For argument’s sake, I will forward two 80:20 observations that apply to chromatography:

80% of published applications are for research and development (R&D) studies, and 20% are for routine applications

80% of all chromatographs sold are for routine work, and 20% go to the R&D labs.

While not surprising, it means that chromatographers often ignore issues that are critical in routine, industrial settings that should see more of our light. So, think about it: Most chromatographs are tasked on day one with a specific analysis and, 20 years later, their final analysis will be the same as the first.

Deploying a chromatograph in a quality control (QC) or process setting is critical to many complex streams in that it provides detailed information on the composition of a huge array of mixtures and provides the means of integrating compositional control strategies in the plant. Day-to-day and instrument-to-instrument variability will frustrate data analysis. Computer-enabled technology can be applied to mitigate the impact of aging instruments and instrument differences, leading to a more robust, lower-maintenance world for industrial chromatographs.

The key issue is to control retention time drift. Because peak retention on the column has some variability, this shifting increases the likelihood of misidentifying a peak and, ultimately, risks misreporting a component’s concentration. Chromatographic retention time drift from run-to-run is always present, but usually is not a significant source of variability. Over time, however, any retention drift amplifies the risk of an error in peak identification, which can lead, in turn, to mistakes in peak tables or in evaluating the overall distribution. Software control of retention time drift is a generic solution to this problem and addressing retention time variation simplifies ownership of a chromatograph by making the data consistent from one month to the next, even one instrument to the next.

In 1965, Kováts described how you can use peaks that are easily identified as markers to calculate relative retention times of neighboring peaks (1). In the late 1990s, groups worked to address the problem of retention time variability by borrowing a multivariate technology that had been applied to voice recognition; one example is correlation optimized warping (COW) described by Nielsen (2). Software, both commercial and freeware, has been available for these approaches since the early 2000s. My team has employed both techniques, separately and in combination, for two decades or so, and has used alignment technology to manage a variety of routine and complex applications, including clinical analysis for the Centers for Disease Control (CDC), systems for economic fraud investigations for the U.S. Food and Drug Administration (FDA), and process hydrocarbon evaluations in refineries and chemical plants.

The traditional way of handling this situation is to rerun the standard and accept new time positions for the peaks. This approach gives a very short-term context and, by forcing frequent recalibrations, lowers the number of samples that can be processed. Hands-on recalibration makes the system more unreliable online, where it is harder to attend to these changes. We often relegate this type of analysis to a supervised laboratory rather than placing it in a less-supervised position near the sampling point, where the feedback would be timely.

But what if we could rely on retention times not changing? This is an example of where chemometric alignment technology should be used. For software alignment to work, one chromatogram must be previously chosen to be the alignment standard; the algorithm then adjusts the peak positions in all the other traces to match that standard as closely as possible. COW-based alignment does not require the alignment standard to be run close to the analysis date, nor does it even need to be run on the same chromatograph. In practice, alignment keeps the peaks in place for a significantly longer time and reduces the work necessary to keep the system calibrated.

The idea that a small amount of software can be added to a chromatographic data system and have that addition open a true plug-and-play capability for the instrument and application is very powerful. Ostensibly, a company could keep a cold spare chromatograph in inventory. When a process or laboratory unit goes down for any reason, the storage unit would be placed online and have its data be completely comparable to all previous runs on its very first injection, possibly without ever performing a traditional calibration. Alignment can also apply this procedure to the historical chromatographic data assembly, resulting in a consistent database ready to be mined.

This conclusion also holds for situations where the column and the method conditions are similar. But if this is the case, the ability to perform more complex chromatographic evaluations in a fully automated manner is achieved.

(2) N-P. V. Nielsen, J.M. Carstensen, and J. Smedsgaard, 

 is the President and CEO of Infometrix, in Bothell, Washington. This article is a collaboration between 

 and the American Chemical Society Analytical Division Subdivision on Chromatography and Separations Chemistry.

Articles

Computer-enabled technology approaches can mitigate the impact of aging instruments and instrument differences—leading to more robust, lower-maintenance analysis in industrial settings.

Controlling Retention Time Drift in Industrial Chromatography Settings

Sophia Barhdadi works as a scientific collaborator for Sciensano. She obtained her Ph.D. in pharmaceutical sciences with her research on the chemical and toxicological assessment of e-cigarette liquids. She is continuing her research on e-cigarettes and related products in Sciensano.

Michael Canfyn works as a technical expert in Sciensano and supports the GC analyses of several research projects within the service Medicines and Health Products.

Sanae El Merabety has an advanced master’s in industrial pharmacy and currently works as QA Packaging Representative at Eli Lilly and Company.

Patricia Courselle works as a senior scientist in the service Medicines and Health Products of Sciensano. This service acts as the Belgian Official Medicine Control Laboratory and National Reference Laboratory for the quality control of medicines and consumer products.

Vera Rogiers is professor emeritus of the Vrije Universiteit Brussel (VUB). She has a Ph.D. in pharmaceutical sciences and was for many years head of the department of In Vitro Toxicology and Dermato-Cosmetology. She is currently the holder of the Chair Mireille Aerens for the Development of Alternative Methods at the VUB and the Director of the Innovation Center-3Rs (IC-3Rs).

Tamara Vanhaecke has a Ph.D. in pharmaceutical sciences, is full professor in toxicology and head of the department In Vitro Toxicology and Dermato-Cosmetology at the Vrije Universiteit Brussel. Her research is focused on the development of human relevant liver-based in vitro systems to detect chemical‑induced liver toxicity or injury. She also collaborates in several research projects that address toxicity concerns related to different types of consumer products.

During the past decade, e-cigarettes have become increasingly popular. To guarantee their safe use and to comply with the notification requirements of the EU Tobacco Product Directive, the EU member state regulatory authorities need information about the exact composition of the e-liquids and their emissions. However, one of the challenges encountered during the analysis of e-liquids is the presence of the highly abundant e-liquid matrix components propylene glycol and glycerol. In this study, headspace gas chromatography (HS-GC) analysis is presented as an excellent method for the analysis of high volatile components in e-liquids. For the analysis of semivolatile ingredients, an additional sample preparation step is proposed based on a liquid–liquid extraction (LLE) followed by a freeze-out of the matrix components. The developed method was successfully validated in accordance with the validation requirements of ICH guidelines for the quantification of four flavourings with a potential health concern for e-cigarette users.

Since its introduction more than 10 years ago, the e-cigarette has become a popular consumer product. To guarantee its safe use and to comply with the notification requirements of the EU Tobacco Product Directive (1), analytical characterization of the refill liquids (e-liquids) used for e-cigarettes is essential. The most prominent methods used for the analytical assessment of e-liquids in general are liquid chromatography (LC) and gas chromatography (GC).

LC is mainly used for the analysis of nicotine, nitrosamines, and nonvolatile flavourings (2–5). The sample preparation for LC methods is, in most cases, limited to a simple dilute-and-shoot approach because of the “simple” e-liquid matrix composed of propylene glycol and glycerol. So far, sample preparations involving extractions in e-liquids have only been explored for the analysis of tobacco nitrosamines and phtalates (6,7).

GC is the most commonly applied technique for the analysis of e-liquids because they mainly contain volatile components. As such, GC has been used for the analysis of e-cigarette ingredients, additives, and contaminants (8–10). Similar to LC, the sample preparation methods most frequently used with GC are dilute-and-shoot approaches using organic solvents. Yet, Hutzler et al. (11) have also described alkaline and acidic extractions to cover a wide range of components. Other sample preparations combined with GC analysis used for e-liquids are thermal desorption and solid-phase microextractions (SPME), especially for highly volatile organic compounds (12,13).

The dilute-and-shoot approach as well as the thermal desorption method result in a high abundance of the matrix components propylene glycol and glycerol in the chromatographic system, unless high split ratios or higher dilutions are used, which would lead to a loss in sensitivity. This abundancy of matrix components will have an impact on the chromatographic performance and could affect the detector performance. Furthermore, the viscous and greasy properties of the e-liquid matrix may also result in a faster deterioration of the GC columns and deactivation of the active groups. These issues are less a problem for LC methods, because of the robust columns and higher dilutions. To avoid the e-liquid matrix during the GC analysis, proper sample preparation is needed to improve the life span of the column and reduce the total number of cuts of the GC inlet. Often, the target components that need to be analyzed are also miscible with propylene glycol and glycerol. Therefore, it is quite challenging to simultaneously extract all analytes.

This article presents a strategy for GC analysis of flavourings in e-liquids.
The screening methods were developed using representative flavourings for the main chemical groups (aldehydes, [di]ketones, pyrazines, esters, alcohols). The flavourings were further divided into two subsets for which different GC methods were developed, that is, high and semivolatiles.

Considering the high number of flavouring chemicals used in e-liquids, the developed method for the semivolatile components was validated using a selection of four compounds that previously were predicted to be either genotoxic (2-furyl methyl ketone, pulegone, estragole) (14) or an inhalation toxicant (cinnamaldehyde) (15).

For the development of the screening GC–mass spectrometry (MS) method, the test solution consisted of a mixture of the following standards in a concentration of 10 mg/mL in hexane or ethanol (according to the solubility): ethyl butyrate, benzaldehyde, limonene, benzyl alcohol, linalool, alpha‑terpineol, citral, trans-cinnamaldehyde, damascenone, eugenol, vanillin, ethyl vanillin in hexane, 2,3-butanedione, 2,3-pentanedione, acetoin, trimethylpyrazine, acetylpyrazine, L-menthol, carvone, methyl 2-aminobenzoate, methyl cinnamate, piperonal, maltol, gamma-octanoic lactone, and ethyl maltol in ethanol.

For the validation of the analysis of the selected flavourings (2-furyl methyl ketone, pulegone, estragole, and cinnamaldehyde) the calibration standards were prepared from a 10 mg/mL stock solution in hexane. The stock solutions were stored in amber glass at 4 °C. A separate 10 mg/mL stock solution was prepared for the internal standards o-methyl anisole and 2-chlorobenzaldehyde. The calibration solutions were obtained by diluting the stock solutions into the different concentrations 1 µg/mL, 10 µg/mL, 25 µg/mL, 50 µg/mL, 75 µg/mL, and 100 µg/mL with hexane as solvent and the internal standard at 10 µg/mL After vortexing for 10 s, 150 µL was transferred into a 10 mL headspace vial. The validation samples were prepared at three concentration levels in triplicate. To prepare the validation samples, a propylene glycol–glycerol matrix was spiked with the reference standards of the target component. The most common matrix ratio consists of 60% propylene glycol and 40% glycerol. Therefore, 0.5 g of 60:40 propylene glycol–glycerol matrix was spiked with a 10 mg/mL stock solution to obtain 10 ppm, 50 ppm, and 100 ppm formulations and with the internal standard solution to obtain a final concentration of 10 µg/mL after the extraction procedure. All the used standards were purchased from Sigma Aldrich.

The group of flavourings were divided into two subsets: high and semivolatiles. For the high volatiles, a dilute-and-shoot approach was applied in combination with headspace injection. The samples were diluted by dissolving 1 g e-liquid sample in 10 mL dimethyl sulfoxide (DMSO) of which 300 µL was transferred to a 10 mL sealed vial. For the semivolatiles, higher headspace temperatures were needed. Therefore, to minimize the interference of the matrix components propylene glycol and glycerol, a liquid–liquid extraction (LLE) was applied as a sample preparation step. The substances were extracted with hexane followed by separation through a “freeze-and-pour” technique. For each sample extraction, 0.3 g of the e-liquid was weighted in a glass vial of 20 mL, mixed with 3 mL hexane and covered with a Teflon seal. During the first extraction step, vials were vortexed for 10 s and then sonicated for 3 min at 50 °C. Afterwards, vials were transferred to a cooling bath of −78 °C (dry ice dissolved in acetone) for 2 min, followed by 1 min centrifugation at 860 g. Three quarters of the supernatants were transferred to a glass vial. These extraction steps were repeated for a second time by adding another 3 mL of hexane. After extraction, 300 µL of the combined extracted solutions of two extraction cycles was transferred to a headspace vial of 10 mL.

The screening of the high volatiles was performed on an Agilent 7890B gas chromatograph with an Agilent 7000C triple quadrupole mass spectrometer and equipped with a G188A static headspace sampler (Agilent Technologies). The incubation temperature was maintained at 85 °C with an equilibration time of 15 min. The injector port was kept at 160 °C in split injection mode (split ratio 15:1), while the temperatures of the headspace loop and the transfer line were maintained at 100 °C and 120 °C, respectively. The substances were separated on a 60 m × 0.25 mm, 0.25-μm VF-5 ms (5% phenyl–95% methylpolysiloxane) capillary column and an integrated guard column of 10 m (#CP9013, Factor four, Agilent). Helium carrier gas was used at a constant flow of 1.0 mL/min. The initial oven temperature of 45 °C was maintained for 10 min, followed by a temperature ramp of 40 °C/min to a final temperature of 250 °C. The total run time was 18 min. The mass spectrometer was operated in electron impact (EI) mode at 70 eV. Temperatures of the ion source, the quadrupole, and the interface were set at 230 °C, 150 °C, and 280 °C, respectively. The identification was performed in full-scan mode from 25 to 400 

The second screening method was applied to detect the semivolatile substances in e-liquids. Thus, the incubation temperature was maintained at 145 °C with an equilibration time of 15 min to obtain full evaporation mode. The injector port was kept at 160 °C in split injection mode (split ratio 15:1), while the temperatures of the headspace loop and the transfer line were maintained at 150 °C and 155 °C, respectively. The GC–MS conditions were similar to those of the method for high volatiles, except for the temperature gradient. The temperature gradient started at 65 °C (held for 3 min) and raised at 5 °C/min to reach 90 °C. The temperature gradient continued at 20 °C/min, until 185 °C, followed by another fast decrease in temperature to 100 °C by 30 °C/min, which finally increased with 35 °C/min until 290 °C (held for 3 min). The total runtime of the method was 24 min.

 The aim was to develop a general GC–MS screening method for flavouring components present in e-liquids. It is, however, impossible to include every compound that can be potentially found as a flavour in e-cigarettes. The method was therefore developed based on frequently used representatives for each flavouring group (aldehydes, [di]ketones, pyrazines, esters, alcohols). The components chosen for method development were: 2,3-butanedione, 2,3-pentanedione, acetoin, ethyl butyrate, trimethylpyrazine, benzaldehyde, limonene, acetylpyrazine, benzyl alcohol, linalool, L-menthol, alpha-terpineol, citral, carvone, cinnamaldehyde, eugenol, damascenone, methyl 2-aminobenzoate, methyl cinnamate, piperonal, vanillin, ethyl vanillin, maltol, gamma-octanoic lacton, and ethyl maltol. Nicotine was added to check for possible interference.

First, the appropriate solvent was chosen to assure the solubility of the standards and eliminate solvent interferences for the selected compounds. Of the four tested solvents (DMSO, ethanol, methylene chloride, and hexane), hexane was finally selected as most optimal. Next, the chromatographic method was developed through screening of the most appropriate column; 5% diphenyl equivalent/95% dimethylpolysiloxan and 6% cyanopropylphenyl/94% dimethylpolysiloxan columns were tested and the GC injection parameters and oven temperature program were optimized. However, it was noticed that two separate methods were necessary to detect all selected flavourings because of the wide variability in volatility among the different compounds andinterference of other substances. The group of flavourings were divided in two groups: high and semivolatiles. The high volatile flavouring group consisted of the flavourings 2,3-butanedione and 2,3-pentanedione, which could not be detected with the original method (Figure 1) as these substances interfered with the hexane solvent peak. For the separate method of the high volatiles, DMSO was selected as solvent.

Once the chromatographic method was obtained, an appropriate injection mode was chosen and the sample preparation was developed to minimize the presence of the matrix (propylene glycol, glycerol) during the analysis.

For the high volatile flavourings, injection through headspace was considered to limit the transfer of the matrix to the column. The headspace temperature was chosen to obtain an optimal transfer of the target components to the headspace with minimal transfer of the matrix components. The optimal headspace temperature was 85 °C.

For the semivolatiles, full evaporation headspace injection (145 °C) was considered instead of direct injection. However, extra sample clean-up was still needed to avoid transfer of the matrix components to the column. Different sample clean-up techniques including solid-phase extraction (SPE), distillation and liquid–liquid extraction were tested. Both SPE and distillation did not result in a separation of the matrix components. In case of distillation, the boiling points of propylene glycol and glycerol were similar to the analyzed components and therefore excluded as an option, once the pilot experiment showed no acceptable result. SPE was performed with C18 cartridges (0.5 g, 6 mL). After conditioning with methanol and sample load, the cartridge was cleaned with water and then eluted from the sorbent with methanol. This did not result in a decrease of matrix components in the chromatogram. The most appropriate method was a liquid–liquid extraction. The extraction was further optimized with respect to solvent, extraction cycles, and extraction time. However, the technique for separation of the organic layer was not satisfactory.

In the classical approach of liquid–liquid extraction, the separation of the multiple layers is done with a funnel or by pipetting the relevant layer from the whole. However, we observed that the presence of the matrix components was still significant in the organic layer.

Therefore, a separation technique based on flash cooling, as previously described for cosmetics (16), was applied. This “freeze-pour” approach uses a flash cooling technique for separation after the addition of an organic or an immiscible solvent. The extract as a whole was put in an ice bath at -78 °C to freeze and solidify the matrix components glycerol and propylene glycol. Next, the organic phase (hexane) was poured into a vial for further analysis without the matrix components as these were still in a solid phase. This way has proven to eliminate the matrix components as much as possible.

As an example, a commercial e-liquid was analyzed with both separation techniques. The same dilution was used to compare the liquid–liquid extraction with separation through pipetting of the organic phase. The chromatogram in Figure 2 shows that the amount of matrix injected in the column was limited after the freeze‑pour extraction and the recoveries were the same or better. This sample preparation technique can also be used with other GC injection techniques.

Quantification of Potential Toxic Flavourings:

 The method for semivolatiles was applied for the quantification of the potential toxic flavourings 2-furyl methyl ketone, pulegone, estragole, and cinnamaldehyde (14,15). For their GC–MS/MS analysis, the precursor and product ions were chosen for each target component to guarantee minimum interferences. Multiple reaction monitoring (MRM) parameters and retention times are reported in Table 1. The original chromatographic method was shortened to 18.5 min. After a 1 min hold on 65 °C, a temperature ramp was set at 10 °C/min until 240 °C. The chromatogram of the separation is shown in Figure 3. The general internal standards o-methylanisole and 2-chlorobenzaldehyde were chosen, although deuterated molecules of the target component are preferable.

Method validation was subsequently done for the four selected compounds in accordance with the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) guidelines (17).

All validation parameters are reported in Table 2. The method’s limit of detection (LOD) and limit of quantification (LOQ) were determined based on the signal-to-noise ratio (

 =10 for LOQ) of the quantifier ion peaks in the sample matrices. The estimated LODs and LOQs of the method ranged from 0.1 to 10.0 µg/g and from 0.3 to 33 µg/g, respectively. All calibration curves ranging from 1 to 100 µg/mL showed good linearity with correlation coefficients (R²) greater than 0.99. Method precision was assessed based on replicate analyses of spiked samples (three concentration/three replicates). The validation samples were spiked with 10, 50, and 100 µg standards in 1 g e-liquid (60:40 propylene glycol–glycerol). The precision was then calculated based on the percent relative standard deviation (%RSD). The %RSDs were <15%. Method accuracy assesses how close the experimental value is to the expected value. Method accuracy was expressed as the bias to the known amount of analyte in the spiked sample. As shown in Table 2, the bias from the spiked samples was between 7.3% and 16.4%, demonstrating good accuracy of the method, which also indicates that no analyte loss occurs during sample preparation.

The extraction recovery was determined to confirm the minimal loss of the analyzed compound during the extraction (Table 3). A recovery of at least 85% was accepted as high enough for this application. The recoveries with the freeze-pour approach were higher than the extraction with separation through pipetting.

To prove the applicability of the freeze-pour extraction method for semivolatile flavourings, previously screened samples were selected that were positive for one of the target flavourings of the validated method for further quantification. This initial screening resulted in a small sample set of six e-liquid samples. The results are summarized in Table 4. Because of confidentiality the analyzed liquids are anonymized. Whether these concentrations of flavourings actually pose a health risk via e-cigarette vaping should be further assessed and will be further investigated.

Most GC methods used for the analysis of e-liquids apply the dilute-and-shoot approach for practical reasons. For routine analysis it is recommended to limit the presence of high amounts of propylene glycol and glycerol in the GC instrument and mass analyzer. In this study, a strategy was developed for the GC analysis of flavouring chemicals present in e-liquids. It will become more important to analyze these flavours for regulatory purposes since the manufacturers need to notify their presence to the EU competent authorities. Because of the wide range of flavours available, two methods were developed, one for the high volatile and one for the semivolatile flavourings, as both groups could not be analyzed with just one method. In the case of high volatiles, the use of headspace injection was sufficient to limit the amount of matrix components. For the semivolatiles, high headspace temperatures were used, and thus the transfer of matrix components was inevitable. Therefore an additional sample preparation step was included. In this study, we successfully developed a clean-up technique that eliminates most of the matrix components. The sample preparation is a liquid–liquid extraction with hexane followed by a freeze out of the matrix components using a flash cooling technique. Because of the solidification of the matrix components, the organic phase (hexane) with the target component can easily be poured and injected for analysis. The method was successfully validated according to ICH guidelines for four flavourings with potential health concerns for e-cigarette users.

European Parliament and the Council of the European Union, Off. 

Beitrage zur Tab. Int. Contrib. to Tob. Res.

J. Aszyk, P. Kubica, A. Kot-Wasik, J. Namieśnik, and A. Wasik,

P. Kubica, A. Kot-wasik, A. Wasik, and J. Namie,

B. Wei, M. Goniewicz, and R.J. O’Connor, 

C. Rawlinson, S. Martin, J. Frosina, and C. Wright, 

International Conference for Harmonisation, 

Q2 (R1) Validation of Analytical Procedures: Text and Methodology

, (ICH, Geneva, Switzerland, 2005). doi:http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Quality/Q2_R1/Step4/Q2_R1__Guideline.pdf

 works as a scientific collaborator for Sciensano. She obtained her Ph.D. in pharmaceutical sciences with her research on the chemical and toxicological assessment of e-cigarette liquids. She is continuing her research on e-cigarettes and related products in Sciensano.

 works as a technical expert in Sciensano and supports the GC analyses of several research projects within the service Medicines and Health Products.

 has an advanced master’s in industrial pharmacy and currently works as QA Packaging Representative at Eli Lilly and Company.

 works as a senior scientist in the service Medicines and Health Products of Sciensano. This service acts as the Belgian Official Medicine Control Laboratory and National Reference Laboratory for the quality control of medicines and consumer products.

 is professor emeritus of the Vrije Universiteit Brussel (VUB). She has a Ph.D. in pharmaceutical sciences and was for many years head of the department of In Vitro Toxicology and Dermato-Cosmetology. She is currently the holder of the Chair Mireille Aerens for the Development of Alternative Methods at the VUB and the Director of the Innovation Center-3Rs (IC-3Rs).

 has a Ph.D. in pharmaceutical sciences, is full professor in toxicology and head of the department In Vitro Toxicology and Dermato-Cosmetology at the Vrije Universiteit Brussel. Her research is focused on the development of human relevant liver-based in vitro systems to detect chemical‑induced liver toxicity or injury. She also collaborates in several research projects that address toxicity concerns related to different types of consumer products.

 has a Ph.D. in pharmaceutical sciences and is currently head of the scientific service Medicines and Health Products of Sciensano.

HS-GC analysis is presented as an excellent method for the analysis of high volatile components in e-liquids. For the analysis of semivolatile ingredients, an additional sample preparation step is proposed based on a LLE followed by a freeze-out of the matrix components.

Development of a “Freeze-Pour” Sample Preparation Method for the GC Analysis of Semivolatile Flavouring Chemicals Present in E-cigarette Refill Liquids

In last month’s instalment of “LC Troubleshooting” (1), we reviewed the basic concepts of extracolumn dispersion (ECD), and how the level of ECD associated with a particular instrument can impact the apparent quality of a separation (for example, as measured by resolution). We emphasized the point that smaller (that is, shorter and narrower) columns packed with smaller particles are more sensitive to the effects of ECD than larger columns packed with larger particles. We discussed ECD in an aggregate sense, without getting into details about the level of ECD that different liquid chromatography (LC) system components contribute to the total peak variance observed at the detector (σ

). After all, it is the total observed variance that directly affects the resolution of adjacent peaks in a chromatogram.

In this month’s instalment, we begin to discuss in detail the contributions to the total observed variance made by specific system components. Figure 1 illustrates the contributions of different system components to the total observed variance, starting with the injection step and ending with the detection step. In principle, the total observed peak variance is simply the sum of the variances contributed by each of the system components. This is only rigorously correct if the dispersion in each element of the system is independent of the others (2), but, under most conditions, equation 1, shown in Figure 1, is accurate enough to guide method development and system optimization (3).

In this instalment of “LC Troubleshooting”, we start by focusing on the variance associated with the injection of the sample into the mobile phase stream, and eventually the column.

Different Approaches to Injection in Modern LC

A detailed overview of the operational principles of different types of high performance liquid chromatography (HPLC) autosamplers was given recently by Steiner and associates in this magazine (4). In this article, we are mainly focusing on the extracolumn contributions to total peak variance associated with different injection approaches. Drawings of the two injector designs most commonly used in modern LC instruments are shown in Figure 2. In the so‑called 

 (panels a and b), sample is drawn from a vial by an automated syringe and subsequently transferred to a sample loop, in either a pushed loop mode, as illustrated in Figures 2(a) and 2(b), or pulled through the needle directly into the sample loop (pulled loop design) (4). During the sample loading (“load” position), the desired sample is first drawn into the syringe. The loop—drawn in yellow in Figure 2—is at this point also not in the mobile phase flow path from the pump to the column. After fluidically connecting the syringe to the loop, the desired sample volume is pushed out of the syringe into the loop. Then, the valve is switched to the “inject” position; at this point, the sample that had been loaded into the loop is displaced from the loop into the column where sample components can be separated.

 shown in Figures 2(c) and 2(d), also known as 

 (4), the mobile-phase flow path bypasses the injector needle in the “load” position, allowing the sample to be drawn from a vial into the needle using an automated syringe. Once the desired volume of sample has been drawn into the needle, the valve is switched to the “inject” position. This connects the needle to the mobile-phase flow path, pushing the sample through the needle seat capillary and into the column.

Each of these approaches has advantages and disadvantages compared to the other. One of the primary advantages of the fixed-loop approach is that there is the potential to decrease the variance associated with the injection step (

) if a small loop is used. A significant downside to this approach is that the injection volume is fixed, as the name suggests. Significantly changing the injection volume requires a physical change of the capillary used as the loop. It is possible to use partial filling of the loop, which does help to extend the practically useful range of injection volumes available with a fixed‑loop. Conversely, one of the primary advantages of the flow through approach is that the injection volume can be changed over a wide range of volumes under software control, without changing any instrument components. However, this comes at the cost of increased ECD, since the flow path between the sample and the column includes a needle seat capillary that simply is not required in the fixed‑loop approach. Some instrument vendors provide “low‑dispersion” kits where this needle seat capillary has a lower inner diameter to reduce ECD, or have changed their instrument design such that the needle is integrated directly onto the injection valve, eliminating this capillary entirely.

We note here that in two‑dimensional liquid chromatography (2D-LC) separations, the interface that delivers fractions of first dimension (

D) column for further separation is most similar to the fixed-loop injector shown in Figure 2. In this case, fixed-loops are used, and typically the interface valve has two positions that enable injection of sample from one loop into the 

D column while a second loop is filled with 

D effluent in parallel. The major difference with 2D-LC compared to the diagram in Figure 2 is that the sample loop is filled naturally as mobile phase flows out of the 1D column, rather than filling the loop with a syringe. Typically, the loops used in 2D-LC are much larger (for example, 40 to 80 µL) than those used in fixed-loop injectors for 1D-LC (typically less than 10 µL in ultrahigh-pressure liquid chromatography [UHPLC] systems).

Contributions of Injection Parameters to Peak Dispersion

In the simplest case of a fixed-loop injector with: 1) no seat capillary; and 2) an overfilled loop such that the distribution of sample inside the loop is homogeneous, the introduction of the sample into the mobile phase stream is conceptually close to introduction of a rectangular “sample plug”. For a truly rectangular plug, the contribution of the injection step to the observed peak variance (in volume units) would be given by equation 2:

 is the volume of the sample loop itself (5). In practice, however, we observe variances that are usually much larger than those predicted by equation 2. A more general form of this expression replaces the factor 1/12 with 1/

For fixed-loop injectors operated in the full-loop mode, values of 

= 4–8 are observed in practice. The situation with flow‑through injectors is a bit more complicated due to the addition of the needle seat capillary in the flow path between the sample to be injected and the column. Recent work has shown that two distinct factors contribute to the total variance contributed by the injection process for flow-through injectors—a factor related to the physical volume of the injected sample, as in equation 3, and a factor related to hydrodynamic dispersion of the sample due to parabolic flow in the needle seat capillary (6). These contributions to the total injection variance can be expressed using equation 4:

term accounts for hydrodynamic dispersion in the needle seat capillary.

made using a flow-through injector over a range of conditions show that for injection volumes less than about 2 µL,

increases roughly linearly with increasing 

, and then appears to plateau at a value of 

The hydrodynamic component of the variance is related to the dimensions of the seat capillary, as well as the properties of the mobile phase and the molecular weight analyte through their effects on the diffusion coefficient of the analyte, as shown by equation 5:

are the length and diameter of the seat capillary

 is the diffusion coefficient of the analyte in the sample solvent. This equation shows why it is beneficial to reduce the length and diameter of the needle seat capillary in cases where reducing ECD is desired to avoid robbing a very good column of its inherent efficiency. Readers interested in learning about these relationships in more detail are referred to recent literature on the topic (2,6).

 The relationships in the preceding section provide relatively straightforward means to estimate the contribution of the injection step to the total, observed peak variance. However, up to this point in our discussion, we’ve made two important assumptions that are required for these expressions to accurately reflect what happens in a real system: 1) the separation is isocratic; and 2) the sample solvent is nominally identical to the mobile phase into which it is injected. When gradient elution is used, the situation changes substantially; this is one of the topics we will address in next month’s instalment of “LC Troubleshooting”.

Of course, in real applications it is frequently unrealistic, or highly undesirable, to go to the trouble of matching the sample solvent to the mobile-phase composition, so it is important to acknowledge that the contribution of the injection step to the overall ECD of a system may be larger or smaller than that estimated, using equations 3 and 4 above in cases where the sample solvent is very different from the mobile-phase composition. In previous instalments of “LC Troubleshooting”, we have addressed the sample solvent issues in some detail, but there has also been quite a bit of research published on this topic over the last few years. Figure 3 shows an example of how simply inverting the ratio of organic solvent to water in the sample solvent can have a dramatic effect on a separation, making the difference between a chromatogram that looks quite nice and useful, and a chromatogram that is effectively useless (7). This example shows the effect on a reversed-phase separation; however similar effects can occur for most modes of LC separation, including hydrophilic interaction chromatography (HILIC) (8).

In recent years, several groups, including our own, have worked to develop simulations that can predict both the negative and positive effects of a mismatch between the sample solvent and the mobile phase. The published approaches for these simulations range from very detailed models that yield considerable insight into the origins of poor peak shapes, such as those observed in Figure 3(b), to simpler approaches that are not quite as accurate, but are computationally much less intensive and can be calculated using a spreadsheet (9–12). The most recent of these articles by Pepermans and associates provided a very useful rule of thumb to help users anticipate whether or not they will encounter an effect like that shown in Figure 3; this guideline is expressed as equation 6:

 is the retention factor of the analyte in the sample solvent (12). Specifically, equation 6 predicts that when this inequality is true, that distortion of the analyte peak will be so severe that part of the analyte will migrate all of the way through the column in the sample solvent, and breakthrough as a peak detected at the column dead time; most of the rest of the analyte will elute at the expected time based on its retention factor in the mobile phase. For example, for a column with a dead volume of 100 µL, if a sample solvent is used such that the analyte would have a retention factor of 0.5 if the sample solvent were used as the mobile phase, then a breakthrough peak will be observed at the column dead time for injection volumes larger than 50 µL. While equation 6 is very useful as a guide to avoiding problems of this kind, its use does require knowledge of the dependence of analyte retention on solvent composition; this is also true of the more detailed simulations described to date.

If the sample is dissolved in a mobile phase which has a higher elution strength than the initial mobile phase, the effect of this solvent mismatch can be mitigated by drawing mobile phase plugs with lower elution strength before or after the sample plug into the needle. Mixing of these weak solvent plugs with the sample solvent in the precolumn tubing and the column inlet causes the analyte bands to be compressed at the head of the column. This method is called “Performance Optimizing Injection Sequence” (13) and is based on the sandwich injection approach that was introduced to prevent sample precipitation in connecting tubing in polymer analysis (14). This technique was reported to be most useful for compounds in the range of 0.4 < 

< 8, with the most benefit realized in the range 0.4 <

 < 3 (13). Even if the sample solvent matches the initial mobile phase, this technique can significantly reduce contributions of 

contributions to ECB (3). This technique can readily be applied with modern flow-through injectors using a injection program and an additional sample vial containing the weak solvent (3,13).

In this instalment of “LC Troubleshooting”, we have discussed in some detail the impacts of injector design and configuration, as well as sample composition, on the contribution of the sample-injection step to the overall ECD observed in a LC system. Understanding and estimating these effects can help us avoid situations where ECD seriously erodes the inherently high separation efficiency of a good column. In next month’s instalment, we will focus on the contributions to ECD from other system components, including connecting tubing and detectors, and discuss differences in the impact of ECD on resolution when using either isocratic or gradient elution.

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K. Vanderlinden, K. Broeckhoven, Y. Vanderheyden, and G. Desmet,

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 received his Ph.D. in 2010 from the Vrije Universiteit Brussel (VUB), in Brussels, Belgium. Following postdoctoral research at VUB and work as a visiting researcher in the separation processes laboratory at ETH Zurich in Switzerland, he became a research professor at VUB in 2012. He was subsequently promoted to assistant professor and then to his current position as an associate professor in 2017.

 is the editor of “LC Troubleshooting”. Stoll is a professor and the co-chair of chemistry at Gustavus Adolphus College in St. Peter, Minnesota, USA. His primary research focus is on the development of 2D-LC for both targeted and untargeted analyses. He has authored or coauthored more than 75 peer-reviewed publications and four book chapters in separation science and more than 100 conference presentations. He is also a member of LCGC’s editorial advisory board. Direct correspondence to: amatheson@mjhlifesciences.com

Dispersion of analyte peaks outside of chromatography columns can seriously erode the resolution provided by good columns. Here, we focus on the contribution of the sample injection step to the total level of extracolumn dispersion in an LC system.

Where Has My Efficiency Gone?  Impacts of Extracolumn Peak Broadening on Performance, Part 2: Sample Injection

The best option for the gas chromatographic (GC) analysis of highly volatile analytes without cryogenic cooling is to use solid adsorbents. Open tubular GC columns with a deposited layer of solid adsorbent on the inside are known as porous layer open tubular (PLOT) columns. Adsorbents such as molecular sieves, alumina, porous polymers, and even carbon materials are essential when analyzing light gases. Most gas samples contain water; although we are not interested in the amount of moisture that is present in the sample, water is injected onto the column unless a drying step of the sample is utilized. This article reviews the effects of water on retention and selectivity in gas–solid chromatography of various adsorbent PLOT columns.

Activated alumina has been studied as a chromatographic material for over 70 years. The high surface area of this porous material showed promise in separating C1–C5 hydrocarbons. Because of the extreme activity, tailing, and inconsistent responses of alumina, the material was at first considered almost unusable. In the mid-1950s, alumina oxides were deactivated with water vapour. This new approach drastically improved peak shape and provided consistent compound responses. However, water-deactivated alumina columns could be used only under isothermal conditions. The next significant improvement for porous alumina columns occurred in the 1970s when more stable deactivations with inorganic salts, such as potassium chloride (KCl), sodium sulphate (Na2SO4), and various proprietary salts, were implemented (1–3).

Even though the industry moved away from alumina-packed columns in favour of alumina-based porous layer open tubular (PLOT) columns, alumina columns remained a reliable choice for analyzing C1–C5 hydrocarbons. One thing has not changed since the 1950s—we still have problems with chromatography in the presence of moisture. To better understand the problematic effects of water on alumina columns, we studied the chromatographic behaviour of C1–C5 hydrocarbons. Various selectivities of alumina deactivated PLOT columns were tested under the same isothermal chromatographic conditions. The analysis was performed again after injecting 0.1 µL of water onto the column, and the procedure was repeated five times.

Regardless of the column deactivation, the introduction of water onto the alumina PLOT column changes the retention time by accelerating the elution of the analytes. Water will act as a deactivating agent, meaning that the adsorption sites where water molecules are adsorbed will not be available for hydrocarbon retention. This process results in a decrease in retention time. Figures 1 and 2 show examples of the analysis of those two analytes, alumina MAPD (Figure 1) and alumina potassium chloride (Figure 2), as well as the analysis of the columns after exposing them to 0.5 μL of water in increments of 0.1 μL. Not only do we notice shifting retention times, but we also see changes in resolution, indicating that the polarity of the column has changed. Regardless of whether the column was deactivated with proprietary deactivation, MAPD, or potassium chloride, exposure to water decreases retention time and impacts the resolution of the compounds.

The introduction of any moisture to the column would further deactivate aluminium oxide, making the column less polar, which changes the column selectivity. The plotted deviation of retention indices of the analytes, which is based on the retention of adjacent 

-hydrocarbons, confirms our observation. The analytes most affected by the moisture treatment over time are polar analytes—acetylene, propadiene, and methyl acetylene—that show more significant shifts in retention than the butanes and butenes (Figure 3). Similarly, the impact of moisture is more dramatic on a polar column, such as a sodium sulphate alumina column. Figure 4 illustrates changes in relative retention of acetylene on different types of deactivated alumina columns. The polarity of a sodium sulphate-deactivated alumina column declines considerably faster than a potassium chloride-deactivated alumina column. The potassium chloride-deactivated alumina has the lowest polarity, and it is the least sensitive to water.

Water will not damage the alumina column. The column can always be regenerated by conditioning it at the maximum temperature of the column.

Molecular sieves 5A (MSieve 5A) are zeolites, which are part of the family of aluminosilicate minerals. They are strong adsorbents with an unique, tunnel-like crystalline structure and a well-defined pore size. These strong adsorption properties make them ideal for analyzing permanent gases. The analytes separate on molecular sieves based on two mechanisms: First, how well the molecules fit into the material’s pores, a separation based on the size of the molecules; and second, the physical interactions between the molecules and the MSieve 5A crystal, which is a separation that takes place based on the polarity. For example, nitrogen and oxygen are both small enough to fit in the pores of the 5A mineral, but oxygen, a smaller molecule than nitrogen, will navigate through the “tunnels” faster because it has less interactions with the pore surface and elutes from the column before nitrogen. In the case of carbon monoxide, dipole interactions play a more important role, and the MSieve 5A strongly retains these molecules. A bigger molecule, such as sulphur hexafluoride (SF6), will not be able to enter the pores of the zeolite. It will only be retained by the outside, free-accessible surface area. The same phenomena is observed with isoalkanes. Isobutane, for example, would be eluted around nitrogen at 40 °C, while 

-butane (which can fit in the pores because of the linear structure) needs a very high temperature to be eluted (4,5).

Adsorption on molecular sieves is reversible, and the adsorption–desorption process is easily regulated with temperature. Molecular sieves are generally used as drying agents, and we know that we cannot analyze water using the MSieve 5A columns because of the high temperature required to desorb water from the molecular sieve pores. Our study on the effects of water on molecular sieves showed that the MSieve 5A has a relatively high tolerance to moisture in comparison to salt-deactivated alumina columns. A MSieve 5A PLOT column, with the dimensions of 30 m × 0.53 mm, 50‑µm, was tested using a mixture of permanent gases at 40 °C. Afterwards, water was injected onto the column at 100 °C isothermal. Column behaviour was monitored by repeating the analysis of permanent gases at 40 °C. Figure 5 presents a chromatogram of the initial analysis of permanent gases, black overlay, and the blue overlay is a chromatogram after exposing the molecular sieves to water. The column had to be treated with 1 µL of water to see a notable shift in the retention time of the polar compound, which in this case is carbon monoxide.

Although 1 µL of water may seem like a small amount, the quantity is more significant when we look at it from a different angle. For example, if the gas sample has 50% relative humidity, with every 1 mL injection of gas, we are injecting ~0.01 µL of water onto the column. To see a slight shift of the carbon monoxide peak, we would need to make hundreds of injections.

Continued exposure of water to a MSieves 5A column will pack the adsorption sites with water. Figure 6 illustrates the changes in chromatography when the molecular sieves are treated with 50 µL of water at 100 °C. Retention times for all the analytes decrease, and some will be coeluted. When the peaks start to tail (Figure 6, blue overlay), that is an indicator that the column has reached its loading capacity partially because of the adsorbent sites being occupied with water.

The temperature necessary to desorb the water from the 5A molecular sieves is lower than the maximum temperature of the PLOT column. Therefore, water can be removed from the column. By conditioning the column at the maximum temperature of 300 °C, moisture is removed and initial performance is restored (Figure 7).

Porous polymer PLOT columns are based on styrene-divinylbenzene (DVB-styrene) cross-linked polymers. They come in a range of polarities. For example, Q PLOT columns are made with 100% DVB-styrene polymers and are nonpolar. On the other hand, DVB-ethylene glycol-dimethyl acrylate PLOT columns, also referred to as 

Porous polymer PLOT columns are extremely versatile. They can separate a wide polarity range of molecules from gases to semivolatiles. The main advantage of porous polymers is their hydrophobicity. Treating a column with large, continuous water injections does not have any effect on the adsorbent. Water elutes from the column very fast as a chromatographic peak and does not change the selectivity of the column because of adsorption (Figure 8). This means there is no need to condition heat porous polymer columns to drive the water off, which reduces the downtime between injections.

Although we deliberately exposed our columns to aliquots of water to illustrate the effect of water on adsorbent PLOT columns, systems with alumina and MSieves 5A PLOTs should be protected to keep water from causing slow but continuous deactivation and changes in column selectivity. To conclude this column, here are a few tips to keep in mind to prevent water from further affecting your chromatography or to regenerate your column if it has already been exposed to moisture:

If the moisture has already accumulated in the column, sodium sulphate-, potassium chloride-, or MAPD-deactivated alumina PLOT columns as well as MSieves 5A columns can be regenerated by conditioning the column at their maximum temperature.

Increasing the final temperature of the analysis to the maximum temperature of the column for 5–10 min will desorb the water during each analysis cycle.

However, it is always better to avoid water getting into the alumina column. By using a thick film, polar precolumn with a column-switching device, water and other impurities in the sample are backflushed and never enter the analytical column.

Gas samples can be dried using Nafion dryers, salt filters, cryogenic trapping, and preconcentrators. Water and other impurities in the sample are backflushed and never enter the column.

Capillary gas adsorption chromatography (Chromatographic methods)

 is an application chemist at Restek Corporation in Bellefonte, Pennsylvania, USA.

 is the International Specialist Gas Chromatography at Restek Corporation in the Netherlands.

 is a director of Research and Development at Restek. He also serves on the Editorial Advisory Board for LCGC and is the Editor for “Column Watch”. Over the past 20 years, he has worked directly in the chromatography industry, focusing his efforts on the design, development, and application of chromatographic stationary phases to advance gas chromatography, liquid chromatography, and related hyphenated techniques. His main objectives have been to create and promote novel separation technologies and to conduct research on molecular interactions that contribute to retention and selectivity in an array of chromatographic processes. His research results have been presented in symposia worldwide, and have resulted in numerous peer-reviewed journal and trade magazine articles. Direct correspondence to: amatheson@mjhlifesciences.com

The effects of water on retention and selectivity in gas–solid chromatography of various adsorbent PLOT columns is reviewed.

Effects of Water on Adsorbents in Porous Layer Open Tubular (PLOT) Column Gas Chromatography

This contribution is a continuation of Part IX of this series. In that article, the fundamentals of light scattering as applied to SEC was introduced, including the origin and significance of pertinent equations related to the light scattering of macromolecules. In the present contribution, a summary of commercially available light scattering instrumentation is given, with emphasis on new detector technology. Owing to the complexity of data analysis and the many equations involved with light scattering measurements, a glossary of principal symbols and a table of relevant relationships are presented.

This review is a continuation of Part IX (1), in which the theory of light scattering is given, including pertinent equations and data analysis. The following contribution is a summary of commercially available light scattering (LS) detection systems for size-exclusion chromatography (SEC). Information was obtained, for the most part, from current web sites and discussions with manufacturers as of May 2020. Please note that the author does not endorse any specific instrument.

In addition to detector sensitivity and noise specifications, selecting a light scattering instrument depends on many other factors, including software features and technical support. Furthermore, certain instruments now include dynamic light scattering capability for characterizing aggregates and particulates, which will be the subject of Part XI of this series.

Light scattering detectors for SEC are currently available from Agilent Technologies, Brookhaven Instruments, Malvern Panalytical, Postnova Analytics, Tosoh Bioscience, and Wyatt Technology Corporation. The low-angle capabilities of Malvern’s low angle light scattering/right angle light scattering (LALS/RALS) (7°/90°) and Agilent’s dual-angle detector (15°/90°) use collimated annulus optics for low-angle measurements and photodiodes for right-angle detection. Moreover, Agilent’s instrument utilizes fiber optics to transmit scattering signals to the sensor. Other multiangle instruments employ an array of photodiodes that surround the flow cell, placed at specified angles for detecting scattered light, with the exception of Brookhaven Instruments that employs a charge-coupled device (CCD) for detection.

The apparent trend among some companies is to maximize the number detection angles for improved accuracy and precision. For example, Wyatt Technology, Malvern Panalytical, and Postnova Analytics sell instruments that have 18, 20, and 21 angles of measurement, respectively.

For most instruments, the low-molecular-weight limit ranges from about 200 to 103 g/mol. By reason of equation 7c from Part IX, the lower limit can be adjusted by increasing injection concentration. It is important to note, that when measuring the low-molecular tail of a polydisperse sample, realistic molecular-weight detection limits greatly exceed the above range. The lower radius-of-gyration limit appears to be about 10 nm, except, for example, Tosoh Bioscience LenS3 (see following section) and Postnova multiple angle light scattering (MALS) detectors, whose lower limits are approximately 5 and 8 nm, respectively, using typical injection concentrations. Here the lower limit of angular dissymmetry depends primarily on the sensitivity and signal/noise of the instrument.

It should be stressed that lower molecular-weight and radius-of-gyration limits depend, among other attributes, on instrument design, intensity and wavelength of the incident beam, scattering volume, and photocell sensitivity. Specifications are also based on such factors as the specific refractive index of the sample, injection amount, signal-to-noise ratio of the light scattering and concentration detectors, baseline drift, peak broadening from columns and detector cell volume, and other chromatographic conditions.

It is of interest to note that a radius-of-gyration limit of 10 nm corresponds to 105 g/mol for linear, random-chain polymers. For polymers that are smaller than 10 nm, viscometric detectors can be used, provided that universal calibration is valid, a topic that will be covered in Part XII of this series.

Tosoh Bioscience has recently introduced a multiangle instrument (LenS3 MALS) that is capable of measuring radii of gyration well below 10 nm and molecular weights less than 500 using typical injection concentrations. This newly designed instrument has a conically shaped flow cell (referred to as a chamber), composed of blackened poly(ether ketone) to absorb stray scattered light. It also is equipped with a green laser-diode (505 nm), rather than longer wavelength diodes (635 to 680 nm), typical in some other detectors, with the exception, for example, of Postnova Analytics’s 532-nm light source. Because of the 4th-power dependency on wavelength, the intensity of scattered light at 505 nm, as compared to 635–680 nm, is increased by a factor of 2.3–3.0, representing a significant increase in sensitivity.

The cell design and optical pathway of the LenS3 MALS instrument are illustrated in Figures 5 and 6, respectively. (Figure designations are continued from Part IX.) Eluent enters from the bottom of the flow chamber, splits into two pathways, which are then combined before exiting. For increased sensitivity, the unit is constructed such that nearly the entire cell volume or path length acts as the scattering volume. This is accomplished by passing the incident beam through the center of the cell, allowing forward scattering to be measured at 10

 (low-angle LS or LALS) and back scattering at 170

 (high-angle LS or HALS); these supplementary angles are close to the theoretical limits of 0

, contributing to greater radius-of-gyration sensitivity. In addition, scattered light intensity is also measured at 90

FIGURE 5 (NOTE: Figure numbers are continued from Part IX): Configuration of a newly designed SEC/multiangle instrument using the detector and GPC system as described in the New MALS Detector Technology section. This model is capable of measuring radii of gyration as low as 2 nm and molecular weights less than 500 g/mol using typical injection concentrations. Eluent enters from the bottom of a conically shaped flow chamber from two inlets, which are then combined before exiting the cell. The three photodiodes measure scattered light intensities at low (10°, LALS), high (170°, HALS) and right angles (90°, RALS).

FIGURE 6: Schematic of the optical pathway of a highly sensitive SEC/multiangle instrument. The incident beam of a 505-nm laser diode passes through the center of the cell, allowing forward scattering to be measured at 10° (low-angle LS or LALS), back scattering at 170° (high-angle LS or HALS), as well as at 90° (right-angle LS or RALS). The low-wavelength light source, long optical pathway, and widely spaced supplementary scattering angles contribute to the high sensitivity of the detector.

The high sensitivity and low baseline noise are demonstrated by the analysis of a mixture of styrene oligomers in tetrahydrofuran, ranging from dimer to nonamer. These data, given in Table I, show that the instrument is capable of measuring the molecular weight of styrene oligomers, as low as 260 g/mol for the dimer with an error of −2% when compared to the predicted value. Note that the same specific refractive index, 0.170 mL/g, was applied to all oligomers, which may have contributed to the progressively increasing error. The total mass of sample injected was only 0.43 mg of which approximately 30 μg was attributed to the dimer.

The LenS3 MALS instrument uses angular dissymmetry, rather than Zimm’s method, to calculate the radius of gyration. With this approach, the particle scattering function is represented as a ratio of scattering intensities,

° is the scattering intensity taken at 10°. Furthermore, an instrument normalization factor, 

, is employed that corrects for scattering volume, detector sensitivities, and distances from the scattering volume to the detector. Thus, for each elution volume increment, three particle scattering functions are generated:

With this procedure, the particle scattering function (equation 9) can be expressed as:

which is referred to as the Guinier relationship (15). Since 

 = 1, and the initial slope (at the intercept) is –

An example of this angular dissymmetry approach is given in Figure 7, in which a broad polystyrene standard (NIST SRM 706a) was analyzed by the LenS3 MALS detector, courtesy of Tosoh Bioscience. Owing to a slight amount of curvature, a quadratic equation (

 = 27 nm, in agreement with the literature value. Owing to the low noise level and high sensitivity of the instrument, only three angles are required for radius-of-gyration and molecular weight measurements.

FIGURE 7: Angular dissymmetry plot of a broad polystyrene standard (NIST SRM 706a) analyzed by SEC/light scattering using the detector and GPC system as described in the New MALS Detector Technology section. The particle scattering function, Pθ, measured at 10°, 90°, and 170° is plotted against the angular parameter, μ

 is the scattering intensity at angle θ, 

(θ⁄2). A quadratic equation was used to fit the data, in which 

Table II shows preliminary molecular weight and radius-of-gyration data of a series of nearly monodisperse polystyrene standards, adapted from data supplied by Tosoh Bioscience. Radii of gyration were in agreement with literature data obtained from small-angle 

-ray scattering, whose authors used similar lots of polystyrene standards dissolved in toluene (2). These initial results were well below the established limit of 10 nm. As previously noted, detection lim- its depend upon many factors that can contribute to the signal-to-noise ratio (S/N) of both the light scattering instrument and concentration detector, such as baseline noise and drift, column efficiency, injection amount, and specific refractive index of the sample.

Light scattering is the ideal detector for SEC, because it provides molecular weight and radius-of-gyration distributions in addition to corresponding statistical averages without column calibration. With this detector, the only information that is required is the specific refractive index of the polymer and the refractive index of the mobile phase.

By using low injection concentrations, the Rayleigh equation is reduced from three (

), greatly facilitating data analysis. Moreover, if low-angle measurements are used, molecular weight can be determined directly without resorting to graphical analysis, however, molecular size information is lost.

To increase the reliability of molecular weight and radius-of-gyration measurements, several instrument companies have opted to maximize the number of photodiodes that surround the flow cell; for example, Wyatt Technology, Malvern Panalytical, and Postnova Analytics have instruments of 18, 20, and 21 angles, respectively. Of late, Postnova Analytics and Tosoh Bioscience have instruments with green- rather than red-laser diodes, resulting in increased sensitivity. Furthermore, Tosoh Bioscience’s instrument consists of a newly designed flow cell and optical system that can mea- sure the radii of gyration as low as about 2 nm under ideal condition. With normal injection amounts, the radius-of-gyration limit is approximately 5 nm, and the molecular weight limit is below 500 g/mol for polystyrene.

For the reader’s benefit, a glossary of principal symbols and a summary of relevant equations and their significance are given, respectively, in Tables III and IV.

Parts XI and XII of "Chromatography Fundamentals" will be devoted to online dynamic light scattering and viscometric detectors, respectively.

The author gratefully acknowledges Cara Tomasek, David Gillespie, and Sébastien Rouzeau, all from Tosoh Bioscience, for their insightful discussions on light scattering and for providing figures and preliminary data on the LenS3 MALS instrument.

LenS is a registered trademark of Tosoh Bioscience LLC.

EcoSEC and TSKgel are registered trademarks of Tosoh Corporation.

(2) F. Abe, Y. Fumiaki, T. Yoshizaki, and H. Yamakawa, 

 is with Analytical Chemistry Consultants, Ltd., in Wilmington, Delaware. Direct correspondence to: 

This exploration into commercially available light scattering detection systems for size-exclusion chromatography (SEC) emphasizes new technologies and will help users understand the options.

Chromatography Fundamentals, Part X: Light Scattering Detection Systems for Size-Exclusion Chromatography

People often ask, what do “bioinert”,” biocompatible”, “metal-free”, and “inert” actually mean and what is the difference between them—if there is any? The terms “stainless steel‑free”, “truly inert”, or “fully biocompatible” are also used in this sense.

The first mention of the term “bioinert” dates back to 1933 and was related to the use of alumina as a material for biomedical applications (1). Since then, there has been significant interest in continually developing novel materials for biomedical implants. The term “bioinert” has since diffused into many other fields and different contexts. One of these fields is liquid chromatography.

When separating biomolecules, chromatographers often encounter unwanted and unpredictable interactions between samples and chromatographic surfaces (injector, tubes, column hardware, and detector cells) (2). Indeed, adsorptive interactions negatively affect chromatographic performance by altering peak shape (asymmetry, tailing) and reducing the recovery (3).

What is certain is that there are two different concerns. It is important to mention that both issues are associated with the chromatographic hardware (LC system and column) and not with the stationary phase (though in some similar ways, stationary phases must also be carefully designed). One issue with chromatography hardware is the potential adsorption of solute molecules onto wetted surfaces (nonspecific adsorption) in an LC instrument (sample flow path) or within the column hardware, especially on frits and the inner wall of the column tubing. It is most appropriate to call any technologies and materials that improve analyte recoveries and limit undesired interactions with the hardware as being 

. To be more precise with the terminology, we could refer to these as

 materials. The other concern with chromatography hardware is related to the resistance of the system and its columns to harsh conditions typically applied for biomolecule separations. The use of halide mobile phase components, for example, chloride ions, can corrode stainless steel surfaces, and this is one of the clearest examples where chemical resistance is needed. A chromatographic system designed to be resistant to such conditions is often termed as being 

In nondenaturing modes of chromatography, mobile phases often contain high concentration of salts (for example, 0.2–2 M). Stainless steel can, and does, rust and corrode if continuously exposed to high concentrations of halide mobile phase salts. The corroded metal can form metal complexes that leach onto the column, interact with the stationary phase, clog the frits, and negatively affect peak shape and resolution. Stainless steel may also release iron ions into the mobile phase, and these ions can bind to the stationary phase and interact with metal‑sensitive analytes. On the other hand, in denaturing LC conditions, low pH (pH 2–3) mobile phases are frequently applied and the mobile phase temperature is often set to 70–90 °C, which can also be an extreme condition for the hardware to withstand. Therefore, in a biocompatible system, alternative materials are needed, for example, PEEK, sapphire, titanium, or specialized metal alloys. In addition, pumps and autosamplers need to be robustly designed with frequent seal washing and automatic rinsing. Depending on the material used for the LC flow path, it might be appropriate to call this type of system “metal-free” or “stainless steel-free”. Technically speaking, it might be even more precise to call these systems “corrosion-resistant”.

In laboratories that do not have corrosion-resistant systems, it has become common practice to perform conditioning procedures that are meant to reduce the amount of metals eluting from a system. However, these procedures can be tedious and time‑consuming because they involve flushing the entire system with a chelator or oxidizing acid, for example, EDTA, citric acid, acetylacetone, 30% phosphoric acid, using an overnight process. This is a serious drawback when it comes to the laboratory and experimental productivity.

There is a need to use more descriptive and precise terms to describe some of the properties of the products used for biopharmaceutical analysis. It is probably more correct to say “low adsorption” and “corrosion-resistant” systems.