Applications of Cavity-Enhanced Direct Frequency Comb Spectroscopy Kevin Cossel Ye Group JILA/University of Colorado-Boulder OSU Symposium on Molecular Spectroscopy 2010

What is CE-DFCS? The 3 building blocks of Cavity-Enhanced Direct Frequency Comb Spectroscopy: 1 Mode-locked laser (frequency comb) 2 High-finesse enhancement cavity 3 Dispersive detection system M. J. Thorpe and J. Ye, Appl. Phys B 91, 397 (2008)

Benefits of frequency combs from S. T. Cundiff, J. Ye, and J. L. Hall, Scientific American, Apr 2008 Single ultrashort pulse Train of pulses Frequency combs provide narrow lines over a wide spectral bandwidth: High resolution Broad bandwidth Rapid acquisition Spatially coherent Multi-species detection with high sensitivity in near real time

Cavity-comb coupling Frequency Domain Frequency comb Cavity modes Time Domain Jones & Ye, Opt. Lett. 27, 1848 (2002). Mode-locked laser Cavity mode structure: Frequency comb structure: Thorpe et al., Opt. Express. 13, 882 (2005). Adler et al., Annu. Rev. Anal. Chem. 3, 175 (2010).

I. Trace contaminant detection

Trace gas detection in arsine Experimental setup: 250-MHz-Er:fiber laser with highly nonlinear fiber ( ≈ µm) cavity with peak Finesse of spanning µm arsine extremely toxic  set up in specialty lab at NIST (Optoelectronics Division, K. Bertness) Mirror data Laser spectrum

VIPA FSR 2D Spectrometer Mode-locked laser VIPA spectrometer High finesse optical cavity with intra-cavity gas sample >3000 channels simultaneously (typically 25 nm bandwidth) ~1 GHz resolution S. A. Diddams et al., Nature 445, 627 (2007) M. J. Thorpe et al., Opt. Express 16, 2387 (2008)

Arsine Results I Coverage from µm ( cm -1 ) Absorption sensitivity of 1  cm -1 Hz -1/2 in nitrogen over 3000 simultaneous channels Measurement of H 2 O, CH 4, CO 2, H 2 S in nitrogen with minimum detectable concentrations from 7 ppb to 700 ppb

Arsine Results II Detection level for water in arsine of 31 ppb K.C. Cossel et al., Appl Phys B, in press (2010).

II. Breath Analysis

Application: breath analysis Medical research has (maybe) identified many molecules as markers for certain diseases in breath. Our focus: lung cancer & COPD Collaborators: CU Medical School (O. Reiss, J. Repine) CU Cancer Center (N. Peled) Samples: from cell cultures, rats, and humans What are the challenges? many molecules present in breath samples complex molecules have “messy” spectra recognize molecule spectra bottom line: Can we definitely link certain molecules to cancer?

Application: breath analysis Develop CE-DFCS system in the mid-IR Why use comb spectroscopy? simultaneous detection of multiple molecule species (generate marker pattern!) high sensitivity (fundamental mid-IR band!) fast acquisition (compared to GC-MS) high resolution (separate mixtures) First test with NIR comb: M. J. Thorpe et al., Opt. Express 16, 2387 (2008)

III. Atmospheric Chemistry

Important atmospheric measurements Isotope ratios ( 13 C, 18 O) Greenhouse gases (CH 4, CO 2, N 2 O) Pollutants (formaldehyde, benzene, acetone, NOx, nitric acid, etc.) Primary organics (e.g., isoprene) lead to aerosol formation Need fast acquisition over a broad bandwidth with high resolution in the mid-IR

Mid-infrared OPO Power and efficiencySpectral tunability Fan-out PPLN crystal; 10 W Yb:fiber pump more than 1 W over 1 µm tuning range continuous tunability from 2.8 to 4.8 µm (0.3 µm bandwidth)

Fourier Transform Spectrometer 22 bit, 1 MS/s digitization 160 MHz resolution 10 s sweep time Broad spectral acquisition  Frequency Comb Enhancement cavity or multipass cell High spectral brightness = short averaging time

Mid-IR Results I AtmosphericAtmospheric/Breath Breath Atmospheric/Breath

Mid-IR Results II Atmospheric Multiline detection advantage ~30 scan time 3.8×10 -8 cm -1 Hz -1/2 per 45,000 spectral elements Detection limits: H 2 CO (40 ppb), CH 4 (5 ppb), Isoprene (7 ppb), CO 2 (< 1 ppb), CH 3 OH (350 ppb single line or 40 ppb), …

Complex Mixture Analysis

Summary CE-DFCS provides a unique combination of broad bandwidth, high resolution, high sensitivity and rapid data acquisition Detection of many species and complex mixtures in near real time Collaborations: Scott Diddams (NIST) Martin Fermann (IMRA) Ingmar Hartl (IMRA) Axel Ruehl (IMRA) Ronald Holzwarth (Menlo) Kris Bertness (NIST - Arsine) Jun Feng (Matheson - Arsine) Mark Raynor (Matheson -Arsine) Miao Zhu (Agilent) CE-DFCS: Mike Thorpe Florian Adler Piotr Maslowski Aleksandra Foltynowicz Travis Briles Kevin Moll David Balslev-Clausen Matt Kirchner Funding: NSF, AFOSR, NIST, DARPA, DTRA, Agilent