1. Vapor Sensors Using Olfactory Proteins Coupled to Carbon Nanotubes
Mitchell Lerner, Brett Goldsmith, Joe Mitala, Bohdana Discher, Paul Rhodes, A.T. Charlie Johnson University of Pennsylvania APS March 2010

2. Types of Olfactory Receptor Cells
Biological Olfaction
Mammal
Types of Olfactory Receptor Cells
Human
350
Mouse
1000
Rat
1500
Dog
1100
G-coupled protein receptor- transmembrane protein that senses molecules outside the olfactory receptor cell and activates signal transduction pathways inside the cell (eventually cell response).
1) Odorant molecules bind to olfactory receptors.
2) Olfactory receptor cells generate electric signals.
3) Signals from each type of cell converge on a glomerulus, which forwards the collective signal to the brain for interpretation.
Key point: There are hundreds of different types of olfactory receptor cells with thousands of copies of each. Each type of cell produces an electric signal for a variety of odorants and each odorant may bind to a variety of receptor cells, so the signal to the brain from the glomeruli represents a chemical signature of these hundreds of responses which is unique to the particular odorant. A slightly varied response from only a few glomeruli can be distinguished as a different smell. Get number of discernable smells >> number of types of cells
Number of discernable smells is much greater than the number of types of olfactory receptor cells
(350 sensor types detect >10,000 odors for humans)

3. Experimental Objective
Integrate biology with nanotechnology
Simulate biological olfaction by combining biological elements with nanoelectronics
Attach olfactory receptor protein in a membrane to an electronic readout
Biomimicry?

4. Nanotube Transistors
Use standard techniques to construct state of the art CNT transistors
CVD Nanotube growth on Si/SiO2 wafer
Photolithography and gold evaporation for the metal contacts
Silicon back-gate
Transistor- device that can be controlled to allow/disallow current to flow by modulating the local electric field. Can be done in a number of ways: chemically with an ionic solution, changing surface potential with a top gate, applying a voltage to the silicon as a back gate.
Curve is a plot of current vs gate voltage. Apply 100 mV bias voltage to the source electrode, ramp the gate voltage up and down. Channel for charge carriers is created between the source and the drain once the gate voltage decreases through a threshold value and current can flow. Above the threshold, the channel disappears and source-drain current falls to zero. Important part: Transistor can be turned ON/OFF.
Mention the hysteresis coming from charge traps in SiO2 substrate. Not relevant to the discussion.

5. Nanotube Functionalization
Olfactory Receptor Proteins
Genetically engineered with His-tag
Lyse cell, put CMF in digitonin solution
Purify in Ni column
Nanotube- Just fly through
Diazonium salt to attach carboxyl group
EDC-NHS treatment
NTA with three Ni-binding sites
Proteins attach to NTs in Ni solution
Loosely based on process from Graff, R.A., T.M. Swanson, and M.S. Strano, Synthesis of nickel-nitrilotriacetic acid coupled single-walled carbon nanotubes for directed self-assembly with polyhistidine-tagged proteins. Chem. Mater., : p
Olfactory receptor protein in micelles
Diazonium salt
Bare Nanotube
EDC
NHS
NTA

6. Experimental Design: Attachment
Confirmation of protein attachment to nanotubes
AFM image of a nanotube. Bright spots in the background are proteins in micelles that have passively attached to the substrate. Blue arrow indicates a protein that is selectively bound to the nanotube.
1mm

7. Reproducible Sensing
blue arrow – inert gas flush
green arrow – eugenol exposure
Need to find a coherent way to analyze different data sets since current can drift over time.
MOR174-9 in digitonin micelles response to eugenol
Explain graphs!
Left graph: Measuring source-drain current over time as sensor is exposed to eugenol at 50% flow. At time = 0, eugenol vapors are introduced and the current goes up as the analyte binds to the ORs. At t= 100 sec, the flow is turned off and molecules passively detach from the ORs, current falls back to the original value. Repeat cycle three more times. Each curve is the same device on subsequent runs. See how the baseline can drift? Need to quantitatively compare these measurements by normalizing the data.
Right graph: These are the same data but now we are considering the percent change in current over time. Found by dividing the raw current value by the baseline (value if there was no sensing response, value it will return to). Taking this into account we see that all three curves collapse onto roughly the same plot, showing that eugenol induces a positive response of ~12% increase from baseline current.
curves from 3 experiments showing a reproducible response to eugenol for mOR174-9
mOR responds to eugenol in vivo

8. Sensor Timing
experiment showing a response to cyclohexanone for mOR
mOR responds to cyclohexanone in vivo
Initial response k-1 3s, secondary response is 30s
Recovery k-1 is 30s.
Contrast with other data: Full time resolution, 200 usec =5 kHz data. Other data was taken on a different ammeter at 1 Hz.
Left plot is taken at the introduction of cyclohexane to MOR Notice current falls initally, so this OR has a negative response to cyclohexane.
Right plot starts on turning off flow of cyclohexane, current returns to baseline value.
Been shown that biological systems show a fast response and slow response as a newly introduced analyte activley binds to olfactory receptor proteins. Confirmed by the data needing to be fit by sum of two exponentials with time constants ~3 sec and ~30 sec. There is only a slow response as analyte flow is turned off and molecules passively detach from the ORs. Right graph is fit with a single exponential with a time constant ~30 sec.

9. Concentration Dependence
Explain graph axes: % Change in Conductance comes from measuring baseline conductance (1/R = I/bias V) and conductance at peak amplitude of sensing response and calculating the percent change. Similar measurement to current in Current vs. Time plot before.
Vapor pressure is pressure of a vapor in equilibrium with its condensed phases in a closed container. Analyte sits in a bubbler at its vapor pressure, then gets mixed with a carrier gas. Percent saturation depends on the ratio of carrier gas to analyte vapor. If flow rates are equal, then vapor pressure is 50% saturated (half analyte vapor, half carrier). As percent saturation increases, the concentration of odorant in the gas flowed over the sensor is increasing.
Some analytes induce positive response (eugenol), some negative (amyl acetate and heptanone) or no response (methyl benzoate).
fMRI results show that the same areas of the brain are activated when exposed to amyl acetate and heptanone, indicates that mice cannot distinguish between them. Sensing responses of mOR to amyl acetate and heptanone are identical within error. Seems we have reproduced an aspect of mouse olfaction in our electronic system.
Emphasize that DNT is manmade, but mammals (dogs and rats) can detect it, even though they couldn’t have evolved this ability. Want a sensor that can learn to detect new chemicals as they are invented like organisms can. This sensor shows that ability since it can detect DNT.
DNT vs TNT
Rat vs mouse TNT sensing
Values of vapor pressure
Compare with known facts about mouse ORs
experiments showin responses for mOR 203-1
mOR responds to 2-heptanone in vivo
fMRI studies show identical responses to 2-heptanone and n-amyl acetate in mice

10. Each of the three proteins gives a different set of reproducible, concentration dependent responses
mOR
mOR 174-9
Nanodisks vs. digitonin vs. empty micelles
MOR 174-9
mOR 203-1
mOR (check pics to see which is which)
Explain that each device is functionalized with a different OR protein. Still get responses from all devices for some chemicals (like cyclohexane) and each device is sensitive to a range of chemicals. Far from one chemical- one sensor idea.
Can create a library of unique chemical signatures from the responses of several different functionalized sensors (give example). If a new chemical is introduced, the sensor will generate a signature that can be used to detect it in the future.
mOR 203-1

11. Summary
We reproduced many properties of biological sensing in an electronic device.
Proteins are maintaining some features found in vivo
First use of G-protein coupled receptors in electronic sensors
Potential for application in hazardous material detection and pharmaceutical testing
Don’t know mechanism, put an asterisk on it all
May be getting response from non-biological function
“G-coupled protein receptor”
The exact sensing mechanism cannot be determined conclusively. Prefer if everything worked as it did in an organism, but we are fundamentally altering the state of this OR protein. Could be getting a sensing response from non-specific binding of the analyte to the protein.
However, even if this is the case, the G-protein coupled receptor is very important, never made into an electronic sensor before. Potential worth exploring. It’s the target of roughly 30% of all pharmaceuticals (ref below). Can be conceivable to test new drugs by flowing them over this sensor functionalized with a particular GPCR and test for attachment.
In sum, we have demonstrated a biologically based vapor sensor derived from a G-protein coupled receptor, whose properties are consistent with several aspects of biological systems. It has numerous applications, from detecting chemicals in a clinical setting to detection of hazardous materials like explosives (TNT).
Filmore, David (2004). "It's a GPCR world". Modern Drug Discovery (American Chemical Society) 2004 (November): 24–28.

12. Acknowledgements DARPA RealNose Project Evolved Machines
Paul Rhodes, Sam Khamis, Ryan Jones
Johnson Group - Penn
A.T.Charlie Johnson, Brett Goldsmith, Mitchell Lerner, Ye Lu
Discher Group - Penn
Bohdana Discher, Joe Mitala
Brand Group - Monell
Joe Brand, Jesusa Josue, Alan Gelperin
Sliger Group - UIUC
Stephen Sliger, Tim Baybert
Luetje Group - UMiami
Chuck Luetje
Thanks for your attention!