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AF4-ICPMS with the 300 Dalton membrane to resolve metal-1 bearing 'colloids' < 1 kDa: optimization, fractogram deconvolution, and advanced quality control Chad W. Cuss, Iain Grant-Weaver, and William Shotyk Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b01427 • Publication Date (Web): 01 Jul 2017 Downloaded from http://pubs.acs.org on July 6, 2017

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Analytical Chemistry

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AF4-ICPMS with the 300 Dalton membrane to resolve metal-

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bearing 'colloids' < 1 kDa: optimization, fractogram deconvolution,

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and advanced quality control

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C.W. Cuss, I. Grant-Weaver, W. Shotyk*

Department of Renewable Resources, University of Alberta, Edmonton, AB T6G 2H1, Canada 5 6 7 8 9 10

* Corresponding author at: Department of Renewable Resources, University of Alberta, 348B South Academic Building, Edmonton, AB T6G 2H1, Canada. E-mail addresses: [email protected] (C.W. Cuss), [email protected] (I. Grant-Weaver), [email protected] (W. Shotyk)

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Abstract:

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The smallest colloids exert a disproportionately large influence on colloidal systems owing to

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their greater surface area; however, the challenges of working in the smaller size range have

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limited most field-flow fractionation-ICPMS analyses to sizes > ca. 1 kDa. We discuss

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considerations and present solutions for overcoming these challenges, including: high pressures

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associated with using the 300-Da membrane, calibration in this small size range, accounting for

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drifting LODs and separation conditions during membrane aging, and optimizing the

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compromise between resolution and recovery. Necessary flow program ranges for observing

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pressure limits are discussed, and calibration is conducted using a combination of chemical and

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polystyrene size standards. The impact of membrane drift on size is demonstrated, and

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effectively corrected by routine calibration. Separation conditions are optimized by monitoring

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the recovery and resolution of several trace metals. A precise, high-resolution separation is

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achieved using fractogram deconvolution to fully resolve overlapping peaks. Method

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effectiveness and precision are demonstrated through triplicate analyses of three natural water

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samples: Mp = 2.89 ± 0.04, 3.20 ± 0.03, and 3.50 ± 0.12 kDa for DOM-associated Fe in the three

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samples (± 95% CI). A primarily inorganic Fe fraction with Mp = 14.7 ± 0.5 kDa was also

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resolved from the DOM-associated fraction. Quality control methods and considerations for

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optimizing flow conditions are detailed in the supplementary information as a guide for

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researchers seeking to analyze colloids in this smallest size range using AF4-ICPMS with the

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300-Da membrane.

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Introduction

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Dissolved (i.e. 0.45–ߤm filterable) trace metals in natural waters can be roughly divided into

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three major fractions on the basis of their composition: 1) free ions and primarily ionic species

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such as inorganic hydroxo, carbonato, and sulfato complexes, 2) organic-dominated material that

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consists of metal ions or small metallic particles complexed with dissolved organic matter

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(DOM), and 3) larger primarily inorganic species such as oxyhydroxides of Al, Fe, or Mn with

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trace metals adsorbed to the surface or encapsulated inside the lattice structure. The latter two

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fractions are considered ‘colloids’, which are also distinguished based on their size: ‘truly

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dissolved’ material passes through a 1-kDa membrane (< ca. 1 nm in size), and the remaining

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0.45–ߤm filterable material is considered colloidal1,2. While the IUPAC definition of colloids

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includes particles in the size range from 1–1000 nm, the 0.45–ߤm filterable fraction of colloids

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receives the greatest interest because it is generally considered to be the most toxic fraction and

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serves as the benchmark for many water quality standards. Indeed, definitions of colloids and

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colloidal systems differ by discipline and by the properties and functions of interest, making it

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challenging to ascribe a single functionality to what is in reality a diverse and dynamic system of

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interacting molecules3-6.

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Colloids govern the transport, speciation, and toxicity of dissolved trace metals in natural

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waters, and the properties of the smallest colloids dominate the behaviour of colloidal systems

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because of their greater surface area1,7,8. While biological variables certainly play a role, trace

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element speciation is a strong predictor of bioaccessibility as recognized by the free ion activity

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and biotic ligand models8-11. Free ions are the most accessible form for most aquatic organisms,

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but ‘truly dissolved’ material may also be biologically available, and in some cases organic-

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metal complexes can promote metal uptake7,11-14. Slight changes in physicochemical conditions

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such as pH and ionic strength can also alter the size, speciation, and conformation of DOM and

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colloidal aggregates6,10,15-17. The same considerations apply to understanding the uptake of

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micronutrients and toxic metals by plants from soil solutions, where adsorption to mineral

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surfaces and transport through pore spaces also play large roles18,19. Hence, reliable methods that

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can measure the smallest colloids and distinguish between 0.45–ߤm filterable metal species (e.g.

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mainly ionic, organic-bound, and primarily inorganic) are needed to accurately measure the size-

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dependence of their bioaccessibility, bioavailability, and toxicity in aquatic and terrestrial

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systems.

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Asymmetrical flow field-flow fractionation (AF4) is rapidly becoming the method of

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choice for the non-destructive separation of organic and inorganic colloids according to size,

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under in-situ conditions20-30. AF4 is compatible with a wide range of detectors and other

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analytical instruments, including ultraviolet absorbance (UV) for measuring the optical

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properties of organic species, and inductively-coupled plasma mass spectrometers (ICPMS) for

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measuring trace element concentrations. Hence, online AF4-UV-ICPMS is a powerful method

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for measuring the size-based distribution of dissolved trace elements amongst primarily ionic

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species, organic-dominated colloids, and primarily inorganic colloids20-24,28,29. AF4-UV-ICPMS

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has also been used to analyze the properties of engineered nanoparticles (ENP) as a function of

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size, and shows promise for distinguishing ENP from natural metal-containing colloids in

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environmental systems26,28.

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Unfortunately, a considerable proportion of small colloids and ‘truly dissolved’ organic-

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trace element complexes are lost through the membrane when pore sizes ≥ 1 kDa are used in

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AF4-UV-ICPMS. These losses limit the analysis and resolution of the smallest colloids near 1

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nm, where higher crossflow rates are required to effectively resolve size differences (> ca. 2–3

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mL min-1)28. Although a membrane with a pore size of 300 Da is available, few research groups

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use this in AF4-UV-ICPMS29,31, and even these groups have used low crossflow rates ≤ 1.5 mL

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min-1. Avoidance of the 300-Da membrane and high crossflow rates have been motivated by

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several challenges, including high pressures, changing membrane conditions, and limited

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calibration options since multi-angle light scattering (MALS) is not useful for sizes < ca. 10

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nm32. Despite these challenges, there are many benefits associated with using this membrane,

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including measuring the distribution of trace elements in the smallest colloids, high resolution in

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the small size range, and high precision.

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In the following we report on considerations for using the 300-Da membrane in AF4-

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ICPMS, and demonstrate the optimization of separation in the lower size range. Statistical

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fractogram deconvolution is applied to achieve complete resolution of overlapping peaks33.

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Advanced quality control methods for achieving high precision by accounting for membrane

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drift are also detailed. Method effectiveness is demonstrated through the triplicate analysis of

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three natural water samples. This work is intended to serve as a guide for academic researchers

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and industrial scientists and engineers seeking to develop similar AF4-ICPMS methods for

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analyzing colloids and ENP using the 300-Da membrane.

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Experimental section

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Samples, reagents, and instrumentation

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Natural waters were collected from two locations in a large river with low DOC and Fe

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concentrations (4‒5 mg C L-1 and 9‒15 µg L-1, respectively, samples AR15 and AR18), and from

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a tributary stream, with high concentrations of DOC and Fe (22 mg C L-1 and 435 µg L-1,

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respectively, sample SR). Sample collection, filtration, and analysis were conducted using

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exceptional measures to ensure the absence of contamination, including acid-cleaning of all

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surfaces and conducting measurements in an ultra-clean metal-free laboratory (see

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Supplementary Information for details).

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Size separation was conducted using a Postnova MF2000 fractionation system with a

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300-Da polyethersulfone (PES) membrane and PN5300 auto-injector (Postnova Analytics, Salt

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Lake City, Utah, USA), coupled to a UV/Visible diode array detector (Agilent Technologies

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G4212 DAD, Santa Clara, California, USA) (Figure S1). Absorbance at a wavelength of 254 nm

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(A254) was used as a proxy for DOM concentration. Although some DOM moieties do not absorb

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light, A254 is linearly proportional to DOM concentration for a given DOM source; however,

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DOM with a higher molar mass absorbs more strongly at longer wavelengths. Thus, using A254

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as a proxy for size-resolved DOM likely underestimates the proportion with higher molar mass.

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The AF4 system was coupled to a quadrupole ICPMS operating in kinetic energy discrimination

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(KED) mode with He collision gas (iCAP-Qc, Thermo Fisher Scientific, Waltham,

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Massachusetts, USA). Carrier fluid was adjusted to match the pH (8.3) and ionic strength (300

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µS cm-1) of samples using a buffer made with ultrapure ammonium carbonate and HCl (Sigma-

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Aldrich, St. Louis, Missouri, USA). A high-pressure injection valve (Rheodyne, California,

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USA) was installed downstream of the AF4 to facilitate the analysis of bulk samples and NIST

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SRM 1640a, to measure the sample recovery and ensure the accuracy of ICPMS measurements

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(see Table S1). Ultrapure indium standard (Spex CertiPrep, Metuchen, New Jersey, USA) and

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ultrapure HNO3 were added to the carrier fluid downstream from the injection valve through a

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micro-mixing tee (IDEX, Lake Forest, Illinois, USA). Importantly, MALS is not useful for size

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measurements in the range of < ca.10 nm, requiring the use of polystyrene-sulfonate Na salt

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(PSS) standards or macromolecules25,27,29,32. The relationship between retention time and molar

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mass (M) was calibrated using the molar mass at peak maximum (Mp) of a mixture of PSS size

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standards measured at an absorbance λ = 270 nm (Mp = 0.89, 3.42, 10.2, and 20.7 kDa; PSS-

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Polymer Standards Service-USA, Inc., Amherst, Massachusetts, with order numbers PSS-pss1k,

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PSS-pss3.4k, PSS-pss10k, and PSS-pss20k) and bromophenol blue measured at λ = 590 nm (M

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= 0.69 kDa; Sigma-Aldrich). The relationship between trace element concentration and ICPMS

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counts per second (CPS) was calibrated using multi-element calibration standard (Spex

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CertiPrep). The considerations and pressure-related constraints associated with using the 300-Da

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membrane and procedures used to calibrate the ICPMS system are discussed at length in the

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Supplementary Information. Instrument settings are shown in Table S2.

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Analytical conditions, membrane drift, and advanced quality control

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Samples were analyzed in triplicate under routine operating conditions and interspersed between

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other samples to provide realistic sources of error; AR15 and SR were analyzed on the same day,

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and AR18 over 13 days. DOM recovery was calculated by dividing the peak area for A254 from

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the fractionated sample by that of the unfractionated sample injected upstream of the channel,

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with the crossflow turned off 27,34. Element recoveries were calculated by dividing the integrated

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peak area of the fractionated sample by that of the unfractionated sample injected downstream of

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the channel under identical conditions. To assess the precision of Mp measurements, Suwannee

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River natural organic matter standard (SRNOM) was obtained from the International Humic

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Substances Society, diluted to a concentration of 10 mg C L-1, and analyzed five times in a single

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day with UV detection.

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The slow accumulation of DOM and iron and manganese oxyhydroxides in the pores and on the surface of membranes rapidly reduces flow through the membrane35-39. Progressive

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deposition on/in the membrane can cause the slow drift and non-linear changes in the focussing

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position and channel thickness, leading to a change in the relationship between retention time

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(tR) and Mp (see Supplementary Information for details). The impact of this drift is demonstrated

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in striking detail by recording the shift in the slope and intercept of the calibration curve over

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time, and calculating the corresponding change in Mp for fixed tR (Figure S2). Accurate size

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measurements for analytes that contain DOM, or iron or manganese oxyhydroxides, therefore

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require routine size calibration. Changes in the LODs were also apparent from day to day as

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material accumulated on and desorbed from the membrane or conditions changed (e.g. when

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cones were changed or the system was idle). Advanced quality control measures were applied to account for changing membrane

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conditions. The AF4 channel was calibrated at the beginning and end of each day (i.e. every 6–8

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samples), and a complete blank analysis was conducted after every sample to minimize and

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monitor memory effects (i.e. complete analysis with eluent as the sample). To minimize the

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accumulation of material near the focussing position, the analysis of all samples and blanks was

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concluded with a 5-minute cleaning cycle (tip flow 4 mL min-1, crossflow off and purge valve

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open). A flow rate of 0.1 mL min-1 was maintained overnight and between analysis days to

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facilitate desorption of material from the membrane surface. Element LODs were calculated

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daily.

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Optimization

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The AF4 separation procedure was adapted from an earlier method27, and optimized to separate

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0.45–ߤm filterable trace elements into species previously related to varying levels of

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bioaccessibility; thus, the ‘truly dissolved’/primarily ionic species (e.g. carbonates, hydroxides

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and, oxyanions), organic-dominated species, and primarily inorganic oxyhydroxides were of

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greatest interest8,10,13,19,40. The flow program was optimized to achieve high resolution in the

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small size range by observing the impact of varying crossflow rates and times on the calibration

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standards, DOM, and key elements with known speciation, while minimizing the loss of material

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through membrane pores.

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Fractogram deconvolution

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Overlapping void, DOM-associated, and iron oxyhydroxide-associated peaks were deconvoluted

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using statistical fractogram deconvolution (detailed in [33]). This region of each fractogram was

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fitted 9000 times for one to three Gaussian peaks. The location, height, and width of each peak

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were optimized using a modified simplex algorithm41. For each number of peaks, three sets of

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1000 fits were conducted, and the fit with the lowest root-mean square error (RMSE) was chosen

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as the best fit from each set. The number of peaks was chosen by comparing the standard

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deviation in peak location and RMSE of the three best fits for each of 1–3 peaks. The peak areas

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from the fit with the lowest RMSE for the chosen number of peaks was used to calculate the

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concentration of each element associated with each peak.

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Results and discussion

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Optimization and calibration

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Optimization was constrained to the following ranges by the pressure limits of the separation

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system: channel outflow rates (OF) of 0.3–0.7 mL min-1, cross flow rates (CF) of 1.0–2.7 mL

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min-1, and inlet flow rates (IF) of 0.1–0.3 mL min-1. Losses were exacerbated by low channel

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flow rates, so OF was fixed at 0.7 mL min-1. Optimal conditions were chosen by comparing the

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recovery and resolution of DOM and trace elements in sample SR over a range of flow

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conditions. The trade off between recovery and resolution was demonstrated by comparing DOM

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and element fractograms while changing CF and focussing time (FT) (Figure 1).

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Figure 1: Impact of crossflow (CF) and focussing time (FT) on recovery and resolution for sample SR with channel and injection flow rates fixed at 0.7 and 0.2 mL min-1, respectively. The percentage recovery of each element corresponding to the peak area is shown.

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A CF of 1.0 mL min-1 provided maximum recovery; however, the inadequacy of the field

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strength was apparent from the large void peak and lack of resolution there from. Resolution was

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improved by increasing the CF to 1.8 mL min-1, drastically reducing the recovery of mainly ionic

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species (e.g. Mg). Resolution from the void peak was slightly improved by increasing CF to 2.7

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mL min-1 suggesting that 1.8 mL min-1 was inadequate, but recoveries of DOM also decreased Page 10 of 26


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by 12.3%. Curiously, recoveries of Cu and Mn appeared to increase slightly as a result of

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increasing CF (by 6.6 and 1.9%, respectively); however, this fell within the range of variation for

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triplicate analyses (Table 1). Extreme losses of Mg and losses of Fe and Mn from the void peak

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and left-hand side of the DOM-associated peak occurred when FT was increased to 10 min., with

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no corresponding increase in the high molar mass fractions. Similarly, DOM with low molar

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mass, Mg, and Cu were lost when CF was increased from 1.0 to 1.8 mL min-1 without a

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corresponding increase in the larger size fractions, suggesting that the majority of lost material

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was ‘truly dissolved’ and primarily ionic species. These results suggested optimized settings of

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1.8 mL min-1 ≤ CF ≤ 2.7 mL min-1 and 5 min. ≤ FT ≤ 10 min. Further testing revealed that

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conditions were optimal with CF = 2.1 mL min-1 and FT = 6 min. Under these conditions, the

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resolution of calibration standards and the linearity of the calibration curve were excellent for the

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injection of both individual standards and mixtures (Figure 2). The slope and intercept of the size

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calibration curve varied over the course of the study, with average values of (± 95% CI):

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log(tR-t0) = [0.527 ± 0.017] • log(M) – [1.346 ± 0.049], R2 = 0.987 ± 0.002

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for n = 6 and void time t0 20,25,42.

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[1],

Stable baselines were desired for optimal fractogram deconvolution, requiring an elution

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time of 29 min. (e.g. Fe, Figure 1). CF was then reduced to zero over 1 min. and an additional 20

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min. was needed to elute the larger material remaining in the channel23,28,43-46. This approach

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offered the advantage of clearly resolving an additional size fraction from the DOM peak: large

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primarily inorganic material. Alternatively, a decaying CF could be used to measure the size of

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the particles across the complete size spectrum; however, since MALS cannot be used in this size

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range the development and fitting of a non-linear calibration curve would be necessary32.

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Figure 2: Fractograms of: (upper) calibration standards injected separately using an aged membrane, (middle) injected as a mixture at the beginning and end of the day with a fresh membrane, and (bottom) SRNOM standard, sample SR, and Fe. The delayed retention times and elevated baseline apparent for the individual standards (upper) are indicative of drifting membrane conditions.

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Resolution, deconvolution, and precision

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The optimized flow program resolved trace elements into four distinct size fractions

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(shown on fractograms in Figure 3): 1) The void peak, containing small material that passed

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through the membrane with sufficiently long focussing time such as primarily ionic species and

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organic-associated elements with low molar mass (i.e. < ca. 300 Da). The void peak can also

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contain unfocussed material such as particles or agglomerates/aggregates which do not elute

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according to the Brownian elution mode; however, the void peak was negligible when the Page 12 of 26


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focussing time was increased during optimization, with no corresponding increase in other size

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fractions (Figure 1). This suggests that incompletely focussed material plays a negligible role in

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the void peak. Additionally, elements expected in primarily hydrated (e.g. [Mg(H2O)6]2+) and

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oxyanion (e.g. MoO42-) forms were almost entirely depleted with increasing focussing time

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during optimization, whereas primarily DOM-associated elements such as Cu did not appear in

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the void peak when field strength was adequate. Further, elements present both as ions and

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oxyhydroxides (e.g. Fe, Mn) were depleted from the void peak with increasing focussing time,

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but not with increasing field strength (Figure 1). This suggests that the behaviour of elements in

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the void peak may be useful as an indicator of speciation, but confirmation of the size and

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morphology of this size fraction under different focussing conditions is required; 2) trace

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elements associated with organic matter; 3) primarily inorganic colloids that elute after the DOM

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peak such as oxyhydroxides that may be associated with larger DOM at low concentration; and

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4) primarily inorganic material >> 20.7 kDa such as oxyhydroxide species and occluded

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elements that were released after the CF was terminated, with negligible contribution from UV-

253

absorbing DOM. Some of the elements in this fraction could be sorbed to the surface of

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oxyhydroxides, but much of the weakly bound material would be removed by tangential shear

255

forces during focussing. Since the trace elements in fractions 2–4 are strongly associated with

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primarily inorganic material and DOM > 0.3 kDa to the extent that they are not stripped by

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tangential shear forces during focussing, these fractions could be used to estimate the relatively

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inaccessible bound fraction in bioacceessibility tests. The remaining material would then

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represent the mainly ionic species that are the most bioaccessible for most aquatic organisms (i.e.

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total concentration – bound fractions). Notably, this bioaccessible fraction then includes the

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entire fraction that was not recovered which overestimates the mainly ionic fraction to the extent

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that DOM-associated trace elements and Fe and Mn oxyhydroxides are adsorbed to the

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membrane surface. Further organism-specific testing is required to adequately ascertain the

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bioaccessibility of these size fractions.

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Figure 3: Size profile of DOM and selected trace elements for samples SR (black) and AR15 (red). X-axis: retention time (s). Y-axis: concentration (ng L-1). The molar masses corresponding to retention times are shown at the top of the figure. Fractions ❶–❹ are discussed in the main text.

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Statistical fractogram deconvolution yielded complete resolution of overlapping peaks for

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fractograms with both high and low signal-to-noise ratios (Figure 4). Deconvolution facilitated

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exact Mp determination, and triplicate analysis revealed that both the separation and

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deconvolution were highly precise (63% of deconvoluted Mp < 10% RSD and 31% < 5% RSD).

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The Mp ± 95% of deconvoluted peaks for all elements and samples are provided in Table S3. For Page 14 of 26


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DOM fractograms, the Mp of SRNOM, AR15, AR18, and SR was 986 ± 6, 970 ± 4, 963 ± 13,

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and 962 ± 6 Da, respectively. The Mp of SRNOM was on the lower end of the range previously

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measured for Suwannee River humic substances using AF4 (i.e. NOM, fulvic acids, and humic

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acids, 0.86–2.7 kDa)13,27,34,42. A lower Mp is expected given the smaller membrane pore size,

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which minimizes the loss of smaller DOM moieties. For ICPMS fractograms in sample SR, the

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deconvoluted Mp ±95% CI for size fractions 2 and 3 were 2.89 ± 0.04 and 14.7 ± 0.5 kDa for

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Fe, and 3.42 ± 0.40 and 17.0 ± 1.4 kDa for Pb. The Mp of Fe peaks were similar to those

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previously estimated in creeks draining a peat bog, without fractogram deconvolution (ca. 1.8

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kDa and 18 kDa)29. The Mp of the DOM-associated Fe fraction was also similar to those of

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seawater-soluble NOM that was isolated from peat bog drainage and pre-separated into three

285

conductivity-based fractions using gel chromatography (Mp ± 99% CI = 2.42 ± 0.90, 2.81 ± 0.10,

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and 3.32 ± 0.10 kDa)47. Interestingly, the Mp of primarily inorganic Pb fractions were

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significantly larger than their corresponding Fe fractions. This suggests that the Pb was either

288

adsorbed to the surface of Fe particles or part of amorphous Fe, since Pb is expelled during the

289

crystallization of goethite and hematite and does not affect the bulk structure of these Fe

290

oxides48. Further study is therefore needed to verify the composition and morphology of these

291

size fractions, and their implications for bioaccessibility.

Page 15 of 26



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Page 16 of 32

292 293 294 295 296

Figure 4: Resolution of size fractions using statistical fractogram deconvolution for trace elements with high (left) and low (right) signal-to-noise ratios for triplicate analysis of sample SR. The dark line shows the fit (sum of the underlying peaks) used to approximate the fractogram (grey).

297

Element speciation, fraction distributions by source, recovery, and LODs

298

The distribution of elements amongst different size fractions generally accorded with their

299

expected speciation and geochemical behaviour (Figure 3, Table S3). Alkali and alkaline-earth

300

metals Ba, Li, Mg, Sr should be present as mainly ionic species such as carbonates or hydrated

301

ions49, and were primarily associated with the smallest size fraction between 0.33–0.89 kDa (nb:

302

Mp < 0.69 kDa were estimated by extrapolating the linear calibration curve and so should be

303

interpreted with caution). Molybdenum and vanadium should exist primarily as molybdate and

Page 16 of 26


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304

vanadate, respectively, and were present primarily in fraction 1 between 0.33–0.59 kDa21,49.

305

Arsenic was distributed in both size fractions 1 and 2, and organophilic metals such as Co, Cu,

306

Ni, and Zn were primarily grouped with DOM-associated fraction 2 (81–97% of retained

307

material). Crustal elements (Y, Pb, Th) and oxyhydroxides of Fe and Mn were primarily

308

distributed amongst DOM-associated and primarily inorganic fractions 2–4 (63–97% of retained

309

material). The coincidence of relatively insoluble Th and Y with Pb, Fe, and Mn in the large

310

primarily inorganic fraction suggests that this fraction is largely crustal material produced by

311

weathering49,50. Curiously, Al was only present in fraction 1 (Mp ~ 0.35‒86 kDa), suggesting the

312

dominance of Al(OH)4- and an absence of aluminosilicate clays49. Although organic Al species

313

could be expected in these waters, both the total 0.45-ߤm filterable concentrations (2.9‒7.6 µg L-

314

1

315

pH near 7 where colloidal Al could not be detected43. In studies using ultrafiltration, Al < 10 kDa

316

also dominated in a range of boreal rivers with pH 7‒8, and were poorly correlated with DOM

317

concentrations50.

) and recoveries (9‒19%) were quite low. These results are similar to Alaskan river waters with

Table 1: Total concentrations and percentage recovered for DOM and representative elements (± 95% CI).

Page 17 of 26



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Page 18 of 32

318

High resolution in the small size range clearly distinguished differences in the

319

distribution of the elements between size fractions for different samples (Figure 3; Table S3).

320

Most obviously, primarily inorganic size fraction 3 was present only in SR, and contained Fe,

321

Pb, and Mn (Mp 15–17 kDa). Fraction 2 of Ba and U were also clearly resolved from fraction 1

322

only in SR. In fraction 1, the Mp of Sr was shifted towards organic-associated fraction 2 in SR

323

(Mp = 0.84 and 5.29 kDa for Sr fractions 1 and 2, respectively), whereas fraction 1

324

overshadowed fraction 2 for Ba, Sr, and U in AR15 and AR18. Alternatively, fraction 2 may

325

have been altogether absent in AR15 and AR18 because of lower DOM concentrations and

326

associated binding capacity (e.g. also compare Mn for AR15 and SR in Figure 3).

327

The recovery of elements varied with speciation and associated source water

328

characteristics (Tables 1, S4). Recoveries were highest for organophilic metals Co, Cu, Ni, and

329

Zn, and were higher in SR (recovery ± 95% CI = 51 ± 16% to 129 ± 8% in SR compared to 22 ±

330

3 to 74 ± 3% in AR15 and AR18). The recovery of As was also significantly higher in SR (11 ±

331

2, 9 ± 2, and 23 ± 7% for AR15, AR18, and SR, respectively). Because the Mp(DOM) was < 1

332

kDa, approximately half of the DOM and associated organophilic elements that were retained by

333

the 300-Da membrane would be lost through the pores of 1-kDa membranes, and would be

334

classified as ‘truly dissolved’ material rather than colloids (e.g. Cu, Ba, Ni, Th, Co, U, Sr, and Y

335

in Figure 3). This finding is further supported by the results of ultrafiltration, which have shown

336

that 24–99, 50–113, 80–100, and 65–104% of dissolved C, Cu, Mg, and Mo in natural waters

337

pass through the 1-kDa membrane50,51. Recoveries of mainly ionic Al, Li, Mg, Sr, and V were

338

considerably lower than for organophilic elements (1.5–19%), suggesting that they are more

339

bioaccessible to most aquatic organisms. The recovery of Mo was higher (25, 21, and 119% for

340

AR15, AR18, and SR) in agreement with other studies that have suggested that Mo is present as

Page 18 of 26


Page 19 of 32

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341

MoO42-, which can be retained by electrostatic repulsion from the negatively charged membrane

342

surface21. The recovery of Ba and U were source-specific, reflecting the differences in their

343

distribution between the organic and void/mainly ionic peaks (7–14% for AR15 and AR18, 51–

344

58% for SR). Recoveries of Fe, Mn, Pb, and Th were variable and no relationship with source

345

was apparent (9–15% for Mn, 18–37% for Fe, Pb, and Th). The recovery of Y was high in all

346

samples due to its strong association with DOM (Figure 3; 84 ± 11 to 102 ± 7%). It is

347

challenging to compare recovery across studies owing to differences in focussing time and field

348

strength (i.e. prioritizing resolution or recovery), preconcentration/sample volume (0.1–45 mL),

349

sample type (river, bog, ground, and soil pore waters), and speciation differences due to different

350

solution/eluent pH and ionic strengths. In general, the recovery of DOM and trace elements was

351

greater than most studies using AF4-ICPMS with 1-kDa or larger membranes, and within the

352

range reported by the two other groups who have used the 300-Da membrane21,22,29,31,51,52. For

353

example, recoveries of Mg, Mo, and Cu have respectively been reported as 0.2–0.4, 15, and 23–

354

40% using the 1-kDa membrane compared to 3–6, 21–120, and 40–103% in this study. Given

355

comparable recovery, coupling higher field strength with fractogram deconvolution provides a

356

significant advantage by providing outstanding precision and resolution in the low size range.

357

The use of peak areas for deconvolution and the absence of integrable peaks in blanks

358

precluded their use for determining LODs; hence, LODs were determined using the calibration

359

curve parameters22,53,54. The procedures used to perform ICPMS calibration and calculate LODs

360

on each analysis day are detailed in the Supplementary Information. Given the range of

361

instrumentation (e.g. AF4 and ICPMS system models), ICPMS operating modes (e.g. high vs.

362

low resolution, collision vs. normal mode), components (e.g. carrier solution characteristics and

363

purity, eluent reservoir material), and calculation methods (e.g. peak area in blanks, baseline

Page 19 of 26



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Page 20 of 32

364

standard deviation, variability of calibration curve parameters) it is also challenging to compare

365

LODs across studies. In general, the LODs in this study were within the range of reported values;

366

however, they exhibited considerable variation from day to day and differed for each element

367

due to changing instrumental conditions and accumulation of material on the membrane. (Table

368

S5). For example, the detection limit for Li varied over three orders of magnitude, from 0.005–

369

0.153 ߤg L-1, whereas the LOD for Mo was relatively stable (0.015–0.035 ߤg L-1). This

370

emphasizes the importance of calculating LODs on a daily basis in AF4-ICPMS, rather than

371

reporting a single ‘best case’ value.

372 373

Conclusions AF4-ICPMS using the 300-Da membrane with fractogram deconvolution has resolved

374 375

colloids in the lower size range and precisely determined their size distribution, while also

376

retaining mainly ionic species. In some cases, the ionic species were associated with the void

377

peak and in other cases they were associated with a slightly larger size fraction. The responses of

378

the void peak/smallest size fraction and more stable larger size fractions were compared under a

379

range of separation conditions during optimization, providing additional information about the

380

material and suggesting that much of the material in the void peak may be ionic species;

381

however, detailed characterization of these size fractions is required to verify their chemical

382

forms.

383

A considerable proportion of retained trace metals were associated with DOM that was
> 20.7 kDa which were eluted after the crossflow was terminated. Given the necessary

395

compromise between resolution and recovery and the impact of field strength on the linear range,

396

the analysis of colloids using AF4-ICPMS should be optimized on an application-specific basis

397

with due attention paid to the behaviour of both DOM and a spectrum of elements with known

398

differences in size/species under a range of flow conditions. Ideally, this would include flow

399

programs optimized to maximize resolution in all size ranges, and changing the focussing time

400

and field strength to assess the potential presence of small ionic species. Importantly, detailed

401

characterization of the smallest species and associated organic material requires the use of high

402

field strengths and the smallest available pore size, which necessitates working near the pressure

403

limit of the current generation of AF4 systems. Using the 300-Da membrane in AF4-ICPMS may

404

also provide novel information about small engineered nanoparticles and their alteration in

405

natural systems, which may not be feasible using membranes with a larger pore size; for

406

example, tracking the behaviour and fate of degradation and dissolution products in natural

407

systems using isotopically enriched ENP. Future work will involve more detailed

408

characterization of the different size fractions, and the connection of trace element species

409

distributions to bioaccessibility and toxicity under a range of conditions.

Page 21 of 26



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Page 22 of 32

410 411 412

Acknowledgements The authors are grateful for the contributions of M. Donner and T. Noernberg who

413

assiduously applied ultra-clean sampling techniques during field sampling and provided safe

414

transportation on the Athabasca River. We also appreciate the efforts of M. Azim, B. Bicalho,

415

and M.B. Javed, who assisted with the cleaning and packing of sampling equipment. We

416

acknowledge the contributions of the following organizations towards the construction of the

417

SWAMP lab facility, and the funding of this research: Alberta Innovates, the Canadian

418

Foundation for Innovation, the Canadian Oil Sands Innovation Alliance, the Government of

419

Alberta, and the University of Alberta Faculty of Agriculture, Life, and Environmental Sciences.

Page 22 of 26


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420

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Figure 1 190x142mm (300 x 300 DPI)



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

Figure 2 190x142mm (300 x 300 DPI)


Page 28 of 32

Page 29 of 32

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Figure 3 190x142mm (300 x 300 DPI)



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

Figure 4 190x142mm (300 x 300 DPI)


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Page 31 of 32

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


Table 1 190x142mm (300 x 300 DPI)



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

47x26mm (600 x 600 DPI)


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