a complete response. RESULTS: A total of 446 patients underwent randomization. At a median follow-up of 59.7 months, the estimated progression-free survival at 60 months was 63.2% in the isatuximab-VRd group, as compared with 45.2% in the VRd group (hazard ratio for disease progression or death, 0.60; 98.5% confidence interval, 0.41 to 0.88; P<0.001). The percentage of patients with a complete response or better was significantly higher in the isatuximab-VRd group than in the VRd group (74.7% vs. 64.1%, P = 0.01), as was the percentage of patients with MRD-negative status and a complete response (55.5% vs. 40.9%, P = 0.003). No new safety signals were observed with the isatuximab-VRd regimen. The incidence of serious adverse events during treatment and the incidence of adverse events leading to discontinuation were similar in the two groups. CONCLUSIONS: Isatuximab-VRd was more effective than VRd as initial therapy in patients 18 to 80 years of age with newly diagnosed multiple myeloma who were ineligible to undergo transplantation. (Funded by Sanofi and a Cancer Center Support Grant; IMROZ ClinicalTrials.gov number, NCT03319667.).

METAPHOR: Metabolic evaluation through phasor-based hyperspectral imaging and organelle recognition for mouse blastocysts and oocytes.

Only 30% of embryos from in vitro fertilized oocytes successfully implant and develop to term, leading to repeated transfer cycles. To reduce time-to-pregnancy and stress for patients, there is a need for a diagnostic tool to better select embryos and oocytes based on their physiology. The current standard employs brightfield imaging, which provides limited physiological information. Here, we introduce METAPHOR: Metabolic Evaluation through Phasor-based Hyperspectral Imaging and Organelle Recognition. This non-invasive, label-free imaging method combines two-photon illumination and AI to deliver the metabolic profile of embryos and oocytes based on intrinsic autofluorescence signals. We used it to classify i) mouse blastocysts cultured under standard conditions or with depletion of selected metabolites (glucose, pyruvate, lactate); and ii) oocytes from young and old mouse females, or in vitro-aged oocytes. The imaging process was safe for blastocysts and oocytes. The METAPHOR classification of control vs. metabolites-depleted embryos reached an area under the ROC curve (AUC) of 93.7%, compared to 51% achieved for human grading using brightfield imaging. The binary classification of young vs. old/in vitro-aged oocytes and their blastulation prediction using METAPHOR reached an AUC of 96.2% and 82.2%, respectively. Finally, organelle recognition and segmentation based on the flavin adenine dinucleotide signal revealed that quantification of mitochondria size and distribution can be used as a biomarker to classify oocytes and embryos. The performance and safety of the method highlight the accuracy of noninvasive metabolic imaging as a complementary approach to evaluate oocytes and embryos based on their physiology.

Association between tissue loss type and amputation risk among Medicare patients with concomitant diabetes and peripheral arterial disease.

OBJECTIVE: Prior studies have described risk factors associated with amputation in patients with concomitant diabetes and peripheral arterial disease (DM/PAD). However, the association between the severity and extent of tissue loss type and amputation risk remains less well-described. We aimed to quantify the role of different tissue loss types in amputation risk among patients with DM/PAD, in the context of demographic, preventive, and socioeconomic factors. METHODS: Applying International Classification of Diseases (ICD)-9 and ICD-10 codes to Medicare claims data (2007-2019), we identified all patients with continuous fee-for-service Medicare coverage diagnosed with DM/PAD. Eight tissue loss categories were established using ICD-9 and ICD-10 diagnosis codes, ranging from lymphadenitis (least severe) to gangrene (most severe). We created a Cox proportional hazards model to quantify associations between tissue loss type and 1- and 5-year amputation risk, adjusting for age, race/ethnicity, sex, rurality, income, comorbidities, and preventive factors. Regional variation in DM/PAD rates and risk-adjusted amputation rates was examined at the hospital referral region level. RESULTS: We identified 12,257,174 patients with DM/PAD (48% male, 76% White, 10% prior myocardial infarction, 30% chronic kidney disease). Although 2.2 million patients (18%) had some form of tissue loss, 10.0 million patients (82%) did not. The 1-year crude amputation rate (major and minor) was 6.4% in patients with tissue loss, and 0.4% in patients without tissue loss. Among patients with tissue loss, the 1-year any amputation rate varied from 0.89% for patients with lymphadenitis to 26% for patients with gangrene. The 1-year amputation risk varied from two-fold for patients with lymphadenitis (adjusted hazard ratio, 1.96; 95% confidence interval, 1.43-2.69) to 29-fold for patients with gangrene (adjusted hazard ratio, 28.7; 95% confidence interval, 28.1-29.3), compared with patients without tissue loss. No other demographic variable including age, sex, race, or region incurred a hazard ratio for 1- or 5-year amputation risk higher than the least severe tissue loss category. Results were similar across minor and major amputation, and 1- and 5-year amputation outcomes. At a regional level, higher DM/PAD rates were inversely correlated with risk-adjusted 5-year amputation rates (R2 = 0.43). CONCLUSIONS: Among 12 million patients with DM/PAD, the most significant predictor of amputation was the presence and extent of tissue loss, with an association greater in effect size than any other factor studied. Tissue loss could be used in awareness campaigns as a simple marker of high-risk patients. Patients with any type of tissue loss require expedited wound care, revascularization as appropriate, and infection management to avoid amputation. Establishing systems of care to provide these interventions in regions with high amputation rates may prove beneficial for these populations.

A Retrospective Analysis of Surgical Acuity & Procedure Volume Before & During the COVID-19 Pandemic.

INTRODUCTION: In March 2020, the American College of Surgeons recommended postponing elective procedures amid the COVID-19 pandemic. We used Medicare claims to analyze changes in surgical and interventional procedure volumes from 2016 to 2021. METHODS: We studied 37 common surgical and interventional procedures using 5% Medicare claims files from January 1, 2016, through December 31, 2021. Procedures were classified according to American College of Surgeons guidelines as low, intermediate, or high acuity, and counts were analyzed per calendar year quarter (Q1-Q4), with stratification by sex and race/ethnicity. RESULTS: We observed 1,840,577 procedures and identified two periods of marked decline. In Q2 2020, overall procedure counts decreased by 32.2%, with larger declines in low (41.1%) and intermediate (30.8%) acuity procedures. High acuity procedures declined the least (18.2%). Overall volumes increased afterward but never returned to baseline. Another marked decline occurred in Q4 2021, with all acuity levels having declined to a similar extent (40.1%, 44.2%, and 46.9% for low, intermediate, and high acuity, respectively). High and intermediate acuity procedures declined more in Q4 2021 than Q2 2020 (P = 0.002). Similar patterns were observed across sex and race/ethnicity strata. CONCLUSIONS: Two major procedural volume declines occurred between 2020 and 2022 during the COVID-19 pandemic in the United States. High acuity (life or limb threatening) procedures were least affected in the first decline (Q2 2020) but not spared in second decline (Q4 2021). Future efforts should prioritize preserving high-acuity access during times of stress.

Advanced Image Stitching Method for Dual-Sensor Inspection.

Efficient image stitching plays a vital role in the Non-Destructive Evaluation (NDE) of infrastructures. An essential challenge in the NDE of infrastructures is precisely visualizing defects within large structures. The existing literature predominantly relies on high-resolution close-distance images to detect surface or subsurface defects. While the automatic detection of all defect types represents a significant advancement, understanding the location and continuity of defects is imperative. It is worth noting that some defects may be too small to capture from a considerable distance. Consequently, multiple image sequences are captured and processed using image stitching techniques. Additionally, visible and infrared data fusion strategies prove essential for acquiring comprehensive information to detect defects across vast structures. Hence, there is a need for an effective image stitching method appropriate for infrared and visible images of structures and industrial assets, facilitating enhanced visualization and automated inspection for structural maintenance. This paper proposes an advanced image stitching method appropriate for dual-sensor inspections. The proposed image stitching technique employs self-supervised feature detection to enhance the quality and quantity of feature detection. Subsequently, a graph neural network is employed for robust feature matching. Ultimately, the proposed method results in image stitching that effectively eliminates perspective distortion in both infrared and visible images, a prerequisite for subsequent multi-modal fusion strategies. Our results substantially enhance the visualization capabilities for infrastructure inspection. Comparative analysis with popular state-of-the-art methods confirms the effectiveness of the proposed approach.

Robust Multi-Modal Image Registration for Image Fusion Enhancement in Infrastructure Inspection.

Efficient multi-modal image fusion plays an important role in the non-destructive evaluation (NDE) of infrastructures, where an essential challenge is the precise visualizing of defects. While automatic defect detection represents a significant advancement, the determination of the precise location of both surface and subsurface defects simultaneously is crucial. Hence, visible and infrared data fusion strategies are essential for acquiring comprehensive and complementary information to detect defects across vast structures. This paper proposes an infrared and visible image registration method based on Euclidean evaluation together with a trade-off between key-point threshold and non-maximum suppression. Moreover, we employ a multi-modal fusion strategy to investigate the robustness of our image registration results.

Advancements in and Research on Coplanar Capacitive Sensing Techniques for Non-Destructive Testing and Evaluation: A State-of-the-Art Review.

In contrast to conventional non-destructive testing (NDT) and non-destructive evaluation (NDE) methodologies, including radiography, ultrasound, and eddy current analysis, coplanar capacitive sensing technique emerges as a novel and promising avenue within the field. This paper endeavors to elucidate the efficacy of coplanar capacitive sensing, also referred to as capacitive imaging (CI), within the realm of NDT. Leveraging extant scholarly discourse, this review offers a comprehensive and methodical examination of the coplanar capacitive technique, encompassing its fundamental principles, factors influencing sensor efficacy, and diverse applications for defect identification across various NDT domains. Furthermore, this review deliberates on extant challenges and anticipates future trajectories for the technique. The manifold advantages inherent to coplanar capacitive sensing vis-à-vis traditional NDT methodologies not only afford its versatility in application but also underscore its potential for pioneering advancements in forthcoming applications.

Noninvasive prediction models of intra-amniotic infection in women with preterm labor.

BACKGROUND: Among women with preterm labor, those with intra-amniotic infection present the highest risk of early delivery and the most adverse outcomes. The identification of intra-amniotic infection requires amniocentesis, perceived as too invasive by women and physicians. Noninvasive methods for identifying intra-amniotic infection and/or early delivery are crucial to focus early efforts on high-risk preterm labor women while avoiding unnecessary interventions in low-risk preterm labor women. OBJECTIVE: This study modeled the best performing models, integrating biochemical data with clinical and ultrasound information to predict a composite outcome of intra-amniotic infection and/or spontaneous delivery within 7 days. STUDY DESIGN: From 2015 to 2020, data from a cohort of women, who underwent amniocentesis to rule in or rule out intra-amniotic infection or inflammation, admitted with a diagnosis of preterm labor at <34 weeks of gestation at the Hospital Clinic and Hospital Sant Joan de Déu, Barcelona, Spain, were used. At admission, transvaginal ultrasound was performed, and maternal blood and vaginal samples were collected. Using high-dimensional biology, vaginal proteins (using multiplex immunoassay), amino acids (using high-performance liquid chromatography), and bacteria (using 16S ribosomal RNA gene amplicon sequencing) were explored to predict the composite outcome. We selected ultrasound, maternal blood, and vaginal predictors that could be tested with rapid diagnostic techniques and developed prediction models employing machine learning that was applied in a validation cohort. RESULTS: A cohort of 288 women with preterm labor at <34 weeks of gestation, of which 103 (35%) had a composite outcome of intra-amniotic infection and/or spontaneous delivery within 7 days, were included in this study. The sample was divided into derivation (n=116) and validation (n=172) cohorts. Of note, 4 prediction models were proposed, including ultrasound transvaginal cervical length, maternal C-reactive protein, vaginal interleukin 6 (using an automated immunoanalyzer), vaginal pH (using a pH meter), vaginal lactic acid (using a reflectometer), and vaginal Lactobacillus genus (using quantitative polymerase chain reaction), with areas under the receiving operating characteristic curve ranging from 82.2% (95% confidence interval, ±3.1%) to 85.2% (95% confidence interval, ±3.1%), sensitivities ranging from 76.1% to 85.9%, and specificities ranging from 75.2% to 85.1%. CONCLUSION: The study results have provided proof of principle of how noninvasive methods suitable for point-of-care systems can select high-risk cases among women with preterm labor and might substantially aid in clinical management and outcomes while improving the use of resources and patient experience.

Automatic Deep Learning-Based Pipeline for Automatic Delineation and Measurement of Fetal Brain Structures in Routine Mid-Trimester Ultrasound Images.

INTRODUCTION: The aim of this study was to develop a pipeline using state-of-the-art deep learning methods to automatically delineate and measure several of the most important brain structures in fetal brain ultrasound (US) images. METHODS: The dataset was composed of 5,331 images of the fetal brain acquired during the routine mid-trimester US scan. Our proposed pipeline automatically performs the following three steps: brain plane classification (transventricular, transthalamic, or transcerebellar plane); brain structures delineation (9 different structures); and automatic measurement (from the structure delineations). The methods were trained on a subset of 4,331 images and each step was evaluated on the remaining 1,000 images. RESULTS: Plane classification reached 98.6% average class accuracy. Brain structure delineation obtained an average pixel accuracy higher than 96% and a Jaccard index higher than 70%. Automatic measurements get an absolute error below 3.5% for the four standard head biometries (head circumference, biparietal diameter, occipitofrontal diameter, and cephalic index), 9% for transcerebellar diameter, 12% for cavum septi pellucidi ratio, and 26% for Sylvian fissure operculization degree. CONCLUSIONS: The proposed pipeline shows the potential of deep learning methods to delineate fetal head and brain structures and obtain automatic measures of each anatomical standard plane acquired during routine fetal US examination.

Experimental Study and FEM Simulations for Detection of Rebars in Concrete Slabs by Coplanar Capacitive Sensing Technique.

In the present study, a relatively novel non-destructive testing (NDT) method called the coplanar capacitive sensing technique was applied in order to detect different sizes of rebars in a reinforced concrete (RC) structure. This technique effectively detects changes in the dielectric properties during scanning in various sections of concrete with and without rebars. Numerical simulations were carried out by three-dimensional (3D) finite element modelling (FEM) in COMSOL Multiphysics software to analyse the impact of the presence of rebars on the electric field generated by the coplanar capacitive probe. In addition, the effect of the presence of a surface defect on the rebar embedded in the concrete slab was demonstrated by the same software for the first time. Experiments were performed on a concrete slab containing rebars, and were compared with FEM results. The results showed that there is a good qualitative agreement between the numerical simulations and experimental results.

Automated Impact Damage Detection Technique for Composites Based on Thermographic Image Processing and Machine Learning Classification.

Composite materials are one of the primary structural components in most current transportation applications, such as the aerospace industry. Composite material diagnostics is a promising area in the fight against structural damage in aircraft and spaceships. Detection and diagnostic technologies often provide analysts with a valuable and rapid mechanism to monitor the health and safety of composite materials. Although many attempts have been made to develop damage detection techniques and make operations more efficient, there is still a need to develop/improve existing methods. Pulsed thermography (PT) technology was used in this study to obtain healthy and defective data sets from custom-designed composite samples having similar dimensions but different thicknesses (1.6 and 3.8). Ten carbon fibre-reinforced plastic (CFRP) panels were tested. The samples were subjected to impact damage of various energy levels, ranging from 4 to 12 J. Two different methods have been applied to detect and classify the damage to the composite structures. The first applied method is the statistical analysis, where seven different statistical criteria have been calculated. The final results have proved the possibility of detecting the damaged area in most cases. However, for a more accurate detection technique, a machine learning method was applied to thermal images; specifically, the Cube Support Vector Machine (SVM) algorithm was selected. The prediction accuracy of the proposed classification models was calculated within a confusion matrix based on the dataset patterns representing the healthy and defective areas. The classification results ranged from 78.7% to 93.5%, and these promising results are paving the way to develop an automated model to efficiently evaluate the damage to composite materials based on the non-distractive testing (NDT) technique.

3D fluorescent mapping of invisible molecular damage after cavitation in hydrogen exposed elastomers.

Elastomers saturated with gas at high pressure suffer from cavity nucleation, inflation, and deflation upon rapid or explosive decompression. Although this process often results in undetectable changes in appearance, it causes internal damage, hampers functionality (e.g., permeability), and shortens lifetime. Here, we tag a model poly(ethyl acrylate) elastomer with π-extended anthracene-maleimide adducts that fluoresce upon network chain scission, and map in 3D the internal damage present after a cycle of gas saturation and rapid decompression. Interestingly, we observe that each cavity observable during decompression results in a damaged region, the shape of which reveals a fracture locus of randomly oriented penny-shape cracks (i.e., with a flower-like morphology) that contain crack arrest lines. Thus, cavity growth likely proceeds discontinuously (i.e., non-steadily) through the stable and unstable fracture of numerous 2D crack planes. This non-destructive methodology to visualize in 3D molecular damage in polymer networks is novel and serves to understand how fracture occurs under complex 3D loads, predict mechanical aging of pristine looking elastomers, and holds potential to optimize cavitation-resistance in soft materials.

Effect of urea on the hydration and aggregation of hydrophobic and amphiphilic solute models: Implications to protein aggregation.

Despite the emergence of a molecular picture of urea's protein unfolding mechanism in the past few decades, less is known about its action mechanism on protein aggregation. This is especially relevant for understanding the aggregation of amyloid proteins and peptides, implicated in several neurodegenerative diseases. While urea is believed to weaken the hydrophobic effect, a picture consistent with the decrease in the excess chemical potential of sufficiently large alkanes, interactions with protein polar side chains and backbone atoms are also important. Here, we study, through molecular dynamics, the hydration and aggregation of several alkanes and amphiphilic "mutants" of n-dodecane, in an 8M aqueous urea solution, aiming at getting insight into urea's mode of action. A size-dependent crossover temperature is found, above which the hydration of the alkanes is favored in the aqueous urea solution. The hydration of the alkanes is enhanced via entropy, with the enthalpy opposing hydration, consistent with experiments. The reason is that although solute-solvent interactions are favorable, these are overwhelmed by urea-water and urea-urea interactions. In contrast, water-water interactions and entropy are favored by a water depletion around the solute and a reduced water depletion around methane explains its exceptional solubility decrease. Furthermore, we show that while urea favors the hydration of n-dodecane and the amphiphilic mutants, it slightly enhances and reduces, respectively, the aggregation of the alkanes and the amphiphilic mutants. Thus, opposite to the common view, our results show that urea does not necessarily weaken hydrophobic interactions despite solvation being favored.

Multiscale Analysis of Solar Loading Thermographic Signals for Wall Structure Inspection.

Infrared thermography has been widely adopted in many applications for material structure inspection, where data analysis methods are often implemented to elaborate raw thermal data and to characterize material structural properties. Herein, a multiscale thermographic data analysis framework is proposed and applied to building structure inspection. In detail, thermograms are first collected by conducting solar loading thermography, which are then decomposed into several intrinsic mode functions under different spatial scales by multidimensional ensemble empirical mode decomposition. At each scale, principal component analysis (PCA) is implemented for feature extraction. By visualizing the loading vectors of PCA, the important building structures are highlighted. Compared with principal component thermography that applies PCA directly to raw thermal data, the proposed multiscale analysis method is able to zoom in on different types of structural features.

Data Enhancement via Low-Rank Matrix Reconstruction in Pulsed Thermography for Carbon-Fibre-Reinforced Polymers.

Pulsed thermography is a commonly used non-destructive testing method and is increasingly studied for the assessment of advanced materials such as carbon fibre-reinforced polymer (CFRP). Different processing approaches are proposed to detect and characterize anomalies that may be generated in structures during the manufacturing cycle or service period. In this study, matrix decomposition using Robust PCA via Inexact-ALM is investigated as a pre- and post-processing approach in combination with state-of-the-art approaches (i.e., PCT, PPT and PLST) on pulsed thermography thermal data. An academic sample with several artificial defects of different types, i.e., flat-bottom-holes (FBH), pull-outs (PO) and Teflon inserts (TEF), was employed to assess and compare defect detection and segmentation capabilities of different processing approaches. For this purpose, the contrast-to-noise ratio (CNR) and similarity coefficient were used as quantitative metrics. The results show a clear improvement in CNR when Robust PCA is applied as a pre-processing technique, CNR values for FBH, PO and TEF improve up to 164%, 237% and 80%, respectively, when compared to principal component thermography (PCT), whilst the CNR improvement with respect to pulsed phase thermography (PPT) was 77%, 101% and 289%, respectively. In the case of partial least squares thermography, Robust PCA results improved not only only when used as a pre-processing technique but also when used as a post-processing technique; however, this improvement is higher for FBHs and POs after pre-processing. Pre-processing increases CNR scores for FBHs and POs with a ratio from 0.43% to 115.88% and from 13.48% to 216.63%, respectively. Similarly, post-processing enhances the FBHs and POs results with a ratio between 9.62% and 296.9% and 16.98% to 92.6%, respectively. A low-rank matrix computed from Robust PCA as a pre-processing technique on raw data before using PCT and PPT can enhance the results of 67% of the defects. Using low-rank matrix decomposition from Robust PCA as a pre- and post-processing technique outperforms PLST results of 69% and 67% of the defects. These results clearly indicate that pre-processing pulsed thermography data by Robust PCA can elevate the defect detectability of advanced processing techniques, such as PCT, PPT and PLST, while post-processing using the same methods, in some cases, can deteriorate the results.

Evaluation and Selection of Video Stabilization Techniques for UAV-Based Active Infrared Thermography Application.

Unmanned Aerial Vehicles (UAVs) that can fly around an aircraft carrying several sensors, e.g., thermal and optical cameras, to inspect the parts of interest without removing them can have significant impact in reducing inspection time and cost. One of the main challenges in the UAV based active InfraRed Thermography (IRT) inspection is the UAV's unexpected motions. Since active thermography is mainly concerned with the analysis of thermal sequences, unexpected motions can disturb the thermal profiling and cause data misinterpretation especially for providing an automated process pipeline of such inspections. Additionally, in the scenarios where post-analysis is intended to be applied by an inspector, the UAV's unexpected motions can increase the risk of human error, data misinterpretation, and incorrect characterization of possible defects. Therefore, post-processing is required to minimize/eliminate such undesired motions using digital video stabilization techniques. There are number of video stabilization algorithms that are readily available; however, selecting the best suited one is also challenging. Therefore, this paper evaluates video stabilization algorithms to minimize/mitigate undesired UAV motion and proposes a simple method to find the best suited stabilization algorithm as a fundamental first step towards a fully operational UAV-IRT inspection system.

Low-signal limit of X-ray single particle diffractive imaging.

An outstanding question in X-ray single particle imaging experiments has been the feasibility of imaging sub 10-nm-sized biomolecules under realistic experimental conditions where very few photons are expected to be measured in a single snapshot and instrument background may be significant relative to particle scattering. While analyses of simulated data have shown that the determination of an average image should be feasible using Bayesian methods such as the EMC algorithm, this has yet to be demonstrated using experimental data containing realistic non-isotropic instrument background, sample variability and other experimental factors. In this work, we show that the orientation and phase retrieval steps work at photon counts diluted to the signal levels one expects from smaller molecules or with weaker pulses, using data from experimental measurements of 60-nm PR772 viruses. Even when the signal is reduced to a fraction as little as 1/256, the virus electron density determined using ab initio phasing is of almost the same quality as the high-signal data. However, we are still limited by the total number of patterns collected, which may soon be mitigated by the advent of high repetition-rate sources like the European XFEL and LCLS-II.

Natural rubber-SiO2 nanohybrids: interface structures and dynamics.

Homogeneous dispersion of silica nanoparticles (SiO2 NPs) in natural rubber (NR) is a key challenge for engineering high-performance nanocomposites and elucidation of their structure on a molecular basis. Towards this, the present work devised a novel route for obtaining 3D self-assembled SiO2 NP-NR nanocomposites under aqueous conditions and in the presence of Mg2+, by establishing a molecular bridge that clamped the negatively charged NR and SiO2 colloidal particles with a favoured NR-SiO2 NP hetero-aggregation. The characteristic NR-SiO2 NP hetero-aggregates displayed a decreased heat capacity with increase in the SiO2 mass-fraction, implying a restricted NR chain mobility. Such changes in the interfacial layers were tapped by 29Si NMR, DFT calculations and molecular dynamics simulations towards a mechanistic understanding of the structure and dynamics of the NR/SiO2 NP hybrid. Simple models were used to illustrate basic ideas; specific electrostatic interactions such as ion-dipole and H-bonding interactions proved to be the driving forces for the organized assembly leading to the NR-SiO2 hetero-aggregate over the NR-NR or SiO2 NP-SiO2 NP homo-aggregate. Molecular dynamics simulation of the aqueous canonical ensemble of the hybrid showed the stable molecular conformation to reveal a SiO2 NP spherical core encapsulated by a hydrophobically interconnected NR polymer layer as the outer shell, as a unique structural model. Specifically, the lipid end of the NR was involved electrostatically while the lysine end (the protein part of NR) H-bonded to the core silica cluster thereby restricting random aggregation. The calculated negative free energy changes for the hetero-aggregate composites via their vibrational and rotational spectra proved the spontaneity of composite formation.

Flaw-Insensitive Hydrogels under Static and Cyclic Loads.

New applications of hydrogels draw growing attention to the development of tough hydrogels. Most tough hydrogels are designed through incorporating large energy dissipation from breaking sacrificial bonds. However, these hydrogels still fracture under prolonged cyclic loads with the presence of even small flaws. This paper presents a principle of flaw-insensitive hydrogels under both static and cyclic loads. The design aligns the polymer chains in a hydrogel at the molecular level to deflect a crack. To demonstrate this principle, a hydrogel of polyacrylamide and polyvinyl alcohol is prepared with aligned crystalline domains. When the hydrogel is stretched in the direction of alignment, an initial flaw deflects, propagates along the loading direction, peels off the material, and leaves the hydrogel flawless again. The hydrogel is insensitive to pre-existing flaws, even under more than ten thousand loading cycles. The critical degree of anisotropy to achieve crack deflection is quantified by experiments and fracture mechanics. The principle can be generalized to other hydrogel systems.

Fatigue fracture of nearly elastic hydrogels.

Polyacrylamide hydrogels are highly stretchable and nearly elastic. Their stress-stretch curves exhibit small hysteresis, and change negligibly after many loading cycles. Polyacrylamide is used extensively in applications, and is the primary network for many types of tough hydrogels. Recent experiments have shown that polyacrylamide hydrogels are susceptible to fatigue fracture, but available data are limited. Here we study fatigue fracture of polyacrylamide hydrogels of various water contents. We form polymer networks in all samples under the same conditions, and then obtain hydrogels of 96, 87, 78, and 69 wt% of water by solvent exchange. We measure the crack extension under cyclic loads, and the fracture energy under monotonic loading. For the hydrogels of the four water contents, the fatigue thresholds are 4.3, 8.4, 20.5, and 64.5 J m-2, and the fracture energies are 18.9, 71.2, 289, and 611 J m-2. The measured thresholds agree well with the predictions of the Lake-Thomas model for hydrogels of high water content, but not in the case of low water content. It is hoped that further basic studies will soon follow to aid the development of fatigue-resistant hydrogels.