Toward Universal SERS Detection of Disease Signaling Bioanalytes

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Towards universal SERS detection of disease signalling bioanalytes using 3D self-assembled non-plasmonic near-quantum scale silicon probe Jeffery Alexander Powell, Krishnan Venkatakrishnan, and Bo Tan ACS Appl. Mater. Interfaces, Just Accepted Manuscript • DOI: 10.1021/acsami.7b15393 • Publication Date (Web): 30 Oct 2017 Downloaded from http://pubs.acs.org on October 31, 2017

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ACS Applied Materials & Interfaces

Towards universal SERS detection of disease signalling bioanalytes using 3D selfassembled non-plasmonic near-quantum scale silicon probe Jeffery Alexander Powell,aPh.D., Dr. Krishnan Venkatakrishnana,c, d *Ph.D., Dr. Bo Tanb Ph.D. a

Ultrashort Laser Nanomanufacturing research facility, Department of Mechanical and Industrial

Engineering , Ryerson University , 350 Victoria Street , Toronto, ON, Canada , M5B 2K3 b

Nanocharacterization Laboratory , Department of Aerospace Engineering, Ryerson University,

350 Victoria Street, Toronto, ON, Canada , M5B 2K3 c

BioNanoInterface Facility, Department of Mechanical and Industrial Engineering, Ryerson

University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada d

Affiliate Scientist, Keenan Research Center for Biomedical Science, St. Michael's Hospital, 30

Bond St, Toronto, ON M5B 1W8 *

Corresponding Author: [email protected] 416-979-5000 ext 4984

Keywords: SERS; quantum scale; biosensors; glutathione; silicon; universal biosensing

Word Count: 10248 Table Count: 0 Figure Count: 10 Scheme Count: 0

ACS Paragon Plus Environment


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Abstract: Currently, the quantum scale surface enhanced Raman scattering (SERS) properties of Si materials have yet to be discovered for universal biosensing applications. In this study, a potential universal biosensing probe is generated by activating the SERS functionality of Si nanostructures through near quantum-scale (nQS) engineering. We introduce herein, 3D non-plasmonic Si nanomesh structure with nQS defects for SERS biosensing applications. Through ionization of a single crystal defect-free Si wafer, highly defect-rich Si sub-nano-orbs (sNOs) are fabricated and selfassemble as connective 3D Si nanomesh structures with enhanced SERS biosensing activity. By amending the laser ionization and ion-ion interactions, we observe the controlled synthesis of engineered nQS defects in the form of nQS-grain boundary disorder or surface nQS-voids within the interconnected Si sNOs. To our knowledge, it is shown here for the first time, defect-rich Si nanomesh structures exhibit enhanced Raman activity, with the nQS morphological and crystallographic defects acting as the prime SERS contributors without a plasmonic contribution. The SERS biosensing sensitivity with the synthesized defect-rich Si nanomesh structures without an additional plasmonic material was evaluated using of a tripeptide biomarker L-glutathione (GSH); we observe an enhancement factor value of ~102 for the GSH biomolecules with 10-9M sensitivity, a phenomena to our knowledge, that has yet to be reported. Additionally, the SERS detection of multiple disease signalling biomolecules (cysteine, tryptophan and methionine) with is achieved at very low analyte concentration (10-9M). These results indicate a potential new dimension to universal SERS biosensing applications with these unique non-plasmonic defect-rich 3D nQS-Si nanostructures.


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Introduction: For biosensing applications that employ surface enhanced Raman scattering (SERS) techniques, the use of nanostructured noble metal materials have been extensively researched due to the wellestablished surface plasmon resonance (SPR) enhancement effect unique to these nanomaterials.1 These plasmonic nanostructures have been modified and optimized to excite the detection of various bioanalytes; noble metal nanorods2, nanoparticles3, and nanowires4 have all shown enhanced SERS biosensing characteristics. These plasmonic nanomaterials have been able to provide a significant enhancement for the detection of various biomolecules, including proteins5, cancer cells,6 DNA7 and mRNA.8 More recently however, semiconductor/metal oxide (S/MO) nanoscale materials including WO3-x9, TiO210,11, CuO12, and NiO13 have been shown to display significant SERS activity.14 These alternative SERS enhancing nanomaterials have the potential to reduce or replace the use of noble metals in biosensing devices by overcoming the limitations associated with noble metals, including their lack of reproducibility of SERS enhancement and specific biocompatibility issues. There has been a significant number of studies that suggest that gold nanoparticle have significant obstacles in terms of biological toxicity. Au nanoparticles are shown to have moderate to high toxicity when in the presence of biological materials and this inherent toxicity often requires additional mediation such as surface charge or encapsulation.15,16 This intrinsic toxicity of Au nanoparticles, the most common plasmonic material used for SERS sensing applications, presents a significant hurdle to overcome particularly for universal sensing applications where the integrity of a sample containing many diverse biomolecules is essential. However, current research indicates that non-plasmonic SERS active S/MO nanostructures and quantum dots have a high degree of biocompatibility and non-toxicity with biological materials, which can make these S/MO materials prime candidates for universal SERS biosensing platforms.17 Recent studies have shown that by engineering defects within S/MO nanostructures, SERS activity can be significantly enhanced. Cong et al. 18 and Wu et al. 19 have shown that by introducing point defects into S/MO nanostructures in the form of oxygen vacancies, very high enhancement factor values (105-107) and ultrahigh sensitivity (10-7M-10-8M) can be observed for chemical analytes with high Raman cross-section; these reported EF values and sensitivities are on par with NMNs. In addition Ji et.al 20 have shown that O2 vacancies on TiO2 nanoparticles acts as coordination sites



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for molecules which causes greater CT between the nanoparticles and analyte molecules. Additionally, research has shown that S/MO nanostructures can have a significant degree of nonuniformity present within their structure and exhibit a substantial SERS effect potential. Jin et al.21 have shown that Zn nanorods covered with a film of Zn nanocrystals results in a high degree of surface defects and as a result a significant increase in SERS performance. Gong et al. 22 have shown that by regulating the formulation of their anatase TiO2 nanoparticles, they could create polycrystalline TiO2 nanoparticles with significant surface defects. As a result, an increase in the SERS performance was observed due to a promotion of TiO2-to-molecule CT resonance caused by this non-uniformity in structure. These observations of SERS enhancement through defect engineering reveal that by manipulating S/MO nanostructures near the quantum scale results in a significant impact on the SERS enhancement properties. The use of quantum-scale (QS) materials as sensing probe materials is currently a relatively uncharted area of research, yet there has been some evidence to suggest that the QS offers new sensing phenomena not observable at the nanoscale. In the works by Zeng et al. 23 and Liu et al. 24 have shown that QS materials demonstrate ultrasensitive photoelectrochemical detection of complex biomolecules, carcinoembryonic antigen and bacterial DNA, respectively. While the descent towards the QS offers significant potential for vastly enhancing biosensing, QS SERS is still in the nascent stages of development. However, QS SERS functionality has been observed to demonstrate significant sensing proficiency for biosensing applications. Zou et al. 25 have shown that by depositing graphene quantum dots on a 1D nanochain of Fe3O4@Au core-shell nanoparticles, they are able to detect a tuberculosis antigen with a very low detection limit. However there exists limitations of the extent to which QS plasmonic materials can enhance SERS detection. Zhu et al.26 have demonstrated that the SPR SERS enhancing properties of nanogaps between Au nanoparticles are significantly diminished in the quantum regime due to the emergence of quantum mechanical electron tunnelling effects that arise at this scale. The QS SERS properties of non-plasmonic materials, has to our knowledge, not been explored for the application of S/MO materials as SERS biosensing platforms by a series of resonance mechanisms including Mie resonances,27 exciton resonances and of principle importance the charge-transfer (CT) resonance mechanism. 28


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Silicon is the most common material found in the production of biosensing device production and has been shown to have significant advantages for biosensing applications. In the work by He, Yao, et al.29, Si nanowires are grown by oxide-assisted growth via a thermal evaporation technique. These Si nanowires are then coated with silver nanoparticles by reduction reaction to create Si nanowires decorated with Ag nanoparticles. A DNA self-assembly bonding protocol is then followed to bind the target DNA molecules to the Ag nanoparticles that have been coated onto the Si nanowires and the SERS spectra of the DNA is detected. In the work by Deng, Yu-Luen, and Yi-Je Juang.30, a black silicon substrate consisting of silicon nanocones is fabricated by using reactive ion etching technique on a silicon wafer. By adjusting the flow rates of O2 and SF6 they are able to modify the morphology of the Si nanocones. Then, an electron beam evaporation technique is then employed to deposit a plasmonic gold layer of varying thickness on the surface of the Si nanocones; Algal cells are then cultivated on the surface of the gold layer and the SERS enhancement is measured. While these nanostructured substrates are used for SERS biosensing, the main contributor to the SERS enhancement, in both cases, is the deposited plasmonic structure and the Si nanostructure acts as a scaffolding. The SERS properties of Si-only nanostructures have yet to be fully studied and have yet to be applied to biosensing applications. The properties of Si, like other semiconductor materials, can be easily controlled. Si properties such as morphology, degradation resistance, band gap and surface functionality31-32 can easily be manipulated and tailored for specific applications but the most important property of Si nanomaterials is their high degree of biocompatibility. Si nanomaterials have shown significant biocompatibility33 for biosensing applications34, including Si nanowires as protein biosensing platforms35 and as bacterial cell sensing platforms.36 Current research shows that silicon materials display considerable biosensing functionality at the QS. Recently, it has been observed that Si quantum dots exhibit unique electrogenerated chemiluminescence37 biosensing characteristics for DNA detection and for photoluminescence detection of explosive chemicals.38 Si-based SERS platforms have been shown to demonstrate meaningful potential as sensing substrates. Yilmaz et al.39 have shown that a Si-based SERS platform can achieve an EF value on the order of 102-103 for highly Raman molecules methylene blue and rhodamine 6G. Although these preliminary results show that 2D QS Si can be employed as a highly advantageous sensing platform, the SERS biosensing characteristics of QS Si have yet to be studied.



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It has been shown that universal SERS biosensing has the potential to be achieved using both plasmonic and non-plasmonic nanomaterials. In order to detect multiple bioanalytes present within a single sample, each of which can act as biomolecular signals for certain diseases, a sensing material should exhibit universal SERS biosensing characteristics Si nanopillars with Ag nanoparticles40, Si micro-rings41, Ag droplets on GaN nanowalls42, and Ag/Au nanorods decorated with magnetic-beads43 have exhibited universal biosensing of an array of bioanalytes, including protiens, DNA, bacterial cells, antibodies, nucleic acids among others As such, a universal biosensing material is required to detect bioanalytes with high degree of specificity and have limitless biocompatibility; characteristics which have been shown to be a significant drawbacks associated with plasmonic nanostructures44, but can be mediated by utilizing S/MO SERS materials.42 In this study we introduce, for the first time to our knowledge, the concept of enhanced biosensing functionality using a 3D Si-only nanostructure as a means of enhancing the detection of bioanalytes, in addition to controlled engineering of nQS defects within the Si nanostructure as direct and substantial SERS enhancement source without an additional plasmonic SERS contributor. This controlled synthesis of a non-plasmonic nQS defect-rich Si nanostructure establishes a new dimension to universal SERS biosensing applications. Herein, a yet to be studied method of Si-only SERS biosensing is presented, with a self-assembled 3D Si nanostructure consisting of fused sub-10nm nano-orbs (sNOs) and engineered near quantum-scale (nQS) defects within the individual Si sNOs. Through ultrafast pulsed laser ionization of defect-free a single crystal silicon wafer substrate, defect-rich Si sNOs self-assemble and fuse on the Si wafer surface as a highly branched three-dimensional mesh-like structure. Currently, there exists some limited research conducted in the creation of 3D Si nanostructures through ultrafast pulsed laser ionization.45,46 However, to the best of our knowledge no research has been conducted that investigates the controlled fabrication of nQS defects in a Si nanostructure, the formation and properties of these defects nor the potential role these defects can play in a non-plasmonic SERS biosensing application. Herein, QS is defined by the common classification of quantum scale for Si quantum dots, which have size ranges from 1-7nm 37,47-49 and nQS refers to the size of defects observed in this study which approach this scale. We demonstrate that the nQS nature of the defects within the Si sNOs results in a new form of multi-sourced SERS enhancement due to these new forms of nQS defects. It is found that the form of nQS-defects present within the sNOs can be


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modified by amending the ion interactions within the laser-ion plume. Two types of nQS-defects have been introduced into the Si-only nanomesh structures in this study; the first being an internal disorder of the crystallographic grains with the individual sNOs that comprise the nanomesh. The second being in the form of nQS-voids on the surface of the sNOs. It is found that these nQSdefects within the Si sNOs yield a significant activation of vibrational activity of the resultant Si nanomesh structures. To our knowledge, we report here for the first time, nQS-defects as a prime source of ultrahigh Raman activity of a Si-only nanostructure. An investigation of how these near quantum scale defects within these Si nanomesh can affect the SERS selectivity with a biomolecule analyte, L-glutathione (GSH) has been conducted along with an evaluation of the wavelength dependence and detection sensitivity. GSH is a tripeptide biomarker which acts as a signalling molecule for diseases neurodegenerative disorders, cancer and cystic fibrosis among others.50 While GSH has been detected using SERS techniques, very limited enhancement and specificity for this biomolecule has been reported, even with plasmonic noble metal nanostructures. With these nQS-defects, the detection of GSH is enhanced to a maximum enhancement factor (EF) of ~102 and is able to be detected at a concentration of 10-9M. Additionally, the detection of biomolecules tryptophan (Trp), cysteine (Cys) and methionine (Met) with the nQS defect-rich Si nanomesh structures was evaluated to determine the potential of this platform as a universal biosensing nanoprobe. These biomolecules have been identified as disease signalling biomolecules for various diseases 51-53 and there have yet to be studies conducted using Si-only nanostructures as a SERS nanoprobe for detecting any of these biomolecules. We report the detection of these important disease signalling biomolecules up to a 10-9M concentration, which demonstrates the potential of defect rich Si-only nanostructures as a universal SERS diseases sensing nanoprobe material. Figure 1 represents the nQS engineering as a means of activating the CT resonance mechanisms with defect-rich Si-only nanomesh structures.



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Figure 1: a) nQS structure and defects as a means of universal detection of disease signalling biomolecules b) Raman spectra associated with l-glutathione on the Si nanomesh structures and Raman spectra of biomolecules c) cysteine (cys), d) tryptophan (trp) and e) methionine (met) on Si nanomesh structures


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Materials and Methods: The generation of the Si nanomesh structures was achieved using a Clark-MXR IMPULSE pulsed Yb-doped fibre amplified femtosecond laser to ionize a 0.02-Ω cm p-type silicon [100] wafer substrate. This ultrafast pulsed laser formation mechanism is kept consistent by maintaining laser wavelength (1030nm), polarization (circular), average laser power (14W) and laser pulse width (214fs). To consistently create an even distribution of the Si nanomesh on the wafer surface, a piezo-driven raster system was used to ionize the Si wafer in a 300x300 point array with 0.25mm spacing designed by EzCAD software. To control and test the range of Si structures that could be viable SERS active nanomesh structures, the laser pulse repetition rate (4MHz and 26MHz) is varied to alter the ionization energy of the laser-ion plume. In order to modify the Si ion interactions for this nanomesh formation mechanism we introduce gaseous species of N2 and O2 into the ion-plume formed by the pulsed laser. The gases are injected into the laser-wafer interaction zone through seven individual nozzles which evenly surround the ionization zone. The gaseous species are introduced at a flow rate of 0.2MPa. Equations 1 and 2 represent the ionization reactions being investigated in this study. + 𝑁2

(𝑔𝑎𝑠)

+ 𝑎𝑖𝑟 (𝑔𝑎𝑠) →

𝑠𝑐𝑆𝑖 (𝑠𝑜𝑙𝑖𝑑) + 𝑂2

(𝑔𝑎𝑠)

𝑈𝐹𝑃𝐿 𝑖𝑜𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛

(𝑔𝑎𝑠)

𝑠𝑐𝑆𝑖 (𝑠𝑜𝑙𝑖𝑑) + 𝑂2

+ 𝑎𝑖𝑟 (𝑔𝑎𝑠) →

𝑈𝐹𝑃𝐿 𝑖𝑜𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛

(𝑆𝑖 2+ + 𝑆𝑖 4+ )𝑖𝑜𝑛𝑠 + (𝑂2− )𝑖𝑜𝑛𝑠 + 𝑁2(𝑔𝑎𝑠) + 𝑒 − (Equation 1)

(𝑆𝑖 2+ + 𝑆𝑖 4+ )𝑖𝑜𝑛𝑠 + (𝑂2− )𝑖𝑜𝑛𝑠 + 𝑒 −

(Equation 2)

The overall 3D of the nanomesh structures were imaged using a FEI Quanta FEG 250 scanning electron microscope (SEM). The physical morphology of the resulting nanomesh structures and the Si sNOs were imaged using a Hitatchi H-7000 high-resolution transmission electron microscope (HRTEM) on copper mesh grids. The size and size distribution of the individual sNOs observed in these images were manually measured and tabulated using ImageJ software. The Xray diffraction (XRD) crystallographic analysis of the nanomesh structures was performed using a Rigaku Miniflex 600 diffractometer. A 2θ scanning range of 15° to 60° was used to acquire the relevant peaks associated with the Si nanostructures. To acquire the Raman spectra of both the bare nanomesh structures and of the protein biomolecule on the nanomesh structures a Bruker Optics SENTERRA Raman confocal microscope with a 10x magnifying lens. Both 532nm and 785nm Raman spectra of the nanomesh and biomolecule were obtained in this study using 5mW and 25mW power respectively. To achieve a consistent



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significant signal a 10s integration time was used and to maintain consistent signal response, 3 iterations were acquire for each Raman spectra in this study. For the acquisition of l-glutathione (GSH) Raman spectra, and the other biomolecules (cysteine, tryptophan and methionine) 10µL of the biomolecule solution of 10-6M or 10-9M concentration was applied to the relevant nanomesh structure just prior to Raman acquisition. Results and Discussion: Nanomesh formation by multi-photon ionization of solid Si substrate The nanomesh structures created in this study are generated using an ionization process that a transforms a defect-free single crystalline Si (sc-Si) wafer into a three-dimensional self-assembled nanomesh network of defect-rich Si sNOs on top of this same sc-Si wafer. To transform a sc-Si wafer into this complex 3D Si nanostructure, the sc-Si wafer must be broken down into its core ions and reformed within the ion-plume. The only method to our knowledge that can create this type of nanomesh structure with this specific material chemistry is through multi-photon ionization using an ultrafast femtosecond pulsed laser (UFPL) formation mechanism. 45,46 The UPFL process is highly programmable and can be manipulated to produce nanostructures of a desired morphology and composition by altering the properties of the impinging laser pulses. The femtosecond laser pulses that strike the substrate surface will transfer such high energy to the scSi wafer substrate, that there is complete ionic breakdown of the solid crystalline structure of the sc-Si wafer to form a cloud of Si ions just above the wafer surface. This results in an ion plume composed of Si ions which combine and react with each other to form the sNOs which condense and collapse onto the Si wafer surface. Figure 2 shows how the Si nanomesh structures are formed in this experimental study. In Figure 2a, the ultrafast pulsed laser immediately ionizes the Si wafer surface leading to Si ions being rapidly ejected from the wafer surface. These Si ions collide with each other to form proto-nanoclusters which agglomerate and form sNOs which condense and fall out of the ion-plume onto the sc-Si wafer substrate. These sNOs fuse together on the Si wafer surface and form a self-assembled three-dimensional nanomesh-like structure. Figure 2e) shows SEM and HRTEM images of the 3D nanomesh structure, the fused sNOs that comprise the nanomesh and the hybrid nature of the sNOs respectively.


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Figure 2: a) femtosecond laser ionization of a defect-free silicon wafer, b) sNO ejection from ion plume, c) sNOs fuse together on Si wafer surface to form d) 3D nanomesh e) SEM and HRTEM images of nanomesh morphology and Si sNOs Since the ion-plume conditions are variable depending on the laser-substrate interaction conditions, the nanomeshes in this study are classified in terms of a quantity known as the ionization coefficient.54 This value is calculated as: 𝛼 = (𝐼 ⁄𝑡𝑐𝑟𝑖𝑡 ) ln(𝑁𝐶 ⁄𝑁0 )

(Equation 3)

Where I is the laser energy density, tcrit is the duration of the femtosecond pulse required to achieve ionization, NC is the electron density of the ion-plume and N0 is the number of electrons in the wafer before ionization. From this equation the ion-plume energy of the different laser conditions used in this study can be quantified. High ionization energy is defined as α=12.9x1013 and low ionization energy defined as 2.50x1013. A more detailed overview of the calculation of the ionization coefficient is include in the supplementary information section.



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In this study the influence of inducing nQS defects during the ionization of Si is being investigated to determine how the resulting defect-rich 3D Si nanomesh structures allow for SERS detection of biomolecule species. Of particular interest is the determination of how these nQS defects can be engineered to functionalize the SERS activity of a Si-only nanostructure for high sensitivity universal nQS biosensing applications. Characterization of disordered Si nanomeshes An investigation of the material and morphological characteristics of the defect-rich Si nanomesh was carried out using a series of characterization techniques including, HRTEM imaging, XRD analysis and Raman spectroscopy; these techniques reveal substantial amount of information regarding how the ion interactions can be altered to induce structural and morphological disorder and distinct properties of the resultant Si nanomesh structures. Figures 3 and 4 show the characterization data obtained for the Si nanomeshes formed under inert ion-plume conditions and oxygenated ion-plume conditions respectively.


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Figure 3: a) HRTEM analysis of QS-defects within sNO formed under inert-ion plume conditions and associated b) XRD spectra, c) sNO size distribution d) calculated crystallite size and e) calculated residual stress



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Figure 4: a) HRTEM analysis of QS-defects within sNO formed under oxygenated ion plume conditions and associated b) XRD spectra, c) sNO size distribution d) calculated residual stress


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HRTEM analysis of nanomesh structures To determine how the ion interactions affect the formation of nQS defects within the Si nanomesh structures, HRTEM imaging was performed to analyze the structure of the individual sNOs that comprise the nanomesh structures. The images (Figure 5) reveal that two distinct categories of nQS defects are induced within the Si nanomeshes; internal nQS grain boundary disorder and an external surface nQS-void disorder. In Figures 5c and 5d respectively, nQS grain disorder is observed within the crystalline structure of an individual sNO and the presence of a highly disordered nQS-voids on the sNO surface are observed. These two distinct forms of nQS defects, manifest as a result of the amendmentation of the ion-plume conditions; the internal nQS grain boundary disorder is formed within an inert ion plume and the external surface nQS-void disorder is formed within an oxygenated ion plume.

Figure 5: TEM and HRTEM images of nQS disordered Si nanomesh structures formed under a) inert ion-plume conditions, b) oxygenated ion-plume conditions and the observed nQS-defects c) nQS grain disorder within the sNO structure and d) nQS-voids on sNO surface This internal nQS grain boundary disorder and nQS-voids surface morphology are to our knowledge not feasible with other fabrication techniques due to the nature by which the sNOs form within the laser-ion plume. The chaotic movement of ions within the laser-ion plume and formation of nQS-clusters with varied crystal orientations, leads to the generation of nQS defects observed in Figure 5. We postulate that, the engineering of these defects within the sNOs is only possible



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due to the breakdown of a sc-Si wafer through ionization and self-assembly within the ion-plume. Other fabrication techniques used to form Si QS-structures for SERS applications produce reported sc-Si nanostructures though lithographic 55 or etching techniques.56 However, there has yet to be an investigation into the role of QS-defects in Si nanostructures play in Raman activity; this can be attributed to common self-assembly techniques inability to engineer QS defects within nanostructures beyond point defects or doping and the single crystalline nature of the studied materials . An additional analysis of the size/size distribution of sNOs within the nanomesh structures shows that the sNO size and distribution is affected by the laser ionization energy and the conditions of the ion-plume. Higher ionization energy under both inert and oxygenated ion-plume conditions yields sNOs sizes with a broader size distribution than sNOs formed at lower ionization energy. The HRTEM images reveal that the sNOs and the disorders within the sNOs approach the QS regime (defined as several nanometers in size 57,58). These images also reveal that the presence of these nQS-defects is more prevalent in sNOs formed in an inert ion plume at higher ionization energy. nQS-grain boundary disorder An analysis of the XRD spectra (Figure 3, 4) obtained from the nanomeshes reveals that, the ionplume conditions produce crystal grain orientations of Si depending on the plume conditions and ionization energy. The XRD plots show that the nanomesh structures created in this study are composed of nanocrystalline Si ([111], [220] and [311]) and a portion of amorphous Si/SiO2. There is an observed peak when 2θ~32°, which is an artifact of the XRD spectrometer, but occurs as a forbidden peak of [100] Si59 The determination of the crystallite size reveals the nQS nature of the nanograin disorder observed in the sNO formed under inert ion-plume conditions. The crystallite size can be determined by the width of the obtained XRD peaks. To determine an estimate of the crystallite size, we have used the Scherrer equation.60 𝐾𝜆

𝜏 = 𝛽 cos 𝜃

(Equation 4)

Where K is a dimensionless shape factor that varies with the shape of the crystallite, for this study we assume that the shape factor is 0.9 assuming that the crystallites are spherical. λ is the wavelength used to obtained the XRD spectra, in our case it is 0.154nm. β is the measured peak


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broadening width at full width half maximum (FWHM). θ is the Bragg angle for the desired peak. The crystallite size is assumed to be the size of the individual regions of crystallinity within an individual sNO. This is because the individual sNOs are measured to have sizes on the order of ~10nm, and the observed crystallite sizes are on the order of a few nanometers. This indicates that each individual sNO is comprised of multiple nQS crystallites each having an identifiable crystal orientation. We have observed in (Figure 3d), that the concentration of these crystallites within the NOs can be manipulated by altering the ionization conditions. Under inert ion-plume conditions, the [111] and [220] orientations of Si remain relatively unchanged, but the [311] orientation is not observed under these conditions and becomes significantly large under oxygenated ion-plume conditions at lower α. At higher α, vary slightly compared to lower α values, with [220] becoming significantly larger under both suppression and enrichment conditions. These sNO size/size distribution measurements coupled with the calculation of crystallites reveal that as ionization energy increases, the size distribution of sNOs increases and the calculated internal stress within the sNO increases and additional crystal orientations of Si become observable. This indicates that as the ionization energy of the laser pulses increase, more sNOs of larger size are generated that have more variety of crystallites. This indicates that more subnanograin boundaries are present within the sNOs generated at high ionization energy than at low ionization energy and result in a higher residual stress within the sNO and a greater concentration of grain boundaries within the sNO structure. This result shows that ionization energy plays a direct role in the nQS-grain boundary disorder of the generated sNOs. The Raman spectra of with these Si nanomesh structures give key morphological characteristics of the sNO sizes and the stress present within this nanostructure. The residual stress within the disordered nanomeshes can be identified and calculated by determining the shift in the positions of Raman peaks associated with Si. Residual stress in a crystal structure can manifest as a positive or negative in the peak position depending on the type of stress present in the nanostructure, either compressive or tensile respectively.61 To calculate the residual stress in the nanostructures, the following equation is used: 𝜎(𝑀𝑃𝑎) = −4.35𝑀𝑃𝑎 × (𝜔 − 𝜔0 ) (Equation 5)



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Where σ is the calculated stress in MPa, ω0 is the observed peak position of the Si wafer substrate and ω is the observed peak shift of the nanomesh structures. The value 4.35 MPa corresponds to the stress sensitivity of silicon. Figures 3e, 4d are plots of these values for each nanomesh. Figure 4e illustrates that the nQS-grain disorder within the nanomesh is significantly higher at high ionization energy than at low ionization energy. This substantial increase in the stress in confirmed by the HRTEM images in Figure 5; a higher density of nQS-grains are observed in the sNO structure than in sNOs formed at lower ionization energy. Each of the above factors contribute to the observed Raman activity in specific ways that have cooperative effects. We theorize that the increased particle size distribution of the Si sNOs and the nQS crystallite size results in a greater number of large sNOs with smaller crystallites. Previous research has shown that a similar pattern of EF decrease as crystallite size increases for crystalline Si. This increased density of crystallites within individual sNOs, induces more residual stress in the sNOs, resulting in both a larger concentration of nQS-grain boundaries within the sNOs. These multiplicative factors working simultaneously result in the observed enhancement of the Raman signal from the Si nanomesh. Surface nQS-void defects An evaluation of the surface nQS-voids produced under oxygenated ion-plume conditions presents complications due to the very small scale of the nQS-voids on the sNOs surface. As observed from the HRTEM images, the nanovoids are ~2nm in scale, and as such quantifying the nanovoid density from these nQS-voids is beyond this present study. A qualitative investigation however is useful for illustrating the observed results. The HRTEM images in Figure 5 show that at high ionization energy, the nQS-voids are significantly dense on the surface of the sNOs. The nQSvoids observed on the sNOs formed at low ionization energy tend to be broader and less dense on the sNO surface. A more in-depth analysis of the nQS-void concentration and its role in nanobiosensing is an important topic for a future study. The HRTEM images reveal that the sNOs formed under oxygenated ion-plume conditions, similar to those formed under inert ion-plume conditions, are within the defined nQS regime, ~10nm for both high and low ionization energies. Since the HRTEM images did not reveal observable nanograins, an evaluation of the crystallite size for these nanomesh structures was not performed. This lack of observable crystallites is confirmed though calculation of the residual stress within the sNOs; the calculated stress is


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approximately constant at both high and low ionization energies and remains lower than the stress observed in the disordered nQS-grain boundary nanomeshes. SERS Biosensing Activity of Si nanomesh structures The Raman active behavior of the nQS defect-rich Si nanomeshes were examined to determine the Raman activity effects of the observed nQS disorders present within each nanomesh. In order to determine how these types of nQS disorders effect Raman activity at different wavelengths, 785nm and 532nm. The Raman spectra of the Si nanomeshes are shown in Figure 6. The spectra in Figure 6 are evaluated by the observed peak at 520cm-1 which is due to the LO phonon mode associated with crystalline Si.62

Figure 6: Raman spectra of the Si nanomesh a) generated under oxygenated ion-plume conditions @785nm, b) oxygenated ion-plume conditions @ 532nm, c) inert ion-plume conditions @785nm and, d) inert ion-plume conditions @785nm



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The spectra in Figure 6 show that the Raman activity of the nanomesh structures is highly dependent both the type of nQS-disorder within the nanomeshes are formed and the wavelength of the Raman laser used to acquire the spectra. These spectra reveal that the type of defect present within the nanomesh structures plays a significant role in the Raman activity of the nanomesh and the Raman activity caused by the sub-nano disorder is wavelength dependent. The Raman activity of these nanomeshes are quantified in Figure S1 using Equation 6 𝐸𝐹𝑛𝑎𝑛𝑜𝑚𝑒𝑠ℎ =

𝐼𝑛𝑎𝑛𝑜𝑚𝑒𝑠ℎ 𝐼𝑤𝑎𝑓𝑒𝑟

(Equation 6)

Where Inanomesh and Iwafer are the intensities of the 520cm-1 from the Si nanomesh structures and the sc-Si wafer substrate respectively. The EFnanomesh values (Supplementary Figure S3) that the nQS-grain boundary disorder nanomeshes have a notable larger Raman activity compared to the nQS-void nanomeshes for both 785nm and 532nm wavelengths. This indicates that the nQS-disorder in the inert nanomeshes (nQS-grain boundary disorder) exhibits larger Raman activity than the nQS-disorder observed in the oxygenated nanomeshes (surface nQS-nanovoid disorder). This suggests that the increased nQS-grain boundary disordered is causing an increase in the vibrational activity of the LO phonon mode of within Si that is responsible for the 520cm-1 peak.62 However, these spectra do show consistency regardless of conditions and wavelength of laser used; the Si peak from the sc-Si wafer is always than the peak from the nanomeshes, and the 520cm-1 peaks from the nanomeshes formed at lower ionization energy are lower than the peaks from nanomeshes formed at high ionization energy. These observations demonstrate that the nanomeshes are significantly Raman active compared to the sc-Si wafer substrate and that the higher ionization produces higher Raman activity from the nanomesh structures than the lower ionization energy nanomeshes for both Raman wavelengths. Bioanalyte Enhancement efficacy of nQS disordered nanomeshes The SERS enhancement behavior of the nQS disordered nanomeshes are examined using a biomolecule, l-glutathione (GSH). This GSH biomolecule is classified as a tripeptide biomarker, which is an abundant intracellular biomolecule whose metabolism has been linked to liver disease, cancer growth, and neurodegenerative diseases.63 The detection of GSH at low concentrations


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using SERS biosensing techniques could potentially allow for the early detection of these diseases. GSH is also known to have very low vibrational activity as evidenced by low enhancement factors observed for GSH detection.64 The SERS enhancement capability of the nQS disordered Si nanomeshes are evaluated in this study by measuring the Raman response of GSH in the presence of the nanomeshes at both 785nm and 532nm wavelengths to determine how the observed nQS disorder differences in Raman activity of the nanomeshes play a role in GSH detection. To determine how effective the Si nanomeshes are at enhancing the biomarker molecule GSH, the EF values for the respective nanomesh structures are calculated. To calculate the EF values for the GSH biomolecule, the following equation18 was used: 𝐸𝐹 =

𝐼𝑛𝑎𝑛𝑜𝑚𝑒𝑠ℎ ⁄𝑁𝑛𝑎𝑛𝑜𝑚𝑒𝑠ℎ 𝐼𝑤𝑎𝑓𝑒𝑟 ⁄𝑁𝑤𝑎𝑓𝑒𝑟

(Equation 7)

Where Inanomesh is the intensity of the identified characteristic for GSH on the nanomesh structure and Iwafer is the intensity of this same peak on the sc-Si wafer substrate. Nnanomesh is an estimation of the number of GSH molecules on the nanomesh structures contributing to the Inanomesh value and Nwafer is an estimation of the number of molecules on the sc-Si wafer substrate contributing to the Iwafer value. More information on this EF calculation can be found in the supplementary information section. Figure 7 shows selected Raman spectral data and calculated EF values for the detection of GSH with Si nanomesh structures with nQS-grain boundary disorder.



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Figure 7: a) Raman spectra of GSH peaks @785nm, b) calculated EF values for the 1255cm-1 peak @785nm wavelength, C) Raman spectra of GSH peaks @532nm, d) calculated EF values for the 1419cm-1 peak @532nm wavelength Figure 8 shows selected Raman spectra for GSH solutions (10-6M and 10-9M) on disordered Si nanomesh structures with surface nQS-voids and the calculated EF values.


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Figure 8: a) Raman spectra of GSH peaks @785nm, b) calculated EF values for the 1255cm-1 peak @785nm wavelength, C) Raman spectra of GSH peaks @532nm, d) calculated EF values for the 1419cm-1 peak @532nm wavelength The spectra in Figure 7 and 8 show SERS detection of the GSH biomolecule in the presence of the nanomesh structures at both 532nm and 785nm Raman laser wavelengths. This indicates that these nanomeshes are activating distinct vibrational modes of the GSH molecule and demonstrates that the 3D nanomesh morphology and nQS disorder dictates the SERS enhancement of the GSH and varies based on the Raman laser wavelength. The observed peaks of GSH on the nanomesh structures are displayed in Table 1 along with peaks assignments for the peaks corresponding to literature. In Figure 7, it can be seen that the nanomesh enhances different vibrational modes



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depending the wavelength of Raman laser and as such the chosen characteristic peak differs between 532nm spectra and 785nm spectra. For this reason, the 1419cm-1 for 532nm spectra and the 1255cm-1 peak for 785nm spectra were chosen respectively. These observed peaks for GSH are in good agreement with the literature. 50, 65,66 For SERS detection of GSH with NMNs, Lee et.al67 have observed three GSH peaks at 10µm concentration and Huang et.al64 have observed nine peaks of GSH at 10µm concentration for Au nanoparticles on Si nanospheres coated with a Ag nanofilm and Ag nanoparticles in colloidal solution, respectively. Ten major peaks are observed for GSH at 1µm concentration at 532nm wavelength and two major peaks for GSH at 1µm concentration at 785nm wavelength. As a result, the Si nanomesh structures are able to activate a comparable number of vibrational modes of the GSH molecule without plasmonic nanostructures. The observed peaks for GSH identified by these spectra are displayed in Table S1. Figure 8b, d shows a similar trend to the EF values observed in Figure 7, the nanomesh composition and morphology play a pivotal role in the SERS activity of the nanomesh and the enhancement of the GSH spectral peaks. These plots demonstrate that the defect-rich nanomesh structures are able to achieve a significant enhancement of the GSH biomolecule. The results coincide with the observations of the Raman activity of the nanomeshes; nQS-disordered nanomeshes achieve an observed enhancement across all ionization energies with the nanomeshes formed at the highest ionization energy demonstrating the largest observed enhancement. The EF values reveal that the extent to which the SERS enhancement of GSH with the nanomesh structures has a dependence on the wavelength of Raman laser. In Figure 7a) the observed GSH EF values follow a similar trend observed for the EF from the nanostructures, the nanomesh with nQS-grain disorder produce a higher enhancement than the nanomeshes with nQS-voids at both 10-6M and 10-9M concentrations. This effect is attributed to the nQS-grain boundary disorder within the individual sNOs of the nanomesh structure. The nQSgrain boundaries acts as intense Raman scattering centres and through the CT resonance mechanism, the vibration activity of GSH is increased. The high density of nQS-grains within the sNOs causes this significant enhancement in GSH spectral response. These spectra and calculated EF values show that the Si nanomesh structures with surface nQSvoids are able to enhance the detection of the GSH analyte, and similar to the Si nanomeshes with nQS-grain disorder the SERS activity is highly dependent on the ionization energy of the ion-


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plume and the Raman laser wavelength. The observed peaks of GSH on the nanomeshes with surface nQS-voids coincide with those observed with peak observed from Figure 8. From the HRTEM images in Figure 4, the nanomesh structure with surface nQS-voids have no observable grain boundary structure leading to these structures relying on the nQS-voids and nQS of the individual sNOs as the primary SERS enhancing source. It is proposed that this significant increase in the SERS enhancement of 10-9M GSH with the surface nQS-void nanomesh structure is due to a concept termed “CT nanogaps”. The nQS-voids observed on the sNO surface are posited to act as traps or wells for the analyte molecules. It is suggested that at this 10-9M GSH concentration, the GSH molecules when trapped within the nQS-voids on the sNO surface are more efficiently able to produce a CT resonance effect than compared to the 10-6M GSH solution. A visualization of the nQS-grain disorder and surface nQS-void contributions to SERS detection of the GSH molecule is shown in Figure 9.

Figure 9: Visualization of the SERS enhancing effects caused by a) nQS defects, nQS grain boundary disorder defects and b) nQS-voids on sNO surface As discussed, the ionization energy has a significant effect on the SERS activity of the nanomesh structures and thus the detection of the GSH molecule. From the characterization results in Figures



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3 and 4 it is posited in this study that the higher ionization energy under both inert and oxygenated ion-plume conditions yields a higher density of nQS disorder, either nQS-grain boundary disorder or surface nQS-voids compared to the nanomeshes formed at lower ionization energy. From Figures 7 and 8 it can be seen that there is a variance in the SERS enhancement of GSH signal that is contingent upon which form of disorder is causing the SERS activity. The nQS-grain defects achieve a higher EF for GSH at 785nm than the surface nQS-voids, yet the surface nQSvoids are able to achieve a much higher EF for GSH at 10-9M concentration than the surface nQSgrain defects. This variance in SERS activity of these nQS disorders that depends on the Raman laser wavelength can be applied to the detection of biomolecules in both in vivo and in vitro applications.13 As such, the SERS enhancement can be tuned to detect biomolecules specific for applications by creating nanomeshes with specific nQS defects. Towards universal sensing with disordered Si nanomeshes To determine the potential of the nQS disordered Si nanomesh structures as a universal SERS nanoprobe material, it is necessary to determine if the nanomesh structures are able to detect additional biomolecules beyond GSH. In this study, biomolecule analytes, tryptophan (Trp), cysteine (Cys) and methionine (Met) were tested for SERS response in the presence of the nQS disordered Si nanomesh structures. These biomolecules were chosen because of their relevance as signalling molecules for diseases. Tryptophan was chosen due to its close relation to HIV infections and neurological disorders.68 Cysteine was chosen due to heightened levels within the body being linked to cardiovascular diseases and neurotoxicity.69 Methionine was chosen due to its role in determining liver function.70 These biomolecules are similar to GSH these biomolecules are known to have a very low Raman cross-section which makes them difficult to detect using SERS spectroscopy. The results from the study of GSH have shown that the higher ionization energy yields nQS defects that produce the largest enhancement. As such, for this study nanomeshes formed at higher ionization energy are used to enhance the spectra of the biomolecules. Additionally, the SERS spectra of the biomolecules were obtained using 532nm Raman laser wavelength because of the greater number of peaks which allows for more characteristic peaks for the biomolecules to be distinguished. Figure 10 shows the SERS spectra of the Cys, Trp and Met biomolecules on the disordered nanomesh and the sc-Si wafer substrate at both 10-6M and 10-9M concentrations.


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Figure 10: Raman spectra of a) Cys (10-6M), b) Cys (10-9M) c) Trp (10-6M) d) Trp (10-6M) e) Met (10-6M) and f) Met (10-9M) on nanomeshes with (pink) QS-grain boundary defects, (blue) surface nQS-voids and on (grey) defect free sc-Si wafer substrates These spectra show that these biomolecules also exhibit greatly enhanced spectra in the presence of the disordered nanomeshes. Both the nanomeshes with nQS-grain disorder and surface nQSvoids are able to enhance the SERS spectra of Cys, Trp and Met. It is also observed that the type



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of defects present within the nanomesh can activate the vibrational modes of a biomolecule that cannot be activated by the other type of nQS defect. For instance, the nQS-grain boundary disorder is able to activate the benzene/pyrrole ring stretching mode of Trp while surface nQS-voids cannot. Conversely, surface nQS-voids can active the asymmetrical CH2 stretching mode of Met, while nQS-grain disorder cannot. The observed peaks for these biomolecules identified by these spectra are displayed in Tables S2-S4. The detection of these distinct biospectra with both types of nanomesh disorder demonstrates the potential use of this unique Si nanostructure as a universal bioprobe material that can be used for early detection of disease signalling biomolecules. Conclusion: In this study it has been shown, for the first time to our knowledge, that the controlled synthesis of a non-plasmonic Si-only nanostructure with nQS defects, exhibits unique universal SERS based disease signal detection functionality. Through a laser-ionization based self-assembly technique, a 3D Si-only nanostructure is formed from fused Si sNOs and that by influencing ionization energy and ion-ion interactions, nQS defects can be engineered within the individual sNOs. The nQS defects manifest as a result of this controlled engineering and are classified as disordered nQSgrain boundaries within the sNOs and as disordered nQS-voids on the sNOs surface. It has been shown, for the first time, that the morphology of these nQS defects can be directly engineered and by doing so, we can induce changes in the Raman activity of the resultant 3D nanomesh structures and activate the CT SERS enhancing characteristics of these Si-only nanostructures. We have demonstrated that nQS defects within a 3D Si nanomesh structure exhibit substantial SERS biosensing functionality without an additional noble metal plasmonic material and has significant potential for use as a universal SERS sensing nanoprobe for early stage disease signalling. These nQS defect-rich nanostructures have demonstrated substantial enhancement of multiple disease signalling bioanalytes (GSH, Trp, Cys and Met) at very low concentrations; a phenomena, to the best of our knowledge that has yet to be reported. These results establish a potential new dimension to advanced non-plasmonic universal SERS biosensing of disease signalling biomolecules through engineering silicon at the quantum scale.


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Supporting Information: Detailed calculations of enhancement factor (EF) and ionization coefficient (α), calculated EF for nQS grain boundary defects and nQS-void defects @785nm and 532nm Raman wavelengths, observed Raman peaks of GSH, Cys, Trp, and Met on Si nanomeshes @785nm and 532nm wavelengths. Supplemental data regarding Si nanomeshes created with α=7.2x1013 including particle size distributions, Raman peak spectra @785nm and 532nm, EF of Si nanomeshes, Raman spectra of GSH on nanomeshes, and calculated EF values for GSH on α=7.2x1013 nanomeshes. This material is available free of charge via the Internet at http://pubs.acs.org. Author Contribution: Dr. Jeffery A. Powell, Dr. Krishnan Venkatakrishnan and Dr. Bo Tan worked together in designing the project. Dr. Jeffery A. Powell performed the experiments and wrote the manuscript. Dr. Krishnan Venkatakrishnan and Dr. Bo Tan assisted in results, discussion and editing the manuscript. Sara Azimi provided assistance in drawing schematics. This research was funded by NSERC Discovery Grant 132950,134361. Competing financial interest: The authors declare no competing financial interest. References:

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ABSTRACT IMAGE: The descent to QS though fabrication of a a) 3D Si nanomesh structure consisting of b) fused nQS sNOs with c) engineered nQS defects in the forms of grain boundary disorder and surface voids


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ABSTRACT IMAGE: The descent to QS though fabrication of a a) 3D Si nanomesh structure consisting of b) fused nQS sNOs with c) engineered nQS defects in the forms of grain boundary disorder and surface voids 294x448mm (300 x 300 DPI)



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Figure 1: a) nQS structure and defects as a means of universal detection of disease signalling biomolecules b) Raman spectra associated with l-glutathione on the Si nanomesh structures and Raman spectra of biomolecules c) cysteine (cys), d) tryptophan (trp) and e) methionine (met) on Si nanomesh structures 335x409mm (300 x 300 DPI)


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Figure 2: a) femtosecond laser ionization of a defect-free silicon wafer, b) sNO ejection from ion plume, c) sNOs fuse together on Si wafer surface to form d) 3D nanomesh e) SEM and HRTEM images of nanomesh morphology and Si sNOs 313x208mm (300 x 300 DPI)



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Figure 3: a) HRTEM analysis of nQS-defects within sNO formed under inert-ion plume conditions and associated b) XRD spectra, c) sNO size distribution d) calculated crystallite size and e) calculated residual stress 348x421mm (300 x 300 DPI)


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Figure 4: a) HRTEM analysis of nQS-defects within sNO formed under oxygenated ion plume conditions and associated b) XRD spectra, c) sNO size distribution d) calculated residual stress 360x415mm (300 x 300 DPI)



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Figure 5: TEM and HRTEM images of nQS disordered Si nanomesh structures formed under a) inert ionplume conditions, b) oxygenated ion-plume conditions and the observed nQS-defects c) nQS grain disorder within the sNO structure and d) nQS-voids on sNO surface 452x190mm (300 x 300 DPI)


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Figure 6: Raman spectra of the Si nanomesh a) generated under oxygenated ion-plume conditions @785nm, b) oxygenated ion-plume conditions @ 532nm, c) inert ion-plume conditions @785nm and, d) inert ionplume conditions @785nm 163x137mm (300 x 300 DPI)



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Figure 7: a) Raman spectra of GSH peaks @785nm, b) calculated EF values for the 1255cm-1 peak @785nm wavelength, C) Raman spectra of GSH peaks @532nm, d) calculated EF values for the 1419cm-1 peak @532nm wavelength 174x139mm (300 x 300 DPI)


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Figure 8: a) Raman spectra of GSH peaks @785nm, b) calculated EF values for the 1255cm-1 peak @785nm wavelength, C) Raman spectra of GSH peaks @532nm, d) calculated EF values for the 1419cm-1 peak @532nm wavelength 170x139mm (300 x 300 DPI)



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Figure 9: Visualization of the SERS enhancing effects caused by a) nQS defects, nQS grain boundary disorder defects and b) nQS-voids on sNO surface 281x185mm (300 x 300 DPI)


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Figure 10: Raman spectra of a) Cys (10-6M), b) Cys (10-9M) c) Trp (10-6M) d) Trp (10-6M) e) Met (10-6M) and f) Met (10-9M) on nanomeshes with (pink) QS-grain boundary defects, (blue) surface nQS-voids and on (grey) defect free sc-Si wafer substrates 260x363mm (300 x 300 DPI)


Toward Universal SERS Detection of Disease Signaling Bioanalytes

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