Consistency of δ 13 C measurements can be improved 39-47% by anchoring the δ 13 C scale with two isotopic reference materials differing substantially in 13 C/ 12 C. It is recommended that δ 13 C values of both organic and inorganic materials be measured and expressed relative to VPDB (Vienna Peedee belemnite) on a scale normalized by assigning consensus values of-46.6‰ to L-SVEC lithium carbonate and +1.95‰ to NBS 19 calcium carbonate. Uncertainties of other reference material values on this scale are improved by factors up to two or more, and the values of some have been notably shifted: the δ 13 C of NBS 22 oil is-30.03‰.

An international project developed, quality-tested, and determined isotope-δ values of 19 new organic reference materials (RMs) for hydrogen, carbon, and nitrogen stable isotope-ratio measurements, in addition to analyzing pre-existing RMs NBS 22 (oil), IAEA-CH-7 (polyethylene foil), and IAEA-600 (caffeine). These new RMs enable users to normalize measurements of samples to isotope-δ scales. The RMs span a range of δ 2 H VSMOW-SLAP values from-210.8 to +397.0 mUr or ‰, for δ 13 C VPDB-LSVEC from-40.81 to +0.49 mUr, and for δ 15 N Air from-5.21 to +61.53 mUr. Many of the new RMs are amenable to gas and liquid chromatography. The RMs include triads of isotopically contrasting caffeines, C 16 nalkanes, n-C 20-fatty acid methyl esters (FAMEs), glycines, and L-valines, together with polyethylene powder and string, one n-C 17-FAME, a vacuum oil (NBS 22a) to replace NBS 22 oil, and a 2 H-enriched vacuum oil. Eleven laboratories from 7 countries used multiple analytical approaches and instrumentation for 2-point isotopic calibrations against international primary measurement standards. The use of reference waters in silver tubes allowed direct calibration of δ 2 H values of organic materials against isotopic reference waters following the principle of identical treatment. Bayesian statistical analysis yielded the mean values reported here. New RMs are numbered from USGS61 through USGS78, in addition to NBS 22a. Due to exchangeable hydrogen, amino acid RMs currently are recommended only for carbon-and nitrogen-isotope measurements. Some amino acids contain 13 C and carbon-bound organic 2 Henrichments at different molecular sites to provide RMs for potential site-specific isotopic analysis in future studies.

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Interlaboratory studies in measurement science, including key comparisons, and meta-analyses in several fields, including medicine, serve to intercompare measurement results obtained independently, and typically produce a consensus value for the common measurand that blends the values measured by the participants.Since interlaboratory studies and meta-analyses reveal and quantify differences between measured values, regardless of the underlying causes for such differences, they also provide so-called 'top-down' evaluations of measurement uncertainty.Measured values are often substantially over-dispersed by comparison with their individual, stated uncertainties, thus suggesting the existence of yet unrecognized sources of uncertainty (dark uncertainty). We contrast two different approaches to take dark uncertainty into account both in the computation of consensus values and in the evaluation of the associated uncertainty, which have traditionally been preferred by different scientific communities. One inflates the stated uncertainties by a multiplicative factor. The other adds laboratory-specific 'effects' to the value of the measurand.After distinguishing what we call recipe-based and model-based approaches to data reductions in interlaboratory studies, we state six guiding principles that should inform such reductions. These principles favor model-based approaches that expose and facilitate the critical assessment of validating assumptions, and give preeminence to substantive criteria to determine which measurement results to include, and which to exclude, as opposed to purely statistical considerations, and also how to weigh them.Following an overview of maximum likelihood methods, three general purpose procedures for data reduction are described in detail, including explanations of how the consensus value and degrees of equivalence are computed, and the associated uncertainty evaluated: the DerSimonian-Laird procedure; a hierarchical Bayesian procedure; and the Linear Pool. These three procedures have been implemented and made widely accessible in a Web-based application (NIST Consensus Builder).We illustrate principles, statistical models, and data reduction procedures in four examples: (i) the measurement of the Newtonian constant of gravitation; (ii) the measurement of the halflives of radioactive isotopes of caesium and strontium; (iii) the comparison of two alternative treatments for carotid artery stenosis; and (iv) a key comparison where the measurand was the calibration factor of a radio-frequency power sensor.

This paper reports the results of an international interlaboratory study led by the National Institute of Standards and Technology (NIST) on the measurement of high-pressure surface excess carbon dioxide adsorption isotherms on NIST Reference Material RM 8852 (ammonium ZSM-5 zeolite), at 293.15 K (20 °C) from 1 kPa up to 4.5 MPa. Eleven laboratories participated in this exercise and, for the first time, high-pressure adsorption reference data are reported using a reference material. An empirical reference equation [nex-surface excess uptake (mmol/g), P-equilibrium pressure (MPa), a = −6.22, b = 1.97, c = 4.73, and d = 3.87] along with the 95% uncertainty interval (Uk = 2 = 0.075 mmol/g) were determined for the reference isotherm using a Bayesian, Markov Chain Monte Carlo method. Together, this zeolitic reference material and the associated adsorption data provide a means for laboratories to test and validate high-pressure adsorption equipment and measurements. Recommendations are provided for measuring reliable high-pressure adsorption isotherms using this material, including activation procedures, data processing methods to determine surface excess uptake, and the appropriate equation of state to be used.Electronic supplementary materialThe online version of this article (10.1007/s10450-018-9958-x) contains supplementary material, which is available to authorized users.

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SummaryDevelopment of reliable cell-based nanotoxicology assays is important for evaluation of potentially hazardous engineered nanomaterials. Challenges to producing a reliable assay protocol include working with nanoparticle dispersions and living cell lines, and the potential for nano-related interference effects. Here we demonstrate the use of a 96-well plate design with several measurement controls and an interlaboratory comparison study involving five laboratories to characterize the robustness of a nanocytotoxicity MTS cell viability assay based on the A549 cell line. The consensus EC 50 values were 22.1 mg/L (95% confidence intervals 16.9 mg/L to 27.2 mg/L) and 52.6 mg/L (44.1 mg/L to 62.6 mg/L) for positively charged polystyrene nanoparticles for the serum-free and serum conditions, respectively, and 49.7 µmol/L (47.5 µmol/L to 51.5 µmol/L) and 77.0 µmol/L (54.3 µmol/L to 99.4 µmol/L) for positive chemical control cadmium sulfate for the serum-free and serum conditions, respectively. Results from the measurement controls can be used to evaluate the sources of variability and their relative magnitudes within and between laboratories. This information revealed steps of the protocol that may need to be modified to improve the overall robustness and precision. The results suggest that protocol details such as cell line ID, media exchange, cell handling, and nanoparticle dispersion are critical to ensure protocol robustness and comparability of nanocytotoxicity assay results. The combination of system control measurements and interlaboratory comparison data yielded insights that would not have been available by either approach by itself.

According to the Guide to the Expression of Uncertainty in Measurement (GUM) (1995, Geneva, Switzerland: International Organization for Standardization (ISO)), the uncertainty in an estimate of the value of a measurand is assessed by propagating the uncertainty in estimates of values of input quantities, based on a measurement equation that expresses the former value as a known function of the latter values. However, in measurement situations where some of the input quantities in turn depend on the measurand, this approach is circuitous and ultimately impracticable. An alternative approach starts from the observation equation, which relates the experimental data to the measurand: this allows a uniform treatment of the most diverse metrological problems, and, once it is used in the context of Bayesian inference, also facilitates the exploitation of any information that may pre-exist about the measurand, alongside the information that fresh experimental data provide about it. The widest applicability of the observation equation approach is illustrated with detailed examples concerning the lifetime of mechanical parts, the measurement of mass, the calibration of a non-linear model in biochemistry and the estimation of a consensus value for arsenic concentration in a sample measured by multiple laboratories.