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Chemical Power for Microscopic Robots in Capillaries. Tad Hogg Institute for Molecular Manufacturing. with Robert A. Freitas Jr. preprint: http://arxiv.org/abs/0906.5022. Sensors for Medicine and Science (SMSI) implantable glucose monitor. Given Imaging’s PillCam photos inside GI tract.

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chemical power for microscopic robots in capillaries

Chemical Power for Microscopic Robots in Capillaries

Tad Hogg

Institute for Molecular Manufacturing

with Robert A. Freitas Jr.

preprint: http://arxiv.org/abs/0906.5022

microscopic robots for medicine

Sensors for Medicine and Science (SMSI) implantable glucose monitor

Given Imaging’s PillCam

photos inside GI tract

Aarhus University

targeting drugs to cells

microscopic robots for medicine
  • extending today’s implanted devices
    • to much smaller sizes
  • enhancing today’s in vivo nanoparticles
    • with computation & communication
challenges for microscopic robots
challenges formicroscopic robots
  • fabricate
  • control
  • power
  • identify applications
power
power
  • short operation:
    • fuel inside robots
  • extended operation:
    • power from environment, e.g.,
      • acoustics (ultrasound)
      • chemical fuels from environment

see R. Freitas, Nanomedicine Vol. I, chap. 6, 1999

example glucose oxygen
example: glucose + oxygen

C6H12O6 + 6O2 6CO2 + 6H2O

energy released: 4x10-18 Joule per reaction

e.g,:

react a million O2/second = about a pico watt

slide6

How much power can

glucose + oxygen

provide?

slide7

Is power generation

safe for

nearby tissue?

method
method
  • pick medical application context
  • identify key constraints on power
  • evaluate quantitatively
application robots in bloodstream
application: robots in bloodstream
  • useful for diagnosis and treatment
    • devices pass near cells throughout body
  • examples
    • passive motion with fluid
    • active motion
    • self-assembled groups on vessel wall
      • for long term monitoring
glucose and oxygen in blood
glucose and oxygen in blood
  • blood plasma
    • glucose concentration ~100x that of oxygen
    • hence: oxygen is rate-limiting chemical
  • most oxygen carried in red cells
    • bound to hemoglobin
    • ~100x higher concentration than in plasma
chemical power in capillaries
chemical power in capillaries
  • modeling chemical transport
  • results
    • robot power
    • effect on surrounding tissue
modeling challenges
modeling challenges
  • complex geometry
  • distorting cells
  • kinetics of oxygen release from cells
geometry
geometry
  • simplify to axial symmetric
    • vessel, robots and tissue
krogh cylinder model
Krogh cylinder model

tissue cylinder

capillary

venous end

flow

arterial end

A. Krogh, J. of Physiology 52:409 (1919)

numerical model domain
numerical model domain

100μm

capillary

radius 4 μm

40 μm

model a portion of ~1mm capillary length

robots inside vessel
robots inside vessel

10μm

8μm

static robot scenario:

robots attached to wall

fluid moves past the robots

example robot group:

each robot about 1μm3

20 robots in each ring

10 rings

group of 200 robots

modeling challenges17
modeling challenges
  • complex geometry
  • distorting cells
  • kinetics of oxygen release from cells
slide18

cells distort to go through capillaries “single file”

cell maintains volume and surface area as it distorts

typical values:

surface area 135 μm2

volume 90 μm3

approximation for cell distortion
approximation for cell distortion

group of robots

vessel wall

plasma gap

direction of flow

vessel wall

distorted cells

based on Secomb et al., American J. of Physiology: Heart and Circulatory Physiology 281:H629 (2001)

instead of modeling individual cells,

use “smeared out” average fluid with mix of cells and plasma

modeling challenges20
modeling challenges
  • complex geometry
  • distorting cells
  • kinetics of oxygen release from cells
oxygen transport
oxygen transport

oxygen diffuses

out of cell into plasma

from plasma into tissue

O2

cell

O2

flow

diffusion: from higher to lower concentrations

hence cell still has substantial oxygen at end of vessel

cell oxygen release robots
cell oxygen release & robots
  • robots have high power densities
    • larger concentration gradients than tissue
  • cells may pass before releasing oxygen
    • model must include kinetics
cell oxygen release kinetics
cell oxygen release kinetics
  • depends on
    • concentration in surrounding plasma
    • amount of oxygen bound within cell
  • approximate model

Clark et al., Biophysical Journal 47:171 (1985)

numerical model
numerical model
  • fluid flow
  • oxygen transport
    • from cells
    • diffusion in plasma and tissue
  • power generation
    • in robots and tissue
      • Michaelis-Menten kinetics
  • heating
    • conduction and convection
chemical power in capillaries25
chemical power in capillaries
  • modeling chemical transport
  • results
    • robot power
    • effect on surrounding tissue
scenarios
scenarios
  • low demand
    • resting tissue
    • slow flow
      • avg. speed 0.2mm/s
  • high demand
    • active tissue
    • fast flow
      • avg. speed 1mm/s

typical situation for nanomedicine

slide27

oxygen transport

to robots and tissue

slide28

section through vessel and tissue

tissue

vessel wall

flow

streamlines

of fluid flow

tissue

distance (μm)

slide30

no robots

(1022 molecule/m3 )

flow

distance (μm)

slide31

10-micron group

(1022 molecule/m3 )

flow

distance (μm)

slide33

no robots

(1022 molecule/m3 )

flow

distance (μm)

slide34

10-micron group

(1022 molecule/m3 )

flow

distance (μm)

power comparisons
power comparisons
  • robots ~10s of pW
  • cells use 10-1000pW
    • cell size ~10mm
      • i.e., ~103 x robot volume
  • person at rest uses ~100 watts
what can a picowatt do
What can a picowatt do?
  • compute: ~105 logic operations/sec
    • near-term molecular electronics
      • 103 kT/operation
  • communicate: ~104 bits/sec over 100mm
    • with ultrasound
  • move: ~1mm/sec through water
    • overcoming viscous drag

see R. Freitas, Nanomedicine Vol. I, chap. 6, 1999

www.nanomedicine.com

effects of robots
effects of robots
  • less oxygen for tissues
  • local heating
  • waste products
  • forces on vessel wall
oxygen for tissue
oxygen for tissue
  • robots
    • compete with tissue for oxygen
    • block diffusion out of vessel
  • is this a problem for nearby tissue?
slide41

(1022 molecule/m3 )

examine tissue power next to vessel wall

flow

distance (μm)

power in tissue next to vessel

power drop

~ 10%

power in tissue next to vessel

flow

compare

no robots

10-micron group

power decrease much less than oxygen concentration decrease

due to Michaelis-Menten kinetics parameters in tissue

(oxygen must get very low to cause significant power decrease)

heating
heating
  • robots have high power density

~107 watt/m3

  • tissue cells

~104 watt/m3

  • possible significant local heating
temperature increase by robots
temperature increase by robots

high demand

10-4 degrees C

flow

heating not significant
heating not significant
  • about 10-4 degree C
  • for a group of 100s of robots
  • heating would be significant if:
    • much larger groups
    • many groups in nearby capillaries
summary
summary
  • ~10s of pW per robot on capillary walls
  • for group of ~100s of robots
    • oxygen: mainly from passing cells
    • small reduction in tissue power
      • especially for low demand scenario (resting tissue)
    • insignificant local heating
      • in spite of high power density
slide47

additional

biology

questions

effects on robots and surrounding tissue

local effects
local effects
  • white blood cells
    • block oxygen transport as they pass
    • additional forces on robots
  • other functions of blood
    • e.g., immune response, clotting
  • response of cells in vessel wall to
    • forces from robots clinging to wall
    • blocked chemical transport
long range effects
long-range effects
  • lower cell oxygen downstream of robots
  • response to partially blocked vessel
    • increase pressure?
  • time scale of response
  • systemic effects of many robot groups
further info
further info
  • T. Hogg, Designing Microscopic Robots for Medical Diagnosis and Treatment,
  • Nanotechnology Perceptions3:63-73 (2007)
  • preprint: http://arxiv.org/abs/0906.5022
  • R. Freitas Jr.,www.nanomedicine.com