Thermal stability and dynamics of soft nanoparticle membranes: role of entropy, enthalpy and membrane compressibility

Nanoparticle based ultra-thin membranes have been shown to have remarkable mechanical properties while also possessing novel electrical, optical or magnetic properties, which could be controlled by tailoring properties at the level of individual nanoparticles. Since in most cases the ultra-thin membranes are coupled to some substrates, the role of membrane–substrate interactions, apart from nanoparticle–nanoparticle interactions become very crucial in understanding their mechanical and thermal stability, as well as their plethora of applications. However, systematic studies in this direction have been conspicuously absent. Here we report thermal stability and the corresponding microscopic dynamics of polymer supported ultra-thin membranes comprising of self-assembled, ordered grains of polymer grafted nanoparticles having tunable mechanical properties. The initially ordered membranes show distinct pathways for temperature induced disordering depending on membrane flexibility as well as on interfacial entropic and enthalpic interactions with the underlying polymer thin film. We also observe contrasting temperature dependence of microscopic dynamics of these membranes depending on whether the graft polymer–substrate polymer interactions are predominantly entropic or enthalpic in nature. Our results suggest that apart from their varied applications, the soft nanoparticle–polymer hybrid membranes are a playground for rich physics involving subtle entropic and enthalpic effects along with the nanoparticles softness, which eventually determine their thermo-mechanical stability

Optical spectral surveillance of breast tissue landscapes for detection of residual disease in breast tumor margins.

We demonstrate a strategy to "sense" the micro-morphology of a breast tumor margin over a wide field of view by creating quantitative hyperspectral maps of the tissue optical properties (absorption and scattering), where each voxel can be deconstructed to provide information on the underlying histology. Information about the underlying tissue histology is encoded in the quantitative spectral information (in the visible wavelength range), and residual carcinoma is detected as a shift in the histological landscape to one with less fat and higher glandular content. To demonstrate this strategy, fully intact, fresh lumpectomy specimens (n = 88) from 70 patients were imaged intra-operatively. The ability of spectral imaging to sense changes in histology over large imaging areas was determined using inter-patient mammographic breast density (MBD) variation in cancer-free tissues as a model system. We discovered that increased MBD was associated with higher baseline β-carotene concentrations (p = 0.066) and higher scattering coefficients (p = 0.007) as measured by spectral imaging, and a trend toward decreased adipocyte size and increased adipocyte density as measured by histological examination in BMI-matched patients. The ability of spectral imaging to detect cancer intra-operatively was demonstrated when MBD-specific breast characteristics were considered. Specifically, the ratio of β-carotene concentration to the light scattering coefficient can report on the relative amount of fat to glandular density at the tissue surface to determine positive margin status, when baseline differences in these parameters between patients with low and high MBD are taken into account by the appropriate selection of threshold values. When MBD was included as a variable a priori, the device was estimated to have a sensitivity of 74% and a specificity of 86% in detecting close or positive margins, regardless of tumor type. Superior performance was demonstrated in high MBD tissue, a population that typically has a higher percentage of involved margins

Effects of time on <i>ex vivo</i> spectral imaging.

<p>50x bicubic interpolated images of a negative and positive margin from different patients, where the “initial” images of the margins were imaged at approximately the same time points post-excision. The “initial” images represent the actual data measured. The median percent change at 10, 20, and 30 minutes was applied to either the negative or positive image to show how an image would change if measured at various time points beyond the “initial” image time point. A) For β-carotene, THb, and THb/<µ_s’>, the negative margins have higher values and the kinetics decrease over time. Therefore, the percent change is applied to the negative margin to show decreasing contrast (worst case scenario). B) For β-carotene/<µ_s’> the negative margins have higher values and the kinetics increase over time. C) For <µ_s’> the positive margins have higher values but the kinetics decrease over time.</p