MEMS and ECF Volume Sensing

Critical to the success of an automated dialysis platform is on-line real-time estimation of ECF volume, a complex engineering problem in its own right. Esopha-geal Doppler monitoring (EDM), pulmonary-artery catheters, and peripheral waveform analysis all provide measures of central hemodynamic parameters. Bioimped-ance and hematocrit monitoring may provide estimates

Fig. 1. Scanning electron micrograph of a silicon nanopore membrane. Scale bar = 30 ^m. The higher-power inset shows a single pore extending into the plane of the membrane surface. Scale bar = 1 ^m.

of changes in blood volume. Various volume estimation techniques have been deployed in outpatient maintenance hemodialysis [15]. Blood volume monitors have yielded mixed results in clinical trials. An implanted right ventricular pressure monitor appeared to provide valuable clinical information over and above clinical judgment in a very small series [16].

MEMS has traditionally referred to miniature components integrating sensors, actuators, and electronics [17]. These devices are produced using many of the same microfabrication techniques as those used to manufacture integrated circuits on silicon substrates. This manufacturing strategy enables the mass production of miniature, high performance, mechanical, fluidic, and optical components that can be integrated with electronics at low unit cost.

The telemetric application of MEMS sensor technology to hemodialytic systems has two component requirements: (1) sensing and (2) data transmission. If no internal power source is to be used, external powering of the system becomes a requirement, as well. This can be accomplished via inductive coupling, using radiofrequency transmission from an external source, the charging of a capacitor located on the implanted MEMS chip, and the releasing of the energy to power the MEMS chip and circuit in order to transmit sensed data to an external receiving source. An alternate, and even simpler, scheme is suitable for capacitive MEMS sensors, where changes in the sensing parameter can be translated into capacitance variations of the MEMS sensor. In such cases, the MEMS sensor can be configured into a passive tank circuit that

Fig. 2. Hydraulic permeability of silicon nanopore membranes (SNMs). Hydraulic permeability per pore (Kuf, y axis) to phosphate-buffered saline (PBS, d) and deionized water (DI water, X) for SNMs with pores 8-100 nm in small dimension (x axis). Multiply by 1.33 X 109 for ml/ min/mm Hg/m2.

1.0E-07

1.0E-08

1.0E-12

Critical pore dimension (nm) 20 40 60 80

Critical pore dimension (nm) 20 40 60 80

1.0E-08

O DI water ▲ PBS

Fig. 3. Rejection of proteins by 42-nm pore. Apparent sieving coefficients for carbonic anhydrase (25 kD), bovine serum albumin (66 kD), and sweet potato amylase (200 kD) in 150 and 1,500 mM PBS. The high-ionic strength solution minimizes electrostatic shielding between the molecule and the much larger pore.

■ 10X PBS • 1 X PBS

Molecular weight (kD)

50 100 150

Molecular weight (kD)

is comprised of the variable capacitor and a fixed inductor. This tank circuit exhibits a characteristic resonant frequency that varies as the capacitance changes. An external probe can be used to detect the resonant frequency of the implanted sensor without the use of any circuit in the implanted chip. Millimeter-scale continuous-reading wireless probes have been demonstrated for use within deep tissues, and could be adapted for continuous monitoring of hemodynamic parameters, permitting closed-loop cardiovascular feedback for autonomous hemofil-tration systems [18].

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