ABSTRACT
A conventional approach to making miniature or microscale gas chromatography (GC) components relies on silicon as a base material and MEMS fabrication as manufacturing processes. However, these devices often fail in medium-to-high temperature applications due to a lack of robust fluidic interconnects and a high-yield bonding process. This paper explores the feasibility of using metal additive manufacturing (AM), which is also known as metal 3D printing, as an alternative platform to produce small-scale microfluidic devices that can operate at a temperature higher than that which polymers can withstand. Binder jet printing (BJP), one of the metal AM processes, was utilized to make stainless steel (SS) preconcentrators (PCs) with submillimeter internal features. PCs can increase the concentration of gaseous analytes or serve as an inline injector for GC or gas sensor applications. Normally, parts printed by BJP are highly porous and thus often infiltrated with low melting point metal. By adding to SS316 powder sintering additives such as boron nitride (BN), which reduces the liquidus line temperature, we produce near full-density SS PCs at sintering temperatures much lower than the SS melting temperature, and importantly without any measurable shape distortion. Conversely, the SS PC without BN remains porous after the sintering process and unsuitable for fluidic applications. Since the SS parts, unlike Si, are compatible with machining, they can be modified to work with commercial compression fitting. The PC structures as well as the connection with the fitting are leak-free with relatively high operating pressures. A flexible membrane heater along with a resistance-temperature detector is integrated with the SS PCs for thermal desorption. The proof-of-concept experiment demonstrates that the SS PC can preconcentrate and inject 0.6% headspace toluene to enhance the detector's response.
ABSTRACT
Estimating many parameters of biomechanical systems with limited data may achieve good fit but may also increase 95% confidence intervals in parameter estimates. This results in poor identifiability in the estimation problem. Therefore, we propose a novel method to select sensitive biomechanical model parameters that should be estimated, while fixing the remaining parameters to values obtained from preliminary estimation. Our method relies on identifying the parameters to which the measurement output is most sensitive. The proposed method is based on the Fisher information matrix (FIM). It was compared against the nonlinear least absolute shrinkage and selection operator (LASSO) method to guide modelers on the pros and cons of our FIM method. We present an application identifying a biomechanical parametric model of a head position-tracking task for ten human subjects. Using measured data, our method (1) reduced model complexity by only requiring five out of twelve parameters to be estimated, (2) significantly reduced parameter 95% confidence intervals by up to 89% of the original confidence interval, (3) maintained goodness of fit measured by variance accounted for (VAF) at 82%, (4) reduced computation time, where our FIM method was 164 times faster than the LASSO method, and (5) selected similar sensitive parameters to the LASSO method, where three out of five selected sensitive parameters were shared by FIM and LASSO methods.
