This manuscript demonstrates a mechanically robust implantable nanofluidic membrane capable of tunable long-term zero-order release of therapeutic agents in ranges relevant for clinical applications. The membrane, with nanochannels as small as 5 nm, allows for the independent control of both dosage and mechanical strength through the integration of high-density short nanochannels parallel to the membrane surface with perpendicular micro- and macrochannels for interfacing with the ambient solutions. These nanofluidic membranes are created using precision silicon fabrication techniques on silicon-on-insulator substrates enabling exquisite control over the monodispersed nanochannel dimensions and surface roughness. Zero-order release of analytes is achieved by exploiting molecule to surface interactions which dominate diffusive transport when fluids are confined to the nanoscale. In this study we investigate the nanofluidic membrane performance using custom diffusion and gas testing apparatuses to quantify molecular release rate and process uniformity as well as mechanical strength using a gas based burst test. The kinetics of the constrained zero-order release is probed with molecules presenting a range of sizes, charge states, and structural conformations. Finally, an optimal ratio of the molecular hydrodynamic diameter to the nanochannel dimension is determined to assure zero-order release for each tested molecule.
Nanoparticles and their derivatives have engendered significant recent interest. Despite considerable advances in nanofluidic physics, control over nanoparticle diffusive transport, requisite for a host of innovative applications, has yet to be demonstrated. In this study, we performed diffusion experiments for negatively and positively charged fullerene derivatives (dendritic fullerene-1, DF-1, and amino fullerene, AC60) in 5.7 and 13 nm silicon nanochannels in solutions with different ionic strengths. With DF-1, we demonstrated a gated diffusion whereby precise and reproducible control of the dynamics of the release profile was achieved by tuning the gradient of the ionic strength within the nanochannels. With AC60, we observed a near-surface diffusive transport that produced release rates that were independent of the size of the nanochannels within the range of our experiments. Finally, through theoretical analysis we were able to elucidate the relative importance of physical nanoconfinement, electrostatic interactions, and ionic strength heterogeneity with respect to these gated and near-surface diffusive transport phenomena. These results are significant for multiple applications, including the controlled administration of targeted nanovectors for therapeutics.
Our membranes potentially represent a viable nanotechnological approach for the controlled administration of chemotherapeutics intended to improve the therapeutic efficacy of treatment and reduce many of the side effects associated with conventional drug administration.
The novelty of three-dimensional visualization technology (3DVT), such as virtual reality (VR), has captured the interest of many educational institutions. This study's objectives were to (1) assess how VR and physical models impact anatomy learning, (2) determine the effect of visuospatial ability on anatomy learning from VR and physical models, and (3) evaluate the impact of a VR familiarization phase on learning. This within-subjects, crossover study recruited 78 undergraduate students who studied anatomical structures at both physical and VR models and were tested on their knowledge immediately and 48 hours after learning. There were no significant differences in test scores between the two modalities on both testing days. After grouping participants on visuospatial ability, low visuospatial ability learners performed significantly worse on anatomy knowledge tests compared to their high visuospatial ability counterparts when learning from VR immediately (P = 0.001, d = 1.515) and over the long-term (P = 0.003, d = 1.279). In contrast, both low and high visuospatial ability groups performed similarly well when learning from the physical model and tested immediately after learning (P = 0.067) and over the long-term (P = 0.107). These results differ from current literature which indicates that learners with low visuospatial ability are aided by 3DVT. Familiarizing participants with VR before the learning phase had no impact on learning (P = 0.967). This study demonstrated that VR may be detrimental to low visuospatial ability students, whereas physical models may allow all students, regardless of their visuospatial abilities, to learn similarly well. Anat Sci Educ 14: 788-798.
The lack of a viable theory for describing diffusivity when fluids are confined at the micro- and nanoscale [Ladero et al. Chem. Eng. Sci.2007, 62, 666-678; Deen AIChE J.1987, 33, 1409-1425] has necessitated accurate measurement of diffusivity (D) [Jin and Chen Chromatographia2000, 52, 17-21; Nie et al. Science1994, 266, 1018-1021; Durand et al. Anal. Chem.2009, 81, 5407-5412], crucial for a host of micro- and nanofluidic technologies [Grattoni et al. Curr. Pharm. Biotechnol.2010, 11, 343-365]. We demonstrate a rapid and agile method for the direct measurement of diffusivity in a system possessing 10(4) to 10(5) precisely fabricated channels with characteristic sizes (β) ranging from micro- to nanometers. Custom chambers allowed us to measure the diffusivity in a closed unperturbed system using UV/vis spectroscopy. D was measured for rhodamine B (RhoB) in aqueous solution in channels of 200 and 1 μm, as well as 13 and 5.7 nm. The observed logarithmic scaling of diffusivity with β, in close agreement with prior experiments, but far from theoretical prediction, surprisingly highlights that diffusivity is significantly altered even at the microscale. Accurate measurement of D by reducing the size of the source reservoir by 3 orders of magnitude (from 150 μL to 910 nL) proves that a substantial reduction in measurement time (from 7 days to 40 min) can be achieved. Our design thus is ready for rapid translation into a standard analytical tool--useful for multiple applications.
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