Daryl Wilding-McBride is an author of research on computational analysis of mass spectrometry data. A 2022 paper with collaborators developed processing methods for precursor and fragment spectra acquired on a timsTOF instrument, with the aim of improving peptide feature characterization and identification.^[ref]

One useful contribution concerns detector saturation. Strong isotopic peaks can cease to represent their true relative intensities. The paper uses a theoretical model of tryptic-peptide peak-height ratios and an unsaturated later isotope to infer earlier peak intensities, including the monoisotopic peak. This is model-based reconstruction, not a second direct measurement of a saturated signal.^[ref]

For an original calculation illustrating the inference, suppose a declared model predicts $I_0/I_j=2.5$ and an unsaturated later peak has intensity $I_j=400$ arbitrary units. The inferred earlier intensity is ${\hat{I}}_0=2.5\times 400=1000$ units. The arithmetic is straightforward; whether it is informative depends on the appropriateness of the isotope-ratio model and the reliability of the unsaturated measurement.

The paper evaluates such processing in defined proteomic mixtures. Its detector threshold and applicable isotope/mass ranges belong to that data-processing setup, rather than constituting universal limits for every mass spectrometer. The example illustrates how a software correction can recover a useful estimate while retaining dependence on its physical and statistical assumptions.^[ref]

H. D. Nelson is credited with C. L. De Ligny as an author of experimental hydrocarbon solubility measurements published in 1968. The NIST hexane/water record identifies their joint work as its original measurement source. The verified byline supports this contribution without supplying a fuller identity or an individual division of laboratory tasks.^[ref]

The recorded method equilibrates the liquid system, samples the aqueous solution with a microsyringe, and uses gas chromatography with flame-ionization detection. It addresses a quantitative question: how much hexane is dissolved in water, rather than simply whether two visible bulk liquid regions remain.^[ref]

An invented instrument calibration example illustrates why a detector reading is not itself a concentration. Suppose peak area obeys $A=A_0+sc$, with blank area $A_0=20$, response slope $s=10$ area units per $\mu \mathrm{mol}{\mathrm{L}}^{-1}$, and sample area $A=200$. Then $c=(200-20)/10=18\mu \mathrm{mol}{\mathrm{L}}^{-1}$. These are teaching values, not measurements or calibration constants from Nelson and De Ligny's paper. The example separates equilibration, sampling and detector response, all of which matter when interpreting a small dissolved signal.

C. L. De Ligny is listed with H. D. Nelson as an author of 1968 measurements of hydrocarbon solubility in water. The IUPAC-NIST record for the hexane/water system names this publication as its original experimental source and records temperature-dependent measurements. It does not identify which coauthor performed each laboratory operation.^[ref]

This work is relevant to the quantitative meaning of liquid immiscibility. A bulk system can contain separate liquid regions while its aqueous phase still contains a small, measurable amount of hydrocarbon. Equilibrium composition and the total quantity recovered from an aliquot answer different questions.

For an original calculation, suppose a homogeneous solution has hydrocarbon mole fraction $x=3\times 10^{-6}$ and total amount of substance $n=1\mathrm{mol}$. It contains $xn=3\mu \mathrm{mol}$ hydrocarbon. A representative aliquot containing $0.1\mathrm{mol}$ of the solution contains $0.3\mu \mathrm{mol}$ hydrocarbon while retaining the same mole fraction. These chosen values do not reconstruct the NIST table.

The database describes microsyringe sampling and gas chromatography with flame-ionization detection, connecting the small dissolved amount to a measurement procedure.^[ref] Sample size, composition and instrument response must be distinguished before comparing solubility measurements.

Sören-Oliver Deininger is an author of research on processing and interpreting mass spectrometry imaging data. His 2011 paper with collaborators on MALDI imaging normalization lists an affiliation with Bruker Daltonik in Bremen. It examines how spectrum-wide scaling can improve comparisons but also create misleading spatial patterns.^[ref]

One issue is total ion current normalization, which scales a spectrum using its summed signal. A strongly localized component can change that denominator and alter the normalized appearance of other signals. The paper compares normalization choices and illustrates such effects instead of treating one scaling rule as a universal correction for all imaging datasets.^[ref]

An original two-spectrum example shows the mechanism. Suppose a selected signal has raw intensity 10 in both regions, while the remaining summed intensity is 90 in one region and 190 in the other. Their totals are 100 and 200. Dividing by each total gives selected-signal fractions $10/100=0.10$ and $10/200=0.05$. The normalized signal differs twofold even though its stipulated raw intensity did not change.

These invented values do not reproduce the paper's data and do not identify molecular concentrations. They isolate the effect of changing the denominator. A normalization rule must therefore be interpreted alongside sample composition, measurement effects and the comparison it is intended to support. Deininger and colleagues' contribution makes this analytical choice part of the scientific interpretation of an image, rather than an invisible formatting step.

Michael Karas is a researcher associated with the experimental development of MALDI mass spectrometry. His 1985 paper with collaborators examined how laser wavelength and sample absorption influence ion formation from organic solids. The paper lists an affiliation with the Institute of Biophysics at the University of Frankfurt.^[ref]

A central experiment mixed alanine with tryptophan, which absorbs at 266 nm. At that wavelength, the mixture yielded an alanine-related ion at about one tenth of the irradiance required for spectra of alanine alone. The absorbing matrix therefore changed the conditions under which the other component could be observed, rather than merely adding its own peak to the spectrum.^[ref]

An original pulse-accounting example shows why the measurement conditions matter. If a uniform pulse has irradiance $I=10^7\mathrm{W}\mathrm{c}{\mathrm{m}}^{-2}$ for $\tau =10\mathrm{ns}$, its radiant exposure isAt the same irradiance, a one-nanosecond pulse gives $0.01\mathrm{J}\mathrm{c}{\mathrm{m}}^{-2}$. These chosen values are not the experimental thresholds. Equal irradiance does not imply equal deposited energy per area when pulse duration differs.

The original study discussed competing mechanisms for the observed wavelength and matrix effects rather than establishing one universal ionization pathway.^[ref] That combination of controlled comparison and mechanistic alternatives makes the work scientifically informative beyond a single improved signal. A university newsletter with a portrait and development-era apparatus photograph provides a primary visual reference.

B. K. Matuszewski is an author of experimental research on quantitative high-performance liquid chromatography with tandem mass spectrometry. A2003 paper with collaborators, listing an affiliation at Merck Research Laboratories in West Point, Pennsylvania, develops ways to distinguish an analytical matrix effect from extraction recovery. The byline identifies the author with these initials.^[ref]

The scientific problem is that an instrument signal can change even when the amount of analyte reaching the measurement is unchanged. Other constituents of the sample matrix can suppress or enhance the response. Conversely, analyte can be lost during sample preparation without changing the response per unit amount. A low measured signal therefore need not identify which mechanism occurred.

An original three-preparation example separates these effects under a linear-response assumption. Suppose equal nominal amounts of an analyte are introduced into: a clean solvent standard; a blank sample extract after extraction; and a sample before extraction. Match the final volumes and measurement conditions. Let the resulting background-corrected signals be $A=100$, $B=80$ and $C=60$ arbitrary units, respectively. The dimensionless ratios areThe last ratio is the product of the first two. Calling the overall60percent response an extraction recovery would conflate analyte loss with ion suppression. This is an invented calculation, not a reconstruction of the paper's validation data. It assumes that the post-extraction addition represents the matrix response experienced by the extracted analyte.

Matuszewski and colleagues also compared ion-spray and heated-nebulizer interfaces while holding sample preparation and chromatographic conditions otherwise the same. For their investigated compound, a matrix effect appeared with the ion-spray interface and was absent with the heated nebulizer.^[ref] That controlled comparison shows why selectivity in mass spectrometry must be tested for the actual method and sample matrix, rather than inferred from the detector's nominal specificity.

Alan Townshend is an analytical chemistry researcher associated with the University of Hull in the full byline of the 2002 IUPAC recovery recommendations. He also coauthored the 1999 recommendations classifying amplification and related analytical reactions. These contributions distinguish physical chemical operations that can otherwise be hidden behind a single instrument response.^[ref][ref]

The amplification report extends the earlier treatment to include reactions that do not multiply the original chemical species. In a chemical multiplication reaction, a cycle produces a multiplied number of the same species before measurement. Other chemical amplification arrangements can increase the amount available for measurement through a different sequence or accumulation process. The chemical transformation and the mathematical growth law must both be specified.^[ref]

An original idealized counting example shows the difference. Suppose a complete reaction cycle converts each available target species into three of the same species, with enough additional reagent supplied and no losses. Starting with $N_0$ targets, four cycles giveBy contrast, an arrangement that adds $2N_0$ measurable product units per cycle while retaining the initial $N_0$ gives $N_n=N_0(1+2n)$, hence $N_4=9N_0$. The two arrangements agree after one cycle but diverge thereafter. These are stipulated growth laws, not measured yields or a recipe from the paper.

Conservation of mass still applies: the additional material comes from reagents, not from creating matter. In practice, incomplete reactions, changing conditions or depletion can prevent the ideal cycle multiplier from remaining constant. An experimental comparison would therefore measure the product after different cycle counts, together with reagent blanks and known starting amounts, rather than infer a multiplication law from one enlarged signal.

Chemical amplification also differs from simply multiplying an electrical readout by a constant. Increasing the displayed signal need not improve signal-to-noise ratio if background or measurement noise is increased with it. The report's broader classification is useful precisely because similar-looking signal gains can arise from different chemical and measurement processes.

Townshend's recovery recommendations address the complementary question of material lost before the readout. Extraction recovery and apparent recovery describe different ratios, so an amplified signal or a result agreeing with a standard does not by itself demonstrate complete analyte recovery.^[ref]

Michael Thompson is an analytical chemistry researcher named at Birkbeck College in the 1999 international guidelines on using recovery information in analytical measurement. The paper addresses how losses during sample preparation affect reported results, their comparability and their measurement uncertainty. Its authorship and affiliation identify this contribution without conflating other scientists of the same name.^[ref]

A central distinction is between correcting an estimated loss and treating the correction as perfectly known. In a specified recovery correction model,where $x$ is the uncorrected result and $r$ the estimated fractional recovery. The guide treats the determination's uncertainty and the recovery estimate's uncertainty separately. For independent inputs, first-order propagation givesThis states an uncertainty calculation, not a rule that every analytical measurand must be defined identically.^[ref]

For an original example, stipulate $x=8.0mg/kg$ with standard uncertainty $u(x)=0.20mg/kg$ and $r=0.80$ with $u(r)=0.04$. The corrected result is $10.0mg/kg$ andTreating recovery as exact would give only $u(x)/r=0.25mg/kg$. The added uncertainty describes limited knowledge of the correction; omitting it does not make the physical measurement more reliable. Correlated estimates require the appropriate covariance term instead of this independent-input formula.

The experiment used to estimate recovery also matters. An added, readily dissolved analyte may not behave like native analyte strongly bound within the sample matrix. The guidelines discuss this limitation of surrogate recovery studies and the need to consider matrix variation.^[ref] A useful validation therefore specifies what material was added, when it was added, which matrices were represented and whether the procedure had time to equilibrate.

Thompson and his collaborators' reporting recommendations call for identifying whether results are recovery corrected and how the correction was obtained. This makes two laboratories' numbers interpretable: a corrected estimate of total analyte and a reproducible result defined by a particular extraction procedure can otherwise look like measurements of the same quantity when they are not.^[ref]

Klaus Danzer is an analytical chemistry researcher who prepared the 1998 IUPAC recommendations on single-component measurement calibration with Lloyd A. Currie. The paper identifies an affiliation at Friedrich Schiller University of Jena, and the NIST publication record gives both authors' full names. The recommendations organize how measured signal is related to the amount or concentration of an analyte under specified conditions.^[ref]

An analytical calibration function separates the response model from its use to infer an unknown. For an original linear example, let concentration $c$ be measured in $mg/L$ and signal $y$ in arbitrary instrument units. Suppose known standards at $c=0,1,2,4$ give $y=2,7,12,22$, exactly satisfyingAn unknown giving $y=17$ has inferred concentration $c=(17-2)/5=3mg/L$. Ignoring the intercept instead gives $17/5=3.4mg/L$, an error of about thirteen percent. A nonzero blank response is not automatically analyte in the unknown.

The same example shows why a calibration curve is more than a unit conversion. A small perturbation of the inverse relation givesReadout variation, uncertainty in the intercept and uncertainty in the slope affect the inferred concentration in different ways. Their covariances can matter when the coefficients are fitted together. These are independently derived sensitivities for the stipulated model, rather than experimental values attributed to Danzer's paper.

A practical calibration measures standards and unknowns with compatible preparation, sample matrix and instrument settings. Repeated standards can reveal measurement noise and time-dependent response; a model established up to $4mg/L$ does not by itself validate its extension to $8mg/L$. The recommendation's explicit dependence on specified conditions makes those experimental choices part of the meaning of calibration, not incidental details.^[ref]

Danzer also coauthored the 2002 distinction between extraction recovery and apparent recovery. That distinction prevents agreement after calibration from being mistaken for proof that no analyte was lost in sample preparation.^[ref]

Steven L. R. Ellison is the name printed for an analytical chemistry coauthor of the recovery-information report, with an affiliation at the Laboratory of the Government Chemist in Teddington. Eurachem's later organizer biography identifies the same report among the works of Stephen L. R. Ellison, an LGC Fellow specializing in analytical statistics and measurement uncertainty.^[ref][ref]

The Eurachem biography also identifies him as a principal author of Quantifying Uncertainty in Analytical Measurement and a coauthor of method-validation and laboratory-proficiency-testing guidance. This work develops the statistical interpretation needed to compare analytical results under stated conditions.^[ref]

For an original illustration, two independent uncertainty contributions of $0.30$ and $0.40\mathrm{mg}$ combine to $0.50\mathrm{mg}$ in quadrature. If their correlation coefficient is 0.5 and both enter the model with positive sensitivity, the combined standard uncertainty is insteadThe model determines which inputs contribute and whether covariance matters. Listing more decimal places does not resolve an omitted source of uncertainty. This calculation illustrates the subject of the guidance; it is not experimental data attributed to Ellison.

Aleš Fajgelj is an analytical chemistry researcher whose work includes metrological traceability, recovery information and laboratory quality assurance. The 2011 IUPAC traceability report identifies his affiliation with the International Atomic Energy Agency's Department of Nuclear Sciences and Applications.^[ref]

With Paul De Bièvre, René Dybkær and D. Brynn Hibbert, Aleš Fajgelj prepared recommendations intended to give chemical measurement a consistent account of the creation, meaning and role of metrological traceability. He also coauthored the 1999 recovery-information report, connecting the interpretation of laboratory results with the handling of losses during preparation.^[ref][ref]

An original example illustrates the distinction between documenting a reference and validating a process. Suppose a standard solution's assigned concentration is connected to a specified reference through documented measurements. If an unknown loses 20% of its analyte during preparation while the standards do not, that documented standard alone does not justify treating the unknown's preparation loss as zero. The measurement model must represent the relevant stages and their measurement uncertainty.

Likewise, agreement in an interlaboratory comparison supplies evidence about the compared results, but does not by itself write the missing chain of reference relationships. The value of Aleš Fajgelj's standards work lies in making those distinctions explicit enough for laboratories to communicate what a result means.

Paul Willetts is an analytical chemistry researcher associated with studies of laboratory performance and analytical sampling. The recovery-information report lists him at the MAFF Food Science Laboratory in Norwich, establishing the identity independently of unrelated authors with the same name.^[ref]

With Michael Thompson, Sharron Anderson, Paul Brereton and Roger Wood, Paul Willetts studied collaborative sampling of wheat and green coffee from bulk lots. Multiple samplers allowed the study to distinguish variation under different sampling conditions. Between-sampler variation contributed significantly for two wheat analytes; the coffee results showed significant sampling-repeatability variation without a detected significant sampling-reproducibility contribution.^[ref]

The measurement begins before material reaches the instrument. For an original illustration, two perfectly repeatable preparations might contain $10$ and $14\mathrm{mg}\mathrm{k}{\mathrm{g}}^{-1}$ because the collected portions differ. Repeating each instrument reading a hundred times would not make those portions identical. Measurement repeatability within a portion and measurement reproducibility across the relevant procedures therefore cannot be inferred from the same replication design.

Paul Willetts also coauthored the recovery-information guidance. Together these contributions emphasize that sample matrix, material selection, preparation loss and stated measurement uncertainty belong in the interpretation of an analytical result. They cannot all be assessed by repeatedly reading a single prepared solution.

Roger Wood is an analytical chemistry researcher whose work concerns the reliability and comparability of laboratory results. The 1999 recovery-information report identifies him with the MAFF Food Science Laboratory in Norwich and lists him among its five authors.^[ref]

The report addresses a practical difficulty: two laboratories can obtain different answers from the same material if one applies a recovery correction and the other reports the recovered amount. It develops a framework for estimating, applying and reporting recovery information rather than treating a percentage as a context-free property of a method.^[ref]

Roger Wood also supported the launch of the RSC Analytical Methods Committee's Technical Brief series while chairing that committee. The RSC's own account credits Michael Thompson as the instigator and Roger Wood as a strong supporter; the first brief appeared in 2000.^[ref]

An original numerical illustration shows why laboratory agreement needs interpretation. If one laboratory recovers $8\mathrm{mg}$ from material containing $10\mathrm{mg}$ and reports 8, while another applies an appropriate recovery estimate 0.8 and reports 10, their discrepancy can arise from reporting conventions without a difference in recovered amount. Conversely, laboratories sharing an unsuitable correction can agree and remain biased. Interlaboratory comparison and clear reporting answer complementary questions about a measurement system.

Sharron Anderson is an author of research on analytical sampling and laboratory measurement of food. The primary byline of the2002 collaborative sampling study identifies her with Michael Thompson, Paul Willetts, Paul Brereton and Roger Wood.^[ref]

The team had multiple samplers collect portions of wheat and green coffee from bulk material. Its findings separated variation under different collection conditions, showing that the variability observed for one food could not simply be assumed for the other.^[ref]

An original example illustrates the experimental logic. Repeated readings of one extract might give10.0,10.1 and9.9 concentration units, while a second independently collected portion gives14.0,14.1 and13.9. Each extract is measured consistently, but the collection-to-collection difference is much larger. A study replicating only instrument readings would miss that component of measurement uncertainty.

Sharron Anderson's documented coauthorship concerns this joint experimental investigation. The scientific lesson is to replicate the stage whose variation is at issue: additional measurements of the same material cannot substitute for independent collections when assessing analytical sampling uncertainty.

Paul Brereton is a researcher in food safety, quality and authenticity. Queen's University Belfast identifies his interdisciplinary research on risks to the food system and his coordination of the TRACE and FOODINTEGRITY projects. The corresponding Fera project biography corroborates those scientific activities.^[ref]

Paul Brereton coauthored the collaborative trial in which multiple samplers collected wheat and green coffee from bulk lots. The design assessed variation arising in analytical sampling, rather than restricting replication to repeated laboratory readings.^[ref]

An original food-authenticity illustration distinguishes checking a product's composition from checking a label's claim. A mixture containing90 units of ingredient A and10 of B still totals100 units. A method measuring only total material would not reveal the substitution; a selective measurement for B or a validated compositional pattern could. Which observation answers the authenticity question depends on a defined measurand, representative analytical sampling and suitable comparison materials.

This is the scientific connection between laboratory method development and food-system investigation: an analytically accurate result must measure a property relevant to the claim being tested. A method's detection capability for one known substitution does not establish universal detection of every possible substitution.

Paul De Bièvre was a researcher in isotope ratio measurements, reference materials and chemical metrology. His IUPAC Council biography records leadership in isotope mass spectrometry and isotope reference materials at the European Commission's nuclear-measurement laboratory, later the Institute for Reference Materials and Measurements.^[ref]

With René Dybkær, Aleš Fajgelj and David Brynn Hibbert, Paul De Bièvre prepared the2011 IUPAC report on metrological traceability of chemical measurement results. The report's stated aim is a consistent view of traceability's creation, meaning and role in chemical measurement.^[ref]

An original isotope-counting example shows why a ratio must be specified. If a hypothetical two-isotope material contains80 units of isotope A and20 of isotope B, its ratio A/B is4, while the fraction A/(A+B) is0.8. Multiplying a sample's total amount by ten changes neither value. An instrument response ratio still requires a measurement model relating its signals to those isotope amounts; unequal response to the two isotopes can alter the observed ratio.

This connects isotope measurements with reference material characterization and measurement calibration. A precise signal ratio is useful only when its relation to the specified measurand and reference is established, with appropriate measurement uncertainty. Paul De Bièvre's documented work joined those experimental and conceptual aspects.

René Dybkær worked on measurement concepts and terminology in laboratory medicine. The IUPAC traceability report identifies him with the Department of Standardization in Laboratory Medicine at Frederiksberg Hospital, Denmark, and names him as a coauthor with Paul De Bièvre, Aleš Fajgelj and David Brynn Hibbert.^[ref]

The report develops a shared account of metrological traceability for chemical measurement. Such terminology matters because a result must identify the measurand and reference relationship before numerical comparisons become meaningful. A chemical name, number and unit together specify more than an instrument indication alone.

For an original conversion example, a stipulated compound with molar mass $100\mathrm{g}\mathrm{mo}{\mathrm{l}}^{-1}$ at $2\mathrm{mmol}{\mathrm{L}}^{-1}$ has mass concentration $200\mathrm{mg}{\mathrm{L}}^{-1}$. The numbers2 and200 describe equivalent content under that specification, rather than disagreement between laboratories. Conversely, equal numbers attached to different quantity definitions need not be equivalent. Uncertainty in the inputs also belongs in the conversion's measurement uncertainty.

René Dybkær's coauthored standards work addresses this need to make the meaning of a measurement communicable. The example is an original illustration of the problem, not data attributed to his experiments or a statement of a clinical decision threshold.

David Brynn Hibbert, also known as Brynn Hibbert, is an analytical chemistry researcher whose work combines chemometrics, electrochemical measurements and metrological traceability. His university profile records his tenure in the UNSW analytical-chemistry chair from1987 to2013 and his editorship of the IUPAC terminology compendium known as the Orange Book. The Royal Society of New South Wales explicitly verifies his full name.^[ref]

With Paul De Bièvre, René Dybkær and Aleš Fajgelj, David Brynn Hibbert coauthored the IUPAC report on the concepts and implementation of metrological traceability in chemical measurement. Its goal was a consistent account of how a result is related to a reference, including the concepts needed to communicate that relationship.^[ref]

An original illustration explains the value of a measurement model in chemometrics. Suppose two ideal sensors give $x_1=c+b$ and $x_2=c-b$, where $c$ is the target concentration and $b$ an interfering response. Their mean recovers $c$, while their difference diagnoses $2b$. If both instead respond as $c+b$, averaging cannot remove the interference. The chemical response model determines what additional measurements identify; having more numbers alone does not guarantee more independent information.

M. C. Asplund is a coauthor of the primary phase-controlled peptide experiment in two-dimensional infrared spectroscopy. The published paper identifies a University of Pennsylvania chemistry affiliation and retains the exact byline M. C. Asplund, alongside M. T. Zanni and R. M. Hochstrasser.^[ref]

The joint experiment measured phase and amplitude of vibrational photon echoes and resolved features interpreted as coupling between amide-I modes. It connected controlled pulse delays with information about molecular interactions.^[ref]

An original response-matrix example clarifies the extra question asked by a multidimensional experiment. Consider two stipulated mapsTheir diagonal values agree, while their off-diagonal responses differ. Looking only at the two diagonal amplitudes would miss that difference. These are illustrative spectral-response entries, not quantum density matrices or reported experimental data.

A 2D IR cross peak can supply a constraint on relationships between vibrations, but its cause must be assessed using the signal phase, timing and physical model. M. C. Asplund's verified contribution is coauthorship of that experimental method; the paper does not by itself assign every individual apparatus or analysis task to one coauthor.

Martin T. Zanni is a chemist developing multidimensional spectroscopy and its applications to molecular structure and dynamics. The University of Wisconsin name/contact on his author-hosted CV agrees with the primary2011 prize-organizer profile, and a primary Optica collaboration record explicitly expands his earlier M. T. byline.^[ref][ref]

With M. C. Asplund and Robin M. Hochstrasser, Martin T. Zanni coauthored the phase-controlled peptide two-dimensional infrared spectroscopy experiment. His later first-person lecture account describes developing mid-infrared spectral pulse shaping to generate the required pulse trains under computer control.^[ref][ref]

An original acquisition-count example illustrates why control technology matters. Stipulate one acquisition for each pair in a two-delay grid. A $64\times 64$ grid requires4096 acquisitions; a $256\times 256$ grid requires65536, sixteen times as many. Averaging, drift and repeat measurements add further demands. Automated timing can make a scientifically useful multidimensional measurement practical, while pulse characterization and controls determine whether the generated sequence matches the intended one.

The calculation is a sampling-design illustration, not an acquisition specification attributed to a particular apparatus. The broader contribution connects instrument control with access to structural and dynamical information that a single-frequency trace can conceal.