1

Final Report to BSEE: Distributed Chemical 

Sensing for Sub-surface Oil Spill Sensing

Work Performed Under Contract: E14PC00016

Submitted by:

Wei-Chuan Shih, Ph.D.

University of Houston

4800 Calhoun Rd.

Houston TX 77204

(713) 743-4454

wshih@uh.edu

Submitted to:

Bureau of Safety and Environmental Enforcement 

(BSEE)

Oil Spill Response Research Branch

May 21, 2017
2

Acknowledgements

This study was funded by the Bureau of Safety and Environmental Enforcement (BSEE), U.S.

Department of the Interior, Washington, D.C., under Contract E14PC00016.

Disclaimer

This final report has been reviewed by the BSEE and approved for publication. Approval does

not signify that the contents necessarily reflect the views and policies of the BSEE, nor does

mention of the trade names or commercial products constitute endorsement or recommendation

for use.
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Table of Contents

Executive Summary 4

1 Introduction 5

2 Background and Significance 5

3 Research Objectives 5

4 Results and Discussion 5

5 Conclusions 18

6 Recommendations for Future Work 18

7 References 19
4

Executive Summary

In sub-surface offshore oil spills, sensing innovation is urgently needed to replace current 

pipeline monitoring, which is indirect and prone to misses and false alarms. In this project, we 

have investigated the capability of chemical sensing by integrating plasmonic nanostructures, 

multi-modal spectroscopic sensing, and optical fiber technology, with the ultimate goal toward 

real-time leak quantification. The novel sensor would perform 24/7, night & day, distributed, 

and automated operations. Further, since such a low-cost system requires minimal power and 

maintenance, it has the potential to be deployed on multiple platforms to form a cross-platform 

network for spill trajectory monitoring and environmental forensics. 

We have developed a novel plasmonic nanostructures, nanoporous gold disks (NPGD), for multimodal spectroscopic sensing together with optical fiber technology. NPGD possess large specific 

surface area due to their extensive nanoporous network. NPGD also contain numerous plasmonic 

hot-spots throughout the porous network. For selective molecular binding of hydrocarbons, we 

have developed a compatible aryl-bisthiolate functional surface. Based on surface-enhanced 

Raman scattering (SERS) and localized surface plasmon resonance (LSPR), we have 

demonstrated the detection and quantification of various polycyclic aromatic hydrocarbon 

species. We have developed two portable field prototype systems and tested the SERS and LSPR 

sensing performance at Ohmsett. The sensors were capable of detecting ppm level PAH in salt 

water. 

Based on the proof of concept sensor performance, we believe further engineering can push the 

sensors to achieve ppm level detection of PAH and BTEX in the sub-sea environment. Due to 

the compactness and power-efficiency of these sensors, future integration with ROV and/or 

AUV is feasible. 
5

Introduction

In sub-surface offshore oil spills, sensing innovation is urgently needed to replace current 

pipeline monitoring, which is indirect and prone to misses and false alarms. In this project, we 

have investigated the capability of chemical sensing by integrating plasmonic nanostructures, 

multi-modal spectroscopic sensing, and optical fiber technology, with the ultimate goal toward 

real-time leak quantification. The novel sensor would perform 24/7, night & day, distributed, 

and automated operations. Further, since such a low-cost system requires minimal power and 

maintenance, it has the potential to be deployed on multiple platforms to form a cross-platform 

network for spill trajectory monitoring and environmental forensics. 

Background and Significance

Subsea sensing of crude oil spills has been of great interest in environmental protection and 

disaster management. However, this challenge has not been met with reliable and robust 

technology. Our approach addresses many challenges optical sensor faces. By the field 

enhancement of plasmonic nanostructures, the molecular fingerprints can be highly amplified. 

Existing detection limit of plasmon-enhanced sensor can be as good as single molecule level. 

Second, by employing multiplexing and optical fiber links, a single sensing hub can manage 

~100 sensing nodes whose location can be strategically planned before deployment, thus greatly 

improving spatial coverage. Thirdly, together with the intrinsic molecular fingerprinting 

provided by optical spectroscopy, computational sensing algorithms will further enhance the 

detection sensitivity, selectivity, and quantification accuracy. Thus, we envision our approach to 

provide high sensitive, discriminative, and robust detection of trace oil leaks.

Research Objectives

The plasmonic nanostructures of choice is monolithically fabricated nanoporous gold disks 

(NPGD) recently developed by our group [1-19]. NPGD possess large specific surface area due 

to their internal nanoporous network. NPGD also contain numerous plasmonic hot-spots 

throughout the internal volume, which has enabled the demonstration of the following sensitive 

surface-enhanced spectroscopies:

1. Surface-enhanced Raman spectroscopy (SERS)

2. Localized surface plasmon resonance (LSPR) extinction spectroscopy

Results and Discussion

Summary Points of Most Significant Technical Achievement(s) 

1. SERS-based sensing for polycyclic aromatic hydrocarbon (PAH) on NPGD.

2. LSPR-based sensing for polycyclic aromatic hydrocarbon (PAH) on NPGD.

3. Test field-portable instrument prototype at Ohmsett

Identify and investigate key parameters in fabricating NPGD arrays

Experiments have shown the performance of NPGD substrate is strongly influenced by structural 

design such as disk diameter and thickness, under-layer thickness and patterning depth. In order 

for such highly effective SERS substrate to be routinely fabricated and to further enhance its 

performance, it is important to determine the correlation between the SERS activity and these 

fabrication parameters. To this end, we have investigated these parameters and developed a 
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design guideline. For example, Figure 1 shows the tunable LSPR peak by varying the diameter 

of disks.

Figure 1. Tunable LSPR by varying disk diameter.

SERS-based sensing for polycyclic aromatic hydrocarbon (PAH) on NPGD arrays

We have focused on the use of nanotechnology coupled with surface-enhanced Raman scattering 

(SERS) as means for detecting PAH molecules. Our efforts have thus focused on fabricating 

thin film modified porous material as well as evaluating its performance in detecting PAH 

molecules, as shown in Figure 2. Our steps on this work were aimed at the molecular design 

and the synthesis of the bisthiolated aromatic molecule, hexyl-functionalized aryl-bisthiol 

(H(CH2)6OC6H3(CH2SH)2) (C6ArDT), to serve as seeds for the self assembled monolayer 

(SAM) modification on the targeted substrate. C6ArDT offers unique characteristics, such that: 

I) it improves the thermal stability and the life time of the thin film via the chelating effect of two 

headgroups per molecule to the gold surface, II) improve the interaction between the SAM and 

PAH molecules via π-π interaction, and III) SAMs formed from such bisthiolated molecules are 

inherently expected to have a lower packing density compared to other SAM systems that have 

been used in comparable studies. The latter feature allows for a facile way for generating SAMs 

functionalized substrates with high reproducibility in their sensing capability.

Figure 2. Illustration of C6ArDT SAMs formed on NPGD for trapping pyrene molecules.
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Organic thin film characterization on NPGDs Substrates 

Following the optimization of the NPGDs substrate fabrication to give the highest SERS 

intensity, organic thin films of the newly synthesized bisthiolate molecule (C6ArDT) were 

generated by solution deposition of the porous disk substrates in ethanol or THF solutions of 

C6ArDT for 48 hours. In addition, SAMs of n-dodecanethiol (DDT) were also formed using the 

same concentration and incubation time to serve as a reference for this study. X-ray 

photoelectron spectroscopy (XPS) analysis of the generated thin films allows for examining the 

chemical nature of the thiolated head group and the composition of the respected organic film. 

Figure 3 shows the XPS spectra for the regions associated with Au4f, S2p, O1s, and C1s core 

electrons generated from these SAMs. Analysis of the S2p region of all the films confirms film 

formation as indicated by the presence of peaks at ~162 eV and 163.4 eV S2p3/2 and S2p1/2 

respectively. For this region, S2p peaks at ~162 eV and 163.4 eV S2p3/2 are indicative of sulfur 

atoms bound on gold. Furthermore, All S2p region of all SAMs, except those of DDT, show also 

peaks at 163.2–134.4 eV indicating the presence of unbound thiol. In fact, initial assessment of 

the S2p XPS spectra of C6ArDT SAMs generated from ethanolic solution shows that films from 

this system portray a high level of unbound thiol (54%). Thus, in order to improve the binding 

percentage of the bisthiolated headgroups, C6ArDT solutions of THF were tested at 

concentration of 1mM, 0.1 mM, and 0.01mM, see Figure 3 and Table 1. 

Figure 3. XPS spectra of Au4f, C1s, O1s, and S2p core electron emitted from SAMs of DDT 

and C6ArDT of different solution concentrations in THF (1.0 mM, 0.10 mM, 0.010 mM).

Table 1. Ratios of the S 2p, Au 4f XPS Peak Area for the C6ArDT SAMs and Those of C12, as 

well as Their Relative Packing Densities a

SAM (mM) %Bound Thiol (S 2p / Au 4f) Relative Packing Density

C12 (1.0) 100 % 0.0087 1.00

C6ArDT (1.0) 82 % 0.0053 0.61

C6ArDT (0.10) 88 % 0.0049 0.56

C6ArDT (0.010) 94 % 0.0041 0.46

a All the S 2p / Au 4f ratios of the bisthiolated monolayers (C6ArDT are multiplied by a factor of 

0.5 to allow for a fair comparison with monothiolated monolayers (C12).
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Following XPS analysis of the C6ArDT monolayer formed from different concentration, we 

characterized these monolayers using SERS. All monolayers used herein are adsorbed on 

NPGDs substrates that are covered by 4 nm thick outer gold layer. Figure 4 shows SERS spectra 

of the C6ArDT. Featured peaks include 668, 699, 993, 1217, 1297, 1332, and 1590 cm-1. Based 

on earlier Raman analysis of benzylthiol derivatives, the observed peaks correspond to C–S 

stretching (668 cm-1), benezene ring breathing (993 cm-1) and C–C ring stretching (1590 cm-1) 

[Phys.Chem.Chem.Phys.2015,17,7095]. As for bands at 1217 cm-1, such band may correspond 

to benzylic CH2 twisting mods. 

Figure 4. SERS spectra of C6ArDT SAMs generated from 1, 0.1, 0.01 mM THF solution on 

NPGD.

The SERS spectra of the C6ArDT SAMs in Figure 4 also show that the peak intensities for all 

bands of the SAMs formed from 0.01 mM concentration are lower than bands in the other two 

SAMs. In addition, the bands’ intensities in later two types of films are incrementally identical. 

These results corroborate with the relative packing densities obtained by the XPS analysis, thus 

further supporting the notion that lowering the concentration of the bisthiolated molecule not 

only improved the percent binding of the headgroups but also provide a monolayer with lower 

density of molecules, an issue of vital importance in designing SAMs to serve as adhesion layers 

for detecting PAH molecules.

SERS Detection of PAH molecules

Prior to evaluate the SERS detection of PAH molecules (pyrene [PY], anthracene [ANT], and 

benzo[a]pyrene [BaP]) on the newly designed NPGDs platform, we collected and analyzed 

Raman spectra of these PAH molecules in solid state (Figure 5). Based on assignment in the 

literature, Raman peaks for PY are 407 cm-1skeletal stretching mode, 594 cm-1 CC breathing 

mode, 1246 cm-1 CC stretching and CH in plane bending mode, and 1406 cm-1 CC stretching and 

ring stretching mode. As for ANT molecules; the observed strong peaks are 395 cm-1 CC 

breathing mode, 750 cm-1 CC ring stretching (12 C) mode, and 1404 cm-1 CC ring stretching (14 

C) mode [Anal. Chem. 2011, 83, 2518]. As for BaP, The main Raman peaks are those at 1250 

cm-1, 1380 cm-1, and 1650 cm-1, which correspond to C–H bending, C–H in plan bending and 

ring deformation, as well as C–C associated with ring stretching mode.
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Figure 5. Raman spectra of solid PY (black) ANT (red), and BaP (blue).

Limit of Detection (LOD) of PAH Molecules

Following detailed evaluation and analysis of both the substrates as well as the organic coatings 

qualities, we used the optimized conditions to investigate the limit of detection (LOD) of PAH 

molecules on C6ArDT/NPGD substrates. Figure 6 shows the SERS spectra of PY, ANT, and 

BaP as a function of their concentration. Note that PY is detectable at 10 μM concentration as 

evident by the peaks at 406, 590, 1240, and 1406 cm-1. On the other hand, ANT is detectable at 

50 μM concentration. While these PAH concentrations are higher than those obtained from other 

colloidal systems, such concentrations, which correspond to 2 and 9 μg/mL for PY and ANT 

respectively, are comparable and even lower than those been observed on other surface-bound 

plasmonic substrates. Furthermore, the anomalies between ANT and PY might be due to 

difference in solubility of the molecules in the methanol, as well as the greater vdW interactions 

between the PY and the C6ArDT SAMs compared to those of ANT and the organic monolayers. 

Both of these effects are evident in the SERS results of BaP, a larger molecule with lower 

solubility in methanol compared to the PY and ANT. Figure 6 also shows SERS data for BaP

detected on C6ArDT/NPGDs, as well as Raman spectra of solid BaP. Note that all Raman peaks 

corresponding to BaP are clearly detected in the SERS spectrum of 500 μM concentration. 

Furthermore, analysis of SERS spectra obtained from several BaP with descending 

concentrations in methanol solutions shows BaP is detected down to 6 μM concentrations. 
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Figure 6. SERS spectra of Py, ANT, and Bap deposited on C6ArDT / NPGD substrates. The 

PAH concentration was varied to determine their lowest detectable concentration on these 

nanoporous substrates. 

LSPR-based sensing for PAH on NPGD

NPGD has tunable plasmon resonance that is dependent on the diameter. In our previous work, 

we have shown that 500 nm diameter NPGD has plasmon resonance in the near infrared (NIR) 

wavelength ~1200 nm. By monitoring the resonance shift due to hydrocarbon adsorption, a 

sensing mechanism can be established based on LSPR extinction spectroscopy.

Physical and optical characterization of NPGDs 

First, we describe the physical nanostructural features of the NPGD substrate. The NPG disks 

(diameters: 350 and 600 nm) were fabricated according to methods recently published. The SEM 

images show the surface morphology and monolayer distribution of the patterned NPGDs on the 

glass coverslip (Figures 7A-B). With a monolayer coverage of ~50 %, the NPGD nanoparticles 

are separated in between by the bare glass surface.

Figure 7. (A & B) Scanning electron microscopy (SEM) images and (C) normal-incidence farfield extinction spectra of nanoporous gold disks (NPGDs) arrays on glass in air with diameters: 

(A) 350 and (B) 600 nm, respectively. Scale bar is 500 nm.

400 600 800 1000 1200 1400 1600 1800 2000 Extinction (a.u.) Wavelength (nm)

 350 nm

 600 nm
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The far-field extinction spectra provide information for the wavelength range where the in-plane 

near-field resonance, specifically unique to patterned NPG disks, is maximized. As the 

nanoparticle diameter size becomes larger, the in-plane LSPR peak red shifts further toward the 

NIR region beyond 1000 nm (Figure 7C). With the 600 nm diameter NPGD, the in-plane LSPR 

peak at 1415 nm is further red-shifted compared to the 350 nm NPG disks at 890 nm. 

LSPR sensitivity to local refractive index variations

Here we evaluate the index sensitivity of the NPG disk substrate. The LSPR peak shifts of NPG 

disks due to the change in refractive index in the surrounding media have been demonstrated 

previously. Herein, the index sensitivity of the LSPR shift of 350 nm NPG disk is evaluated by 

the use of various liquid samples of known refractive indices. The organic thin films, estimated 

to be ~10 μm in thickness using a Fabry-Perot free spectral range calculation, were sandwiched 

between a blank glass coverslip and a NPG disk coated glass coverslip (Figure 8A). The NIR 

extinction spectra were acquired at normal incidence. In Figure 8B, the LSPR of NPG disks of 

two diameters both red-shifted when the samples were immersed in water: from 890 nm to 1070 

nm for 350 nm NPG disks; from 1415 nm to 1700 nm for 600 nm disks. It is interesting to see 

that the O-H combination peak at 1920 nm has been more significantly enhanced by the 600 nm 

disks compared to the 350 nm disks because the LSPR of 600 nm disks aligned better with the 

O-H peak. In Figure 8C, a progressive red-shift of the in-plane LSPR is observed on 350 nm 

disks with respect to further increase of the sample index. The index sensitivity, dλ/dn, was 

calculated to be 525.7 nm per refractive index unit (RIU) as calculated from the plot of peak shift 

vs. n in different media in the inset of Figure 8C.

Figure 8. (A) Schematic diagram of the sample configuration. (B) Normal-incidence extinction 

spectra of water on NPG disks of two diameters: 350 and 600 nm with the O-H combination 

600 900 1200 1500 1800 2100 2400 Extinction Wavelength (nm)

 H2O - 350 nm NPGD

 H2O - 600 nm NPGD

 H2O 

A

B C

NIR 

detector NIR light

Sample film (10 um thick)

glass NPGD array

glass

600 800 1000 1200 1400 1600 1800 Wavelength (nm)

water salt water ethanol hexane

isooctane

hexadecane

toluene Extinction

1.0 1.1 1.2 1.3 1.4 1.5 0

50

100

150

200

250

y = 525.7x - 519.9 R

2

 = 0.987 Refractive Index (n) 0.951.001.051.101.151.201.251.301.351.401.451.501.55 0

50

100

150

200

250 Peak shift, max

 (nm)
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peak at 1920 nm. (C) Normal-incidence extinction spectra of disks (350 nm diameter) in various 

solvents of known refractive indices varying from 1.33 to 1.49: water (n = 1.33), 5% w/w NaCl 

in water (n = 1.34), ethanol (n = 1.36), hexane (n = 1.37), iso-octane (n = 1.39), hexadecane (n = 

1.43), and toluene (n = 1.49); inset: The LSPR shift with respect to air for the corresponding 

refractive indices for each solvent are shown.

Model and simulation of NPG disks

We have developed modeling techniques for NPG disk. To model the NPG disk and calculate its 

plasmonic properties, two different modeling approaches have been developed. A model based 

on the Bruggeman effective medium theory (B-EMT model) requires little information about the 

nanoporous structure. In contrast, the nanoporous model (NP model) retains the essential 

nanoporous structural features of NPG. To evaluate the performance of these models, simulated 

extinction spectra have been compared to the experimental data. Both the B-EMT and NP 

models perform well to estimate the far-field plasmon resonance peak position. However, to 

obtain the accurate information about the plasmon peak width/ plasmon lifetime and near field 

plasmonic hot-spots formation within the nanopores, the NP model is essential since the B-EMT 

model lacks the nanoporous network. 

B-EMT model 

The B-EMT model for NPG disk can be mathematically expressed as

(1 − δAir)

εAu−εNPGD

εAu+2εNPGD

+ δAir

εAir−εNPGD

εAir+2εNPGD

= 0 (1)

In Eq. 1, εNPGD is the effective permittivity of NPG disk, εAu and εAir are the permittivity of gold 

and air, respectively, and δAir is the porosity or air volume fraction. δAir can be calculated using 

Eq. 2 under the assumption of complete removal and preservation of silver and gold, 

respectively, during the de-alloying process.

δAir =

1−δDealloy−δAu

1−δDealloy

 (2)

δDealloy =

VAlloy Disk−VNPGD

VAlloy Disk

 (3)

In Eq. 2, δAu is the Au volume fraction in Ag-Au alloy disk and δDealloy is the volume fraction 

reduction due to dealloying process. Due to the existence of the adhesion layer, there was no 

diameter shrinkage. However, the thickness was reduced from ~76 nm to ~50 nm. This gives 

δDealloy≈ 0.34, which yields δAir≈ 0.54. The calculated δAir is consistent with the previously 

reported air volume fraction which ranges from 0.3 to 0.65 for NPG films. Besides B-EMT, the 

Maxwell-Garnett effective medium theory (MG-EMT) can also be employed to the NPG 

nanostructure. For example, Jiao et al. used the MG-EMT to estimate the porosity of patterned 

NPG substrate. However, the MG-EMT provides good approximation as long as the inclusion 

volume fraction is below 30%. When the volume fractions of the constituents are comparable in 

a composite material, the B-EMT is preferable. The comparable volume fractions of air (0.54) 

and gold (0.46) in NPG disk thus justify the use of B-EMT over MG-EMT.

Nanoporous (NP) model

The NP model is obtained by utilizing the 2D SEM images of NPG film of comparable 

dimensions. The modeling relies on the following assumptions: the 3D NPG structure can be 
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roughly classified into ligament dominating layer (Au rich layer) and pore dominating layer (air 

rich layer) and these two types of layers alternate. Such assumptions are important for ensuring 

the bi-continuous nanostructures of NPG. For the ligament dominating layer, the tube-like 

ligaments are extended horizontally to form 3D meshes with pores in-between the ligaments. As 

for the pore dominating layer, the ligaments are extended vertically and appear to be suspended 

in air (porous region). Figure 9 (a-e) show the stepwise schematic representations for generating 

the NP model of NPG disk. SEM images are binarized into Au rich and air rich images via 

different threshold values as shown in Figure 9a. The binarized images are stacked into 3D 

ligaments as shown in Figure 9b. For the ligament dominating layer, nearby images are 

generated by gradual increase in the pore area (black part), while for pore dominating layer, 

same images are stacked without modification. 

Figure 9. NP model of NPG disk: (a) step 1-binarization of 2D SEM images, (b) step 2-stacking 

of binarized images, (c) step 3-stacking of layers into NPG film, (d) step 4-disk cutting, (e) step 

5-surface smoothing of as generated NPG disk.

These layers are then alternated and stacked into a thicker NPG film as shown in Figure 9c. 

During stacking, the layers are intentionally overlapped (roughly 20%) to ensure the poreligament continuity. In our study, 75 nm thick NPG film is generated with 5 ligament dominating 

and 4 pore dominating layers. Then NPG disks with desired diameter and thickness are cut out 

from this film as shown in Figure 9d. The generated NPG disk model has many sharp edges 

which are rounded by surface smoothing as shown in Figure 9e. The NP model of NPG disk has 

porosity (δAir) in the range of 52%-54%.

Constructing instrument prototype for Ohmsett testing

LSPR approach

The setup and the schematic drawing of the underwater LSPR sensing system are shown in 

Figure 10. The output from a whitelight lamp is coupled into the input fiber (blue) through a 

fiber collimator. The input fiber terminates at the underwater probe pointing at the plasmonic 

sample. The plasmonic sample is fixed onto an exchangeable sample plate. After passing though 

the plasmonic sample, the light is collected by a fiber collimator and coupled into the output 
14

fiber (orange). The output fiber is connected to a portable spectrometer. The data are read out 

with a computer and the extinction spectra are calculated. 

Figure 10. Setup of the LSPR sensing system. 

SERS approach

The setup and the schematic drawing of the underwater SERS system are shown in Figure 11. 

The laser light (785 nm) is coupled into the input fiber. The laser is focused onto the SERS 

substrate by the portable Raman probe. The SERS signal is also collected by the probe and 

coupled into the output fiber. The output fiber is connected with a spectrograph and a CCD 

camera. By monitoring the intensity change of the SERS signal, one can determine the analyte 

concentration at the SERS substrate surface. The underwater probe is consisted of a portable 

Raman probe fixed into a plastic case. The SERS signal is collected through a fused silica 

window fixed onto the plastic case. The SERS substrate is fixed onto an exchangeable sample 

plate that is attached to the case. 

Figure 11. Setup and schematic of the SERS system. 
15

Ohmsett testing

Background

We tested our prototypes at Ohmsett facility to obtain data using several crude oil varieties 

dispensed in various concentrations in test tanks. The tests were conducted during the period of 

November 6

th

– 10th, 2017. The experimental processes and results are summarized in Table 2

and Table 3. Two types of experiments were conducted for sensing 4 types of crude oils and 

toluene. The concentrations varied within 0-9 ppm in terms of the crude oil or toluene. The PAH 

concentrations were therefore expected to be much lower. Crude oil samples were later 

submitted for laboratory PAH analysis and the results suggested PAH levels were indeed very 

low (sub-ppb).

Table 2. Crude oil tests

Injection Stock 

solution Crude oil Toluene Methanol Crude oil concentration

1 10 ml 0.2 ml 4.9 ml 4.9 ml 1.1 ppm

2 10 ml 0.2 ml 4.9 ml 4.9 ml 2.3 ppm

3 10 ml 0.2 ml 4.9 ml 4.9 ml 3.4 ppm

4 10 ml 0.2 ml 4.9 ml 4.9 ml 4.6 ppm

5 10 ml 0.2 ml 4.9 ml 4.9 ml 5.7 ppm

6 10 ml 0.2 ml 4.9 ml 4.9 ml 6.9 ppm

7 10 ml 0.2 ml 4.9 ml 4.9 ml 8.0 ppm

8 10 ml 0.2 ml 4.9 ml 4.9 ml 9.1 ppm

Table 3. Toluene test

Injection Toluene 

volume Toluene con.

1 2 ml 10 ppm

2 8 ml 60 ppm

3 10 ml 130 ppm

4 10 ml 200 ppm

5 10 ml 260 ppm

6 10 ml 330 ppm

LSPR sensing results

To produce a LSPR peak in the visible wavelength range, gold/silver alloy nanodisk array were 

employed as the sensing surface. The extinction spectra of the LSPR sensor were acquired and 

calculated after each step of injection. There are two modes of peak shift exist with LSPR 

sensing. The first one is a gradual and minor red shift (1-2 nm) in the LSPR peak position with 

increasing crude oil concentration and immersion time. Such shift could be induced by the 

dissolved crude oil being captured by the SAM. In this case, the change at the sensor surface is 

gradual and minor. The second LSPR shift mode is a sudden and major redshift up to few tens of 

nanometers. We believe such shift is caused by the non-dissolved crude oil droplet falling onto 

the sensor surface. In this case, the change at the surface is sudden and major. The crude oil 

droplet falling event happens more often at higher crude oil concentration, possibly because there 
16

are more droplets of non-dissolved oil. Even though the droplet falling onto the sample surface 

will block the surface and results in sensor failing, we do note that due to a large sensing area, 

single droplet falling event does not block the entire surface. Additionally, the capture of crude 

oil droplet can also be an indicator for oil leaking. In the future, a mesh filter may be 

incorporated with the sensor to prevent non-dissolve oil droplets from accessing the sensing 

surface. To analyze the LSPR peak shift, the raw spectra were smoothed out by smoothing spline 

method and the peak positions were obtained by locating the extinction maximum as shown in 

Table 4. From the data, we can obtain the sensitivity of the LSPR sensor by a linear fit. The 

obtained sensitivities were 1.53, 2.18, 0.63 and 1.09 nm/ppm for Alaska North Slope (ANS), 

BAKKEN, ENDICOTT and OSEBURG respectively. The different sensitivities obtained with 

various crude oil samples are presumably caused by the differences in their solubility and 

composition. 

Table 4. LSPR sensing results

Bakken ANS Endicott Osburg

ppm Peak shift (nm) ppm Peak shift ppm Peak shift ppm Peak shift

0 0 0 0 0 0 0 0

1.1 2.4 1.1 2.1 1.1 0.6 1.1 2.3

2.3 5.5 2.3 4.2 2.3 1.7 2.3 3.1

3.4 7.1 3.4 6 3.4 2.6 3.4 3.7

4.6 14.2 4.6 7.1 4.6 2.9 4.6 5.4

5.7 28.2 5.7 48.2 5.7 3.6 5.7 7.3

6.9 29.1 6.9 50.8 6.9 53.2 6.9 8.4

8 41.4 8 60.8 8 62.6 8 9.7

9.1 49.5 9.1 62.8 9.1 N/A 9.1 10.3

SERS Sensing results

To demonstrate the applicability of our technology, we have attached our functionalized NPGD 

substrates to a portable Raman sensor and performed in situ detection of toluene as well as 

different types of crude oil at Ohmsett facility. Figure 12 shows the SERS spectra of toluene in 

water detected using our sensor. Toluene has two major Raman peaks at 780 cm-1and 990 cm-1. 

In addition, Figure 12 shows that we were able to detect toluene in water at concentrations as 

low as 130 ppm. Note that the first Raman peak at 780 cm-1is clearly noticeable at the 130 ppm 

concentration. However, the peak at 990 cm-1overlaps with that of the SAM at 1000 cm-1as in 

indicated by the increase in peak broadening at 1000 cm-1. Analysis of peak intensity versus the 

concentration of toluene shows a linear dependency. 
17

Figure 12. SERS spectra of in situ detection of toluene dissolved in water as well as the linear 

relationship of intensity versus log of toluene concentration in water.

We have also attempted to detect four different types of crude oil using our SERS sensors. 

Crude oil samples of Endicott, North Star, ANS, and Bakken were dissolved in methanol/toluene 

mixture, followed by their injection in aqueous media (Table 2). SERS spectra of different types 

of crude oil were measured and shown in Figure 13, from which Bakken performed the best. A 

clearly defined peak was detected at 419 cm-1, which might be resulting from CCC out of plane 

bending mode of an aromatic system. This peak was detected at oil concentration as low as 1 

ppm. The gradual increase in the concentration of the crude oil results in the increase of the 

intensity of this peak. Other crude oil types did not show a particular Raman peak and tend to 

foul the sensor at concentrations as high as 8 ppm for Endicott and ANS and 6 ppm for North 

Star. As mentioned earlier, the PAH concentrations in the experiments (Table 2) were in the 

sub-ppb range, which were generally lower than the LOD for the SERS sensor. Nonetheless, this 

in situ SERS detection of toluene in water is a promising evidence for potential detection of 

water-soluble aromatic compounds.
18

Figure 13. Raman spectra of crude oil detected on functionalized NPGD sensor at low 

concentrations (ppm range).

Conclusion

We have developed a novel plasmonic nanostructures, nanoporous gold disks, for multi-modal 

spectroscopic sensing together with optical fiber technology. NPGD possess large specific 

surface area due to their extensive nanoporous network. NPGD also contain numerous plasmonic 

hot-spots throughout the porous network. For selective molecular binding of hydrocarbons, we 

have developed a compatible aryl-bisthiolate functional surface. Based on SERS and LSPR, we 

have demonstrated the detection and quantification of various polycyclic aromatic hydrocarbon 

species. We have developed two portable field prototype systems and tested the SERS and LSPR 

sensing performance at Ohmsett. The sensors were capable of detecting ppm level PAH in salt 

water. 

Recommendations for future work

Based on the proof of concept sensor performance, we believe further engineering can push the 

sensors to achieve ppm level detection of PAH and BTEX. Due to the compactness and powerefficiency of these sensors, integration with ROV and/or AUV is feasible.
19

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