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Indexing the Quality of Single-Wall Carbon Nanotube Dispersions Using Absorption Spectra Yu Zheng, Stephen R. Sanchez, Sergei M. Bachilo, and R. Bruce Weisman J. Phys. Chem. C, Just Accepted Manuscript • DOI: 10.1021/acs.jpcc.7b12441 • Publication Date (Web): 05 Feb 2018 Downloaded from http://pubs.acs.org on February 13, 2018

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The Journal of Physical Chemistry C is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

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The Journal of Physical Chemistry

Indexing the Quality of Single-Wall Carbon Nanotube Dispersions using Absorption Spectra Yu Zheng,a Stephen R. Sanchez ,a Sergei M. Bachilo,a and R. Bruce Weisman*,a,b a

b

Department of Chemistry and the Smalley-Curl Institute, Rice University, 6100 Main Street, Houston TX 77005, USA. Email: [email protected]; Tel: 713 348 3709 Department of Materials Science and NanoEngineering, Rice University, 6100 Main Street, Houston, TX 77005, USA.

ABSTRACT: We have found that absorption spectra of single-wall carbon nanotube (SWCNT) dispersions can be accurately represented as linear combinations of two underlying spectra. One, assigned to purified well-dispersed SWCNTs, is strongly structured; the other, assigned to impurities and aggregates, is diffuse. To assess the quality of SWCNT dispersions, a small set of visible and short-wave IR spectra are first measured for a dispersed sample as it is purified by centrifugation. Those data are analyzed using a simple arithmetic process to estimate the pair of underlying component spectra. Then the spectra of other sample dispersions from the same SWCNT source are fit as linear combinations of the two component spectra, giving coefficients that provide a simple metric for sample quality (the S-index). We have validated this approach with measurements of fluorescence intensities, particle abundances, length distributions, and emissive aggregates in samples containing SWCNTs from three growth sources dispersed by high-shear mixing or tip-sonication and then purified by moderate centrifugation, ultracentrifugation, or magnetic processing. Our absorption spectral analysis correlates closely with results from other characterization techniques. One unexpected outcome of our analyses is the estimate that no more than 7% by mass of raw SWCNT material used to prepare aqueous sodium deoxycholate suspensions becomes well-dispersed SWCNTs.

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1. INTRODUCTION Single-wall carbon nanotubes (SWCNTs) are a family of artificial tubular nanostructures with extraordinary physico-chemical properties and a range of potential applications.1-9 However, many uses, including some in bio-imaging, bio-sensing, photonics, and electronics,3,1012

require purification of the as-grown SWCNT material, which is generally aggregated into

bundles bound by van der Waals forces and contains significant amounts of carbonaceous impurities, residual metal catalyst particles, and nanotubes with structural or chemical imperfections. Processing begins with disaggregation into individualized SWCNTs that are stabilized against re-aggregation, typically by dispersing the solid material into liquid media with the aid of surfactant or polymer coatings.13-21 Dispersal is generally achieved through ultrasonic agitation or high-shear mixing in aqueous surfactant solutions. The most common physical purification methods then use centrifugation or magnetic devices to remove impurities and aggregates.22-30 A key need in SWCNT purification is efficient characterization to assess sample condition during processing. Because of its simplicity and relatively low cost, absorption spectroscopy is an appealing tool for this purpose.31-37 Previous workers, including Itkis et al. and Tan et al.,38,39 therefore introduced absorption-based measures of SWCNT sample purity. Both of those approaches are based on distinguishing the structured spectrum of purified SWCNTs from the broad absorption attributed to other sample components.40 However, each has limitations, as the Itkis method is appropriate for samples with large average diameters and strongly overlapped E11S transitions, whereas the Tan method was developed for the narrow range of small diameters present in samples grown by the CoMoCAT process.41 We present here a new characterization method, also based on absorption spectroscopy, with broader applicability. It represents a sample’s spectrum as a superposition of two separate, 2


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systematically determined basis spectra representing pure and impure components. The fitting coefficients then provide an accessible and objective metric for sample quality that we have validated against independent measurements of fluorescence intensity, particle concentrations, length distributions, and aggregate content. We demonstrate that the method can be successfully used to characterize samples grown by the HiPco and CoMoCAT processes, which have diameter distributions with quite different mean values and widths. Our study also traces the changes in these properties as samples dispersed by high-shear mixing or tip-sonication are purified by moderate centrifugation, ultracentrifugation, or magnetic processing.

2. EXPERIMENTAL METHODS 2.1 SWCNT materials SWCNTs used in this study were from two batches (numbers 195.1 and 189.1) grown in the Rice University HiPco reactor plus a commercial sample of CoMoCAT material (type SG65) from SouthWest NanoTechnologies, Inc. 2.2 Sample dispersion For the tip-sonicated samples, 5 mg of raw HiPco SWCNTs was added to 15 mL of 1% aqueous sodium deoxycholate (SDC) solution. The sample was dispersed using tip sonication at an output power of 3 or 5 W (3 mm tip, Misonix Microson XL) for 15 active minutes (45 min with duty cycle of 20 s on, 40 s off) while immersed in a room temperature water bath. For the CoMoCAT sample, 0.9 mg of raw material was dispersed by 30 active minutes of 5 W tip sonication (90 min with duty cycle of 20 s on, 40 s off) in 10 mL of 1% aqueous SDC. To prepare the shear dispersions, we added 10 mg of raw HiPco SWCNTs to 15 mL of 1% aqueous SDC and mixed with a high-shear homogenizer (IKA Ultra-Turrax T8) for 2 h at a low- to midrange speed setting.

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2.3 Sample processing Each SWCNT suspension was divided into three aliquots to compare the effects of purification by moderate centrifugation, ultracentrifugation, and magnetic purification. For the first method, we centrifuged the SWCNT samples at 13,000 g in a Biofuge-13 (Baxter Scientific). After 15, 30, 60, 90, and 120 min, we carefully removed approximately 0.5 mL of the supernatant and diluted it with 1% SDC for characterization. For ultracentrifugation purification, we centrifuged samples at 268,000 g (50,000 rpm) for 2 and 4 h in a MLS-50 swing-bucket rotor in an Optima MAX-130k (Beckman Instruments). The supernatants at each time point were sampled and diluted by the same factor as the moderately centrifuged aliquots. For static magnetic purification, dispersed samples were placed in a 20 mL vial containing stacked nickelplated N48 neodymium magnets. The solution remained in contact with the magnets before sampling at 1, 3, and 7 days (shear-dispersed samples) or 1, 3, and 6 days (tip-sonicated samples). Magnets were removed and cleaned with DI water after 1 and 3 days. The purified solution near the magnets was sampled at each time point and diluted by the same factor as for the centrifuged dispersions. 2.4 Sample characterization Absorption and short-wave infrared (SWIR) fluorescence spectra were measured in 1 cm path length cells using a prototype model NS2 NanoSpectralyzer (Applied NanoFluorescence, LLC). SWIR fluorescence spectra were excited by diode lasers emitting at 642, 659, and 784 nm. We measured variance spectra on a modified version of the step-scan apparatus described in our previous publications.42,43 Samples were loaded into a demountable cuvette with a 100 µm optical path length. The fluorescence excitation source was a 660 nm diode laser (Power Technologies, Inc.) filtered by a pair of short-pass filters (875 and 1100 nm, Edmund Optics). We calculated mean emission and variance spectra from 2000 spectra (each integrated for 200 4


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ms) taken from spatially independent regions of the sample. The computation included two corrections at each spectral channel and accounted for optical artifacts from the cuvette by omitting emission readings more than 5σ from the raw mean value for that channel. Additionally, to correct for any slow time-dependent emission changes, we smoothed the fluorescence traces using a local regression model with a 2nd degree polynomial. These corrections were also used in covariance analysis with the exception that the threshold for omitting emission readings was reduced to 3σ from the raw mean value. We measured SWCNT length distributions using the previously reported method of length analysis by nanotube diffusion (LAND).44 In this method, SWIR fluorescence microscopy is used to observe the trajectories of SWCNTs in liquid suspension as they undergo translational diffusion. Prior to measurement, our SWCNT samples were heavily diluted by 1% SDC and sorbitol powder was added to raise solution viscosities to between 12 and 30 mPa·s in order to better track the motions of shorter SWCNTs. Fluorescence was excited by the beam from a cw Ti:sapphire laser (Del Mar Photonics) tuned to the 840 nm (6,5) sideband absorption. Images were captured with 100 ms exposure times through a pair of interference edge filters (975 LP and 1000 SP) by a liquid nitrogen-cooled InGaAs camera (Roper Scientific OMA-V 2D) mounted on an inverted microscope (Nikon TE-2000U) equipped with a water-immersion objective (Nikon PlanApo 60x, 1.27 NA). After the diffusion coefficients found from individual particle trajectories were converted to nanotube lengths, the first and second moments of the length distribution were calculated to obtain the mean and standard deviation values. 2.4 MCR-ALS analysis To help validate our absorption spectra analyses, we used the method of Multivariate Curve Resolution by Alternating Least-Squares (MCR-ALS), as implemented in MATLAB code by the 2004 version of MCR-ALS GUI.45 5



3. RESULTS AND DISCUSSION 3.1 Decomposing the absorption spectra We observed that the absorption spectrum of a typically prepared SWCNT dispersion changes systematically during centrifugation. This effect is illustrated in Figure 1a for a sample of HiPco SWCNTs in aqueous sodium deoxycholate (SDC). Initially, our samples contain some unstably dispersed material including visible particles. During the first 15 min of moderate centrifugation (13,000 g), those macroscopic particles are removed from the suspension, leaving a stable, visually clear sample. At this point the absorption spectrum shows a diffuse background underneath distinct van Hove peaks from E11S, E22S, and E11M transitions. Further centrifugation suppresses the broad background more than the resonant features. This suggests the possibility of modeling the sample spectra as mixtures of only two components. One is the set of sharp resonant features associated with SWCNTs that are suspended individually or in small bundles. We label this “S”. The other component, labeled “B”, is a broad absorption from stably dispersed material, possibly including carbonaceous impurities, imperfect nanotubes, and larger SWCNT aggregates. We hypothesize that the absorptions of our centrifuged samples can be modeled as a linear combination of just two basis spectra, S and B. To test this hypothesis, we used the following method, equivalent to Gaussian elimination, to deduce the basis spectral profiles. For B, we took the spectrum measured after 120 min of moderate centrifugation, multiplied it by an adjustable factor, and subtracted that from the spectrum measured after 15 min of centrifugation. The factor was then manually adjusted to obtain the smoothest spectral profile, which was used as the B spectrum. To confirm that this spectrum is consistent with those of nanotube aggregates, we prepared a SWCNT dispersion in sodium octyl sulfate (SOS), known to be a poor surfactant for nanotubes. This sample contained visible aggregates and gave only very weak fluorescence. Its absorption spectrum closely 6


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matched the deduced B spectrum, except for small deviations attributed to differences in light scattering and surfactant induced-shifts (see Figure S1). This agreement supports the association between the B spectrum and aggregates. To deduce the S profile, we took the 120 min spectrum and subtracted a factor times the B spectrum, choosing that factor so that minima in the S profile were nearly zero (but positive). We note that S profiles constructed in this way will not accurately reflect the spectra of clean, fully dispersed SWCNT mixtures because spectral congestion from overlapping peaks prevents their absorbance minima from reaching zero. The effect of offset uncertainty on our dispersion quality metric is addressed below. More generally, there is no unambiguous method for deducing overlapped component spectra except with the aid of constraints based on additional knowledge or assumptions. Here we constrain S and B to be non-negative and assume that B has minimal spectral structure and that valleys in the S spectrum can approach zero. After constructing these S and B profiles, we examined whether their linear combinations represented by eq 1 could fit the set of absorption spectra measured during the sample’s moderate centrifugation.

A ( λ ) = α AS ( λ ) + β AB ( λ )

(1)

We deduced coefficients α and β using closed-form least-squares expressions (see Section 7 of Supporting Information) for the spectral fits plotted in Figure 1b with residuals in Figure 1c. The mean absolute absorbance errors in fitting the 30, 60, and 90 minute spectra were 0.0021, 0.0011, and 0.0008, respectively. Despite these low values, the presence of small systematic shapes in the residuals suggests that a third basis spectrum might allow more accurate fitting. To test this, we used a publicly available MATLAB program that implements the method of MCRALS (Multivariate Curve Resolution – Alternating Least Squares), 45-47 The program alternately applies nonlinear least squares optimization in the spectral and time regimes to best fit the set of 7



experimental spectra as linear combinations of basis spectra.45,48 We analyzed the same data set with two and with three basis spectra, initially estimating the third as a constant function and constraining all concentrations and spectra to be non-negative. MCR-ALS optimization reduced the reported fitting error from 0.117% with two functions to 0.062% with three. The maximum contribution from the 3rd basis spectrum was less than 2%, and the average contribution was well below 1%. We conclude that fitting absorption spectra with only the S and B basis functions provides sufficient accuracy for our goal of assessing dispersion quality. Although these results show that sample components giving S and B remained stable during moderate centrifugation, they may change noticeably during more extensive processing. We checked for this by using the same S and B basis spectra to fit absorption spectra measured during ultracentrifugation and magnetic purification. Those results, plotted in Figures S3 and S4, are not as good as those in Figure 1, but are still accurate within a few percent (see Figures S2, S3, and S4), a level we consider satisfactory considering that the dispersed diameter distributions may change slightly during such processing. We view the approximate fits as adequate for deducing the quality index described below. The simulation of absorption spectra by two components representing well-dispersed (S) and poorly dispersed or impure SWCNTs (B) offers a simple metric for judging sample quality during processing. Figure 2 plots the variation of α and β coefficients with centrifugation time, along with the ratio α / (α + β), which we call the “S-index.” This index will be near 0 for samples dominated by aggregates or impurities, and can reach 1 if only spectrally sharp components are present. In the data of Figure 2, the S-index increases monotonically during moderate centrifugation because β, the spectrally broad coefficient, decreases much more rapidly than α, the sharp coefficient, leading to purification of the supernatant. Table 1 lists the deduced spectral coefficients and S-index values for this sample. Some uncertainty is introduced into the 8


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S-index by assuming that the minimum values in the S basis spectrum approach zero. To estimate the size of this error, we used a highly purified sample to deduce a more realistic minimum for the S basis spectrum and found that it gave S-index values higher by 2 to 3%. We consider that this level of systematic error is acceptable in a simple quality metric, justifying the use of S basis spectra constructed with near-zero minima. 3.2 Characterizing the well-dispersed fraction To better understand the nature of the well-dispersed fractions, we applied the recently developed technique of variance spectroscopy. This analytical method allows determination of relative particle abundances, emission efficiencies, and emissive aggregates by measuring wavelength-dependent fluorescence intensity fluctuations. A detailed description of variance spectroscopy has been presented previously.42,49 In brief, we capture several thousand emission spectra from separate small regions of a liquid sample held in a thin cuvette. Since we probe a very small volume (on the order of picoliters), statistical differences in the number of emitting particles within that volume are large enough to cause observable variations in the emission spectra. To find N ( λ ) , the number of particles emitting at wavelength λ, we analyze a set of ca. 2000 emission spectra to obtain the mean emission spectrum, I ( λ ) , and the variance spectrum, σ 2 ( λ ) . Using these values, we express the number of particles within our probed volume as:

N (λ ) = I 2 (λ ) σ 2 (λ ) Figure 3 shows that both the emission intensity and the variance of the supernatant decreased with increasing centrifugation time. We carefully analyzed the five well-resolved spectral features from nanotube species (8,3), (6,5), (7,5), (7,6), and (8,6), as marked in the figure, to measure the effect of the three different purification methods on those spectrally sharp (S) 9



subpopulations. We found that the decreases in emission intensity were independent of nanotube structure (see Figure 4a,c). More importantly, the particle concentrations of all five nanotube species also decreased during centrifugation (Figure 4b,d). By combining these variance results with absorption analysis, we deduce that the decreases in emission intensity and in the S absorption component are directly related to the loss of emissive, mostly individualized particles. Using the LAND (length analysis by nanotube diffusion) method,44 we measured the length distribution of the SWCNT sample after 15 and 120 min of moderate centrifugation. These results are plotted in the top frames of Figure 5. The mean and width of the length distribution decreased with additional centrifugation time. We conclude that the decreases in absorption, emission, and particle density are partly due to removal of longer nanotubes from the supernatant. 3.3 Comparing physical purification methods As an application of our new analysis method, we used eq 1 to characterize and compare the absorption spectra of SWCNT dispersions initially prepared by high-shear mixing or tipsonication in 1% SDC as they were processed by moderate centrifugation, ultracentrifugation, or magnetic purification. Qualitatively, we found that the concentrations of the B components decreased markedly and the S-index rose with increasing processing time. For the centrifugation runs, we made baseline measurements after 15 min to allow initial sedimentation of coarse aggregates. Additional measurements of emission intensities, relative particle abundances, and length distributions were performed. The top section of Table 2 shows findings for samples dispersed by high-shear mixing (see Figures S2-S9 and Table S1). Here, 120 min of moderate centrifugation (ca. 13,000 g) gave a 60% further decrease in the broad (B) component, while the spectrally sharp (S) component dropped by only ca. 17%. This was accompanied by ca. 20% drops in emission intensity and 10


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particle abundance and a ca. 10% decrease in mean length. Alternative purification by ultracentrifugation for 2 to 4 h removed ca. 80% of the B component along with 30 to 40% of the S component. Ultracentrifugation therefore provided higher purity suspensions at the expense of greater material loss and a ca. 30% decrease in mean length to 400 nm. The third investigated purification method was static magnetic processing, in which strong magnets were immersed in an undisturbed suspension for various periods. Note that this technique is similar in mechanism but much slower than the previously described rotational magnetic purification method involving gentle flow of the liquid sample over magnet surfaces.30 After 1 day of static magnetic purification, we found spectral coefficients similar to those from 30 to 60 min of moderate centrifugation. Extending the magnetic purification to 3 or 7 days gave absorption spectra that were comparable to those from several hours of ultracentrifugation, but with only a 5% drop in mean length. This result is consistent with our previous magnetic purification findings, which showed similar absorption spectra after 5 h of rotational magnetic purification or 5 h of ultracentrifugation.30 The results thus confirm that magnetic purification is the preferred method for enriching samples in individually dispersed SWCNTs while retaining most of the long nanotubes. We also find that magnetic purification proceeds approximately 30 times faster when the sample is slowly flowing rather than static. The bottom section of Table 2 shows results for comparable measurements on samples dispersed by the more common method of tip-sonication, which typically produces dispersions with shorter and more uniform SWCNT length distributions than high-shear mixing (see Figures S10-S18 and Table S2). After 120 min of moderate centrifugation, ca. 94% of the spectrally sharp component remains and it appears that only a small fraction of the longer nanotubes are lost. Ultracentrifugation gives samples with shorter mean length and narrower length distribution (see Figure S19). This change in length distribution is consistent with decreases in S component 11



absorption, emission intensity, and overall particle abundance. Although it causes approximately twice the decrease in spectrally sharp absorption as moderate centrifugation, ultracentrifugation provides more length-uniform SWCNT dispersions with lower absorption backgrounds. For magnetic purification, we found that the length distribution is essentially identical to that of the aliquot moderately centrifuged for only 15 min. Similar to ultracentrifugation, roughly 13% of the spectrally sharp SWCNT absorption is lost during magnetic purification, likely from removal of small emissive clusters. We note that the values of α obtained from absorption spectral analysis track quite closely with values of emission intensity and particle abundance, confirming that our simple two-component representation of absorption spectra reveals key underlying properties. 3.4 Analyzing for emissive aggregates We have further analyzed our variance spectral data to assess the presence of emissive aggregates in samples dispersed by high-shear mixing and purified by different methods. For this purpose, we computed 2D covariance matrices for each of the purified aliquots. An example of such a covariance matrix is shown as a contour plot in Figure 6a. Our previous studies demonstrated that the off-diagonal features in such plots arise from emissive heteroaggregates in SWCNT suspensions.42 We envision those species as small clusters containing two or more semiconducting SWCNTs of different structural types, interacting weakly enough to avoid major perturbations to their fluorescence emission. To analyze our data for evidence of such aggregates, we plotted a horizontal slice of the 2D covariance matrix at the (7,5) peak emission wavelength (see Figure 6b). Peaks from species other than (7,5) in such plots reveal the presence of heteroaggregates containing (7,5) nanotubes. To quantify their concentrations, we converted the covariance values into scaled Pearson correlation coefficients,42 which denote the fractions of particles of different (n,m) species aggregated with (7,5) SWCNTs. As shown in the plot of 12


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Figure 6c, baseline measurements made after 15 min of moderate centrifugation reveal large numbers of emissive aggregates. This most likely reflects the sample’s initial preparation by high-shear mixing, which is a relatively mild treatment that is less effective than sonication in disaggregating and debundling raw nanotubes. Subsequent moderate centrifugation, magnetic purification, and ultracentrifugation significantly reduced the relative abundances of emissive heteroaggregates. Of these methods, ultracentrifugation was the most effective, removing ca. 40 to 60% of baseline emissive aggregates containing the (7,5) species, and magnetic purification was the next most effective. 3.5 Assessing purification differences related to SWCNT growth method We have also used our new absorption analysis and other characterization methods to monitor the purification of SWCNT samples produced by different growth techniques. We compared the effect of centrifugation on CoMoCAT SG65 samples dispersed into 1% aqueous SDC by 5 W tip sonication, and on two different HiPco SWCNT batches (195.1 and 189.1) dispersed by 3 W or 5 W tip-sonication or by high-shear mixing (see Figure S20 and Table S3). Figure 7 shows values for the S and B spectral coefficients for each prepared sample measured after five durations of moderate centrifugation. The B absorption, shown in Fig. 7b, decreased similarly for the two HiPco batches prepared by the different dispersion methods. However, the B absorption for the CoMoCAT sample decreased more gradually with centrifugation time. This difference may reflect a lower content of dense impurities in raw CoMoCAT compared to our raw HiPco material. Changes in the S absorption coefficient are plotted in Figure 7a. This component dropped by no more than ca. 5% in all three dispersions that were prepared with 5 W tip sonication. We suspect that this small change reflects trimming of the nanotube length distributions during the relatively harsh sonication, limiting reductions in S absorption through loss of long nanotubes during subsequent centrifugation. By comparison, samples that were more 13



gently dispersed using 3 W tip sonication lost ca. 13% of their S absorption component during the same centrifugation. Shear-dispersed samples showed the largest loss of S component during centrifugation, probably because their initial length distributions had large mean values and broad widths. 3.6 Estimating dispersion efficiencies Although the α and β parameters are valuable for comparative sample characterization, they do not directly indicate mass concentrations. To access such information, we have computed the spectrally integrated near-IR absorbance, from 915 to 1350 nm, for the S and B components of a HiPco batch 195.1 sample dispersed by 5 W tip sonication. The results, plotted in Figure 7c, show a relatively small integrated S component, suggesting low efficiency of full nanotube dispersion. The S integral remains nearly stable during moderate centrifugation while the spectrally integrated B component declines to less than half its baseline value as aggregates and impurities are removed from the supernatant. We also calculated the ratio of S to S+B integrated absorbances, as plotted in Figure 7d. This value provides a measure of dispersion quality similar to the S-index described earlier. Thus, the increasing values in Figure 7d reflect increasing relative concentrations of well-individualized SWCNTs. We find higher values in samples dispersed by tip-sonication instead of high-shear mixing. These ratio values are also higher for CoMoCAT than for HiPco samples. It is unclear how much this reflects differences between the two sources in SWCNT diameter distribution, semiconducting fraction, impurity content, or defect density. We also observed differences between the two HiPco batches (see Figures 7d and Table S3) that are likely related to the higher content of residual iron catalyst in batch 189.1 (~36%) compared to batch 195.1 (~19%). Finally, we used the integrated absorbance measurements to quantify the mass concentration of spectrally sharp SWCNTs in dispersed samples processed by 2 h of moderate centrifugation. 14


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For this we applied the Beer-Lambert Law using an effective integrated absorptivity value for SWCNT samples over the 7400 to 10930 cm-1 (915 to 1350 nm) spectral range. This value was computed as a weighted average of previously reported (n,m)-resolved absolute absorptivities (see the Supporting Information for details).43,49 The resulting deduced mass of spectrally sharp suspended SWCNTs was divided by the initial mass of solid SWCNTs to obtain a dispersion efficiency. Table 3 summarizes the results. We found that tip sonication is much more efficient than shear dispersion, but it still converts only ~5 to 7% of raw HiPco material into highly dispersed form. Tip sonication of CoMoCAT material is even less efficient, at ~3%, so in all cases a large fraction of the raw SWCNT mass is lost during processing.

4. CONCLUSIONS We have developed a practical new method for indexing the quality of SWCNT dispersions by decomposing absorption spectra into superpositions of a sharp and a broad component. These two “basis spectra” can be quantitatively deduced for SWCNTs of a specific source and surfactant by using a simple arithmetic process to analyze at least two absorption spectra taken as a sample undergoes centrifugation. Then other absorption spectra from the same sample type and surfactant can be readily represented as a combination of those sharp and broad components. The ratio of coefficients for sharp (S) to the sum of sharp and broad (S+B) gives a new quality metric (the S-index) that can range from 0 for strongly aggregated, damaged, or impure samples to 1 for pristine purified dispersions rich in individualized SWCNTs. To correlate the new metric with other sample characteristics, we have monitored emission intensity, particle abundance, length distributions, and emissive aggregates in dispersions prepared from three SWCNT sources using both high-shear and tip sonication and processed by moderate centrifugation, ultracentrifugation, or magnetic purification. High-shear dispersion gives a relatively wide initial length distribution. Longer nanotubes in the distribution are then mostly retained during magnetic processing but removed by ultracentrifugation. Moderate 15



centrifugation reduces the particle concentration and fluorescence emission much more quickly in shear-dispersed than in tip-sonicated dispersions. Covariance spectral analysis shows that ultracentrifugation and magnetic purification are both very effective for removing emissive SWCNT aggregates and give samples with high S-index values. Absorption spectra were also used to make quantitative estimates showing that the fraction of raw SWCNT material converted to well-dispersed form by standard preparation methods is below ca. 7%. Finally, our study demonstrates a strong correlation between the S-index and other measures of dispersion quality. This implies that two-component analysis of simple absorption spectra can assist basic and applied nanotube studies by giving valuable quantitative measures of SWCNT sample condition.

Associated Content Supporting Information Details concerning the absorption spectrum of poorly dispersed SWCNTs; additional absorption and variance analysis of the effects of three purification methods on both shear- and tip-dispersed samples; comparisons between different HiPco batches, additional length distribution data; effect of purification methods on emission efficiencies as determined by variance spectroscopy; the calculation of dispersion efficiency; and analytical formulas for least-squares spectral fitting. This material is available free of charge via the Internet at http://pubs.acs.org.

Author Information Corresponding Author e-mail: [email protected]

Notes The authors declare the following conflict of interest: R.B.W. has a financial interest in Applied NanoFluorescence, LLC, which manufactures an instrument used in this study.

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Acknowledgements This research was supported by grants from the National Science Foundation (CHE1409698) and the Welch Foundation (C-0807). S.R.S is grateful to the National Science Foundation for support through an AGEP-GRS award (CHE-1549024). We thank S. Ghosh for TGA measurements of HiPco iron contents.

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Table 1. Variation of absorption spectral components during 13,000 g sample centrifugation

α (S component)

β (B component)

S-index α/(α + β)

15

1.00

1.95

0.34

30

0.94

1.54

0.38

60

0.89

1.16

0.43

90

0.86

0.97

0.47

120

0.83

0.79

0.51

Centrifugation time (min)

Table 2. Dependence of SWCNT sample parameters on processing conditions Dispersion method

Purification method

S-index Relative α/(α + β) α value

Relative Relative Mean emission particle SWCNT intensity abundance length (nm)

Shear

15 min centrifugation

0.34

1

1

1

580

Shear

2 h centrifugation

0.51

0.83

0.80

0.78

520

Shear

7 day static magnetic

0.62

0.72

0.65

0.62

550

Shear

4 h ultracentrif.

0.68

0.63

0.58

0.56

400

Tip

15 min centrifugation

0.34

1

1

1

430

Tip

2 h centrifugation

0.53

0.94

0.95

0.91

---

Tip

6 day static magnetic

0.76

0.87

0.91

0.81

420

Tip

4 h ultracentrif.

0.75

0.87

0.89

0.80

390

Table 3. Dependence of dispersion efficiency on processing method and sample source Shear dispersion (HiPco sample)

< 1%

Tip sonication (HiPco sample)

Tip sonication (CoMoCAT sample)

~5-7%

~3%

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(a) 1.4

Absorbance

1.2 1.0

15 min

0.8

30

0.6

60 90

0.4

120 min

0.2 0.0 400

600

800

1000

1200

1400

1200

1400

1200

1400

Wavelength (nm) (b)

Absorbance

1.0

0.8

30 min

0.6

60

Measured Fit

90

0.4

B 0.2

0.0 400

S 600

800

1000

Wavelength (nm)

(c)

0.02

Residuals

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


-0.02 0.02 0 -0.02 0.02

0

30 min

60 min 90 min

0 -0.02

400

600

800

1000

Wavelength (nm)

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Figure 1. (a) Absorption spectra measured after different durations of moderate centrifugation. (b) Measured data and fits for the 30, 60, and 90 minute traces from (a), with fits shown as thin white curves superimposed on thick colored data curves. Also plotted are curves marked B and S showing the deduced basis spectra whose linear combinations generated the spectral fits. (c) Absorbance residual values for the three spectral fits in frame (b), with scales offset for clarity.

Figure 2. Deduced coefficients (in equation 1) of the spectral components S and B needed to fit spectra measured at different durations of moderate centrifugation time. Red circles show coefficients β and blue squares show coefficients α. Values of the corresponding S-index for sample quality are plotted as black triangles. The curves are guides to the eye.

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Figure 3. (a) Mean emission spectra and (b) emission variance spectra for the HiPco batch 195.1 shear-dispersed sample after 15 min (black curve), 60 min (red curve), and 120 min (blue curve) of moderate centrifugation. Measurements were made with 660 nm excitation. Labels in (b) show the (n,m) species giving main emission peaks.

25



Figure 4. Effects of moderate centrifugation duration on (a) normalized emission intensity, and (b) relative particle abundance for a shear-dispersed HiPco batch 195.1 sample. Results are plotted for the five well-resolved (n,m) species. (c) and (d) show the same quantities for a sample dispersed instead by tip-sonication. All measurements were based on variance spectroscopy data.

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Figure 5. Length distributions measured by the LAND method for a shear-dispersed HiPco sample (batch 195.1) after four different purification treatments. Bars show measured data; solid curves show best log-normal distribution fits. Boxes display values for number of nanotubes measured, their mean length, and the standard deviation of the length distribution. Means and standard deviations were computed from the data rather than the fits.

27



Figure 6. (a) An example of a covariance plot measured for the HiPco batch 195.1 sheardispersed sample (b) Normalized covariance spectra at the (7,5) emission peak position for the same sample after four different purification treatments (c) Scaled correlation coefficients of four nanotube (n,m) species with respect to the (7,5) species. Note that 4 h of ultracentrifugation substantially lowers the concentration of emissive aggregates containing the (7,5) species.

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Figure 7. Effects of moderate centrifugation duration on (a) coefficient α for the spectrally sharp component in samples prepared using the HiPco and CoMoCAT SWCNT sources and dispersion methods listed in the figure legend; (b) the normalized coefficient β for the spectrally broad component in the same samples; (c) the spectrally integrated SWIR absorbance for the tip-sonicated HiPco batch 195.1 sample. Black open circles denote the integrated total absorbance, while the blue triangles and red squares the show the integrated S and B absorbances, respectively; (d) ratio of the integrated sharp absorbance to the integrated total absorbance. Symbols show measured data and solid curves are guides to the eye. The symbol legend shown in graph (a) also applies to graphs (b) and (d). Note that S integrals represented in (c) and (d) may be underestimated by up to ~30% because of offset uncertainties in construction of the S basis spectrum.

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TOC Figure

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Indexing the Quality of Single-Wall Carbon ... - ACS Publications

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