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A 2D IR center-line slope is obtained by locating peak maxima in a sequence of slices through a two-dimensional infrared spectroscopy feature, then fitting the line traced by those maxima. Its definition includes which coordinate is held fixed, which coordinate is maximized and which coordinate is plotted vertically. Those choices constitute the 2D IR slope-axis convention.

Let $x$ denote excitation-frequency offset and $y$ detection-frequency offset. For a positive bivariate Gaussian envelope with widths ${\sigma }_x,{\sigma }_y$ and correlation coefficient $\rho$, completing the square gives the slice maximumReversing the slicing direction givesThese conditional-maximum slopes are generally not reciprocals. For an original example with $\rho =0.6$, ${\sigma }_x=10$ and ${\sigma }_y=20$ in compatible frequency units, they are $1.2$ and $0.3$. Taking $1/1.2$ would not produce the reverse-slice value. Their product is ${\rho }^2=0.36$ for this particular Gaussian model.

A simple linear regression estimator from slice maxima $(x_i,y_i)$ isThis fits the maxima, rather than calculating a spectral-intensity-weighted covariance. Weak slices, overlapping features or asymmetric peaks can move their maxima and change the fitted line. The fitting interval, baseline treatment and slice direction therefore belong with the reported value.

The primary 2007 method uses a specified inverse-slope convention and tests its relation to the vibrational frequency-frequency correlation function using calculated spectra.^[ref] Its convention must be read before comparing its observable with a raw geometric slope or a software output.

For a validated response model, measuring the slope at successive 2D IR waiting times can reveal changing 2D IR line-shape correlation. A single fit does not by itself identify a molecular relaxation time, and finite time windows or signed spectral contributions require attention before treating a spectral feature as a probability envelope.

2D IR diagonal-peak overlap is the contribution of extended diagonal spectral features to regions where an off-diagonal signal might be assigned in two-dimensional infrared spectroscopy. A nonzero value at an off-diagonal coordinate is not sufficient evidence for a separate 2D IR cross peak.

Peak orientation matters as well as width. An original positive-envelope model for a diagonal feature centered at $(c,c)$ isHere $\rho$ specifies 2D IR line-shape correlation. It is a shape parameter in this illustrative model, not automatically a measured population correlation. Let $S=G_a+G_b$ for two diagonal features, with $\Delta =b-a$. At the off-diagonal coordinate $(a,b)$,For original values $a=1600$, $b=1650$ and $\sigma =20$, all in $\mathrm{c}{\mathrm{m}}^{-1}$, the off-diagonal amplitude is approximately 0.0879 at $\rho =0$, but only 0.000340 at $\rho =0.8$. Both models have the same individual marginal width. An overlap estimate based only on that width can therefore miss a substantial shape dependence.

The toy envelope is positive. Actual coherent spectra can contain signed features, so fitting and subtraction must respect the measured response and phase convention. The primary phase-controlled peptide experiment used phase and amplitude information to interpret mode-coupling cross peaks.^[ref]

Useful tests vary the fit region, include plausible neighboring features and compare the proposed cross signal across 2D IR waiting times and other controlled conditions. A residual can indicate a missing feature, an unsuitable diagonal line shape or an unmodeled background. Its persistence alone does not uniquely select one molecular explanation.

Spectral diffusion is the time-dependent wandering of a transition frequency as its local environment changes. A collection of molecules can retain the same instantaneous frequency distribution while individual members lose memory of their earlier frequencies. In the infrared spectroscopy setting, vibrational spectral diffusion concerns vibrational transitions. The name describes frequency evolution, not necessarily spatial diffusion of the molecules.

A useful original stationary model writes $\omega (t)=\overline{\omega }+\delta \omega (t)$ and defines the autocovariance functionFor a real stationary process, its frequency-increment variance isThis follows by expanding the square: stationarity makes the two squared-frequency terms equal, while the cross term is $2C(T)$. The relation supplies a spectral-diffusion frequency-increment variance without assuming that the mean transition frequency shifts.

Take the stipulated model $C(T)={\Delta }^2{e}^{-|T|/\tau }$, with $\Delta =1\mathrm{rad}\mathrm{p}{\mathrm{s}}^{-1}$ and $\tau =2\mathrm{ps}$. After 2 ps, normalized memory is $e^{-1}\approx 0.368$ and the root-mean-square frequency change is $\sqrt{2(1-e^{-1})}\approx 1.124\mathrm{rad}\mathrm{p}{\mathrm{s}}^{-1}$. The long-time increment spread approaches $\sqrt{2}\Delta$, while the single-time spread remains $\Delta$. Thus loss of temporal correlation need not broaden the stationary ensemble distribution over time.

A static distribution offers a contrasting physical limit. Molecules may have different, but individually fixed, frequencies. Then $C(T)=C(0)$ and the increment variance is zero despite a broad ensemble spectrum. A single spectral line width cannot determine how much of that diversity is static or dynamically sampled.

In two-dimensional infrared spectroscopy, controlled infrared pulses first label frequency and later interrogate the response after a 2D IR waiting time. Repeating over waiting times tests the frequency relation rather than merely measuring one ensemble average.^[ref] Its calculated spectra illustrate the associated evolution of spectral geometry.^[ref]

Environmental fluctuations, population loss and exchange between resolved species affect spectra in different ways. A changing 2D IR line-shape correlation is useful evidence only with an appropriate response model and controls. A fitted time scale is not automatically a molecular transport coefficient or the lifetime of every environmental structure.

A vibrational frequency-frequency correlation function describes how deviations of a vibrational transition frequency at two times vary together. For a real stationary process $\omega (t)$ with mean $\overline{\omega }$, defineThis is an autocovariance function. It has squared-frequency units, and $C(0)$ is the variance of the frequency fluctuations. If $C(0)>0$, the normalized quantity $c(T)=C(T)/C(0)$ starts at one. Normalizing removes the fluctuation amplitude, so two systems can have the same $c(T)$ but different $C(0)$.

Vibrational spectral diffusion is the associated evolution of transition frequencies in their changing molecular surroundings. Decay of their correlation need not mean loss of vibrational population.

An original exponential example isWith $\Delta =0.5\mathrm{rad}\mathrm{p}{\mathrm{s}}^{-1}$ and $\tau =2\mathrm{ps}$, $C(0)=0.25\mathrm{ra}{\mathrm{d}}^2\mathrm{p}{\mathrm{s}}^{-2}$ and $C(2\mathrm{ps})\simeq 0.0920\mathrm{ra}{\mathrm{d}}^2\mathrm{p}{\mathrm{s}}^{-2}$, while $c(2\mathrm{ps})=e^{-1}$. The mean transition frequency has not been specified by these fluctuation parameters.

The function also connects a frequency model with coherence decay. For a zero-mean classical Gaussian process, neglect population loss and other broadening, and let accumulated phase be $\varphi (t)={\int }_0^t\delta \omega (s)ds$. Its Gaussian average givesFor the exponential model,Thus the chosen example has $g(2\mathrm{ps})=e^{-1}$ and coherence factor about $0.692$. These are stipulated model values, not an experimental fit.

The 2007 primary analysis relates vibrational spectral diffusion to changing 2D IR line-shape correlation. Three controlled infrared pulses label an initial frequency and later read out the response, with the intervening 2D IR waiting time varied.^[ref] A measured 2D IR center-line slope can constrain such dynamics under appropriate response approximations; it is not a direct time trace of each molecule's frequency. Vibrational relaxation and instrumental effects need separate consideration.