CHROMATOSCOPY: AUTOMATED CHEMICAL ANALYSIS VIA IN-COLUMN SPECTROSCOPY

Abstract

The present invention provides a method of chemically analyzing complex mixtures using spectroscopy and chromatography by collecting spectroscopy data at multiple points along a chromatography column to identify and quantify analytes in minutes. Also disclosed is the related system for chromatography and in-column spectroscopy for chemical mixtures and a larger microfluidic system incorporating the chromatography and in-column spectroscopy system.

Claims

1. A method of automated analysis of chemical mixtures, comprising introducing an unknown mixture to a chromatography column; collecting spectroscopic data to observe and characterize the separation of the unknown mixture within and along the chromatography column, and tuning operational parameters of the chromatography column in real time to optimize the separation in an automated fashion.

2. The method of claim 1, wherein temperature is controlled as a function of spatial position along the chromatography column via one or more microscale resistive heating elements.

3. The method of claim 2, wherein the resistance across the one or more heating elements is monitored to measure temperature and provide feedback control to ensure that a targeted temperature is achieved.

4. The method of claim 2, wherein one or more sensing phase sorbents are maintained at a lower temperature than a stationary phase to concentrate the analyte molecules in a sensing phase to better present analyte molecules to a spectrometer.

5. The method of claim 4, wherein temperature of the one or more sensing phase sorbents is actively controlled to dynamically control the concentration of analytes.

6. The method of claim 1, wherein infrared or Raman spectroscopy is used to inform control of gas phase chromatographic separation.

7. The method of claim 6, wherein a spectroscopic signal is enhanced via a multi-bounce attenuated total reflection prism.

8. The method of claim 7, wherein a chamfer angle, ?, of the multi-bounce attenuated total reflection prism is set using the transcendental equation $?=\frac{90?-\arcsin (\frac{\sin \left(90?-?\right))}{n}}{2}.$

9. The method of claim 6, wherein metallic nanostructures are used to enhance the signal.

10. A method of data processing for the analysis of chemical mixtures, comprising introducing a mixture comprising analytes to a chromatography column; collecting a spectrum at multiple points along the chromatography column to form a spectragram, and splitting the spectragram into a chromatographic part comprising a mixing matrix and a spectroscopic part comprising a spectral matrix.

11. The method of claim 10, wherein the spectragram is split using Non-Negative Matrix Factorization.

12. The method of claim 10, wherein the uncertainty is quantified using a Bayesian approach.

13. The method of claim 10, wherein a method of order selection such as the Bayesian Information Criterion or Bridge Sampling is used to determine the number of analytes from the spectragram.

14. The method of claim 10, wherein the mixing matrix is further analyzed to determine solvation parameters.

15. The method of claim 14, wherein Abraham solvation parameters are used to identify analytes, to automate chromatography column temperature, or both.

16. The method of claim 14, wherein solvation parameters of a sorbent are temperature dependent and solvation parameters of the analyte are temperature independent.

17. The method of claim 14, wherein solvation parameters are used to identify analytes.

18. The method of claim 12, wherein the spectral matrix is further analyzed for analyte identification, quantification, or both.

19. The method of claim 12, additionally comprising analyzing the spectral matrix to detect the presence of chemical functional groups.

20. A system for chromatography and in-column spectroscopy for chemical mixtures, comprising: one or more infrared light sources; one or more chromatography columns, each comprising a column body, an infrared transparent prism, a flowing gas mobile phase, one or more sorbents, and multiple probe points along the column body; an input fiber bundle to direct infrared light from the one or more infrared light sources to the multiple probe points; one or more infrared light detectors; an output fiber bundle to send light from the multiple probe points to the one or more infrared light detectors; and temperature control for the one or more sorbents.

21. The system of claim 20, wherein the one or more infrared light source is a tunable laser.

22. The system of claim 20, wherein the one or more infrared detectors is a linear array.

23. The system of claim 20, wherein the one or more infrared detectors is a 2D imaging spectrometer.

24. The system of claim 20, wherein the column body comprises a machinable ceramic.

25. The system of claim 20, wherein the temperature control for the one or more sorbents uses resistive heating and thermometry to minimize the number of required electrical contacts.

26. A microfluidic system comprising the system of claim 20.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0024] FIG. 1 shows flow charts illustrating the two paradigms of chemical analysis.

[0025] FIG. 2 is a schematic overview of the chromatoscopy technique.

[0026] FIGS. 3A and 3B are schematics of two example microfluidic systems incorporating chromatoscopy. FIG. 3A shows a one core system. FIG. 3B shows a four core system.

[0027] FIG. 4 is a sketch of a preferred prism geometry showing a prism with an index of refraction of 4 and a chamfer angle ? of 39 degrees.

[0028] FIG. 5 is a graph showing the enhancement factor ? provided by embedding some fraction of gold in the common sorbent PolyDiMethylSiloxane (PDMS).

[0029] FIG. 6 shows the absorption spectrum of PDMS (top) and a spectrum showing the maximum ?B product that can be employed with PDMS before the signal to noise is compromised by absorptive losses in the sorbent (PDMS).

[0030] FIG. 7 is a schematic of the variable sorbent gain method of signal enhancement.

[0031] FIG. 8 is a demonstration of Monte Carlo NMF analysis of a spectragram.

[0032] FIG. 9 is a demonstration of Monte-Carlo/Bayesian NMF analysis and order selection (inferring the number of analytes) from the spectragram using the Bayesian Information Criteria (BIC). Note that the correct order (3) has the lowest BIC and the median fit (solid lines) is closest to the ground truth (dashed lines). The shaded region shows the 1%-98% inter-quantile range.

[0033] FIG. 10 shows a structure for resistive heating, thermometry, and infrared signal enhancement via SEIRAS.

[0034] FIG. 11 is a schematic of chromatography friendly microfluidic components and a schematic stackup of how the flow can be routed through a chromatography friendly microfluidic system. The flow routing (bottom) connects the micropump and valve shown at the top of the figure.

[0035] FIG. 12 shows the effect of a pump on a chromatography peak.

DETAILED DESCRIPTION OF THE INVENTION

[0036] Broadly speaking, the present invention is to use in-column spectroscopy to automate the process of chemical separation. In this section, we describe several enabling technologies in more detail. Broadly speaking, these technologies fall into the following categories: [0037] Signal enhancementOur previous work on in-column spectroscopy does provide a sensitivity comparable with the end column detectors commonly used in the laboratory today. We present both optical methods of signal enhancement, and methods that exploit the properties of the sorbent to improve sensitivity. [0038] Data AnalysisThe data must be analyzed to infer actionable information about the state of the analyte mixture separation, namely the spectra of the analytes and how the analytes are distributed in the column, what separation scientists call a peak. We also describe how further hierarchical analysis yields additional useful information. [0039] Automation and Temperature ControlArmed with the spectra and the peaks corresponding to each analyte (or some as yet unseparated mixture of two or more analytes), we present an approach to automate the separation process. We also present a method to control and measure the temperature as a function of position along the column. [0040] Microfluidic SystemUnfortunately, traditional microfluidic components are not chromatography friendly because of either the presence of dead volume which degrades the quality of the separation or the use of materials, particularly plastics, that are not chemically inert. Thus, we describe a zero dead volume valve, a chromatography friendly micropump, and a method of chip construction/flow routing that is appropriate for a chromatoscopy based microfluidic system.

Signal Enhancement

Optical Signal Enhancement

[0041] Our earlier work on in-column GCIR spectroscopy used an infrared (IR) transparent prism to enable optical access through a single bounce attenuated total reflection (ATR) prism. This is convenient because it only requires one side of the column to be infrared transparent (although two IR transparent prisms could be positioned opposite each other and both operated in a similar fashion). However, we find that the signal produced for a single interaction of the infrared beam with the sorbent/analyte mixture to be too low to image low concentrations of analyte within the column. Moreover, extremely thin sorbents, which yield lower IR signals than thick sorbents, are preferred to limit slow diffusion processes and achieve the most effective separation. Thus, the spectroscopic and chromatographic goals compete in the single bounce ATR embodiment.

[0042] We employ the following optical techniques to enhance the signal:

Multi-Bounce ATR

[0043] Multiple optical bounces can be used to increase the effective interaction length between the infrared light and the sorbent/analyte mixture, while still allowing for the thin sorbent phases preferred for chromatography. The system geometry can be configured so that the bounces occur along the column (along the direction of carrier gas flow) or across the column (perpendicular to the direction of carrier gas flow). When the bounces occur along the column, the signal enhancement comes at the cost of spatial resolution in the spectragram. When the bounces occur across the column, the channel may need to be widened to accommodate multiple bounces, which can lead to compromised separation through increased diffusion of the analytes. Ultimately, the choice of multi-bounce geometry depends on the application. Our preferred multibounce prism has a chamfer angle ? that depends on the prism material's index of refraction (n) according to the following transcendental equation:

[00002]$?=\frac{90?-\arcsin (\frac{\sin \left(90?-?\right))}{n}}{2}$

[0044] A sketch of such a prism for the case where n=4 is shown in FIG. 4, for the simplified case of 3 bounces for clarity. This preferred geometry keeps the light within the plane of the device, simplifying alignment and allowing for a more compact device. Furthermore, the large angle of incidence approaches the Brewster angle, allowing p-polarized light to enter (and exit) the prism with minimal losses to reflection. When a polarized infrared source such as a laser is available, this will mitigate or eliminate the need for an anti-reflective coating on the high refractive index prism. Note that all internal reflections are larger than the critical angle (14.5? for the n=4 case).

[0045] Another useful prism geometry is for the light to be incident normal to the surface of the prism to eliminate refraction at the first interface. The geometry of such a prism can then be tuned to increase the number of bounces per unit length (when the chamfer angle is small) without inducing internal reflections that exceed the critical angle.

Surface-Enhanced InfraRed Absorption Spectroscopy (SEIRAS)

[0046] Metallic nano-structures can be embedded within the sorbent to enhance the strength of the interaction between the infrared light and the sorbent/analyte mixture. While there are several ways to understand why this is the case, the end result is that the effective infrared cross section is greatly enhanced. For low resonant structures such as metallic spheres, the enhancement can be an order of magnitude or more. Additional enhancement is possible with highly resonant structures, but the enhanced signal comes at the cost of spectral bandwidth. The preferred nanostructures are designed to be weakly resonant to achieve maximum signal enhancement whilst still maintaining an acceptably flat spectral response over the spectral range of interest. Using the Maxwell-Garnet theory for effective media, an estimate of the enhancement [increase in the absorption coefficient k (or equivalently the IR cross section ?)] provided by SIERAS is shown in FIG. 5 for the case of gold nanostructures in PolyDiMethylSiloxane (PDMS), a common stationary phase sorbent. This effect considers isolated, nonresonant structures, and thus constitutes a lower limit for the enhancement factor provided by SEIRAS.

[0047] Now we consider the effect of these methods on the sensitivity of the system. We use the signal to noise ratio (SNR) of the absorption spectrum as the figure of merit for sensitivity. For shot-noise limited detection, which will essentially always be the case in laser spectroscopy, the SNR is given below:

[00003]$\mathrm{SNR}=\frac{{?}_{\mathrm{Absorbed}}?t}{\sqrt{{?}_{\mathrm{Transmitted}}?t}}=\sqrt{{?}_0?t}\left[1-\exp \left[-n_a?{?}_a{\mathrm{Bt}}_s\right]\right]\exp \left[\frac{n_a?{?}_a{\mathrm{Bt}}_s}{2}\right]\exp \left[\frac{n_s?{?}_s{\mathrm{Bt}}_s}{2}\right]$

Where:

[0048] ?*0:* incident infrared photon flux [0049] ? Absorbed:photon flux Absorbed by the Analyte [0050] ?.sub.Transmitted: photon flux passing through the device to the detector, i.e. not absorbed by the analyte or sorbent. [0051] ?t:measurement time [0052] n.sub.a:number density of analyte [0053] n.sub.s:number density of sorbent [0054] ?:SEIRAS enhancement factor [0055] B:Number of bounces [0056] t.sub.s:sorbent thickness [0057] ?.sub.a: analyte infrared cross-section

[0058] In the trace analyte limit (n.sub.a.fwdarw.0), where sensitivity is of primary concern, the SNR reduces to:

[00004]$\begin{array}{c}\mathrm{SNR}?\sqrt{{?}_0?t}\left[n_a?{?}_a{\mathrm{Bt}}_s\right]\left[1-\frac{n_a?{?}_a{\mathrm{Bt}}_s}{2}\right]\exp \left[-\frac{n_s?{?}_s{\mathrm{Bt}}_s}{2}\right]\\ \mathrm{SNR}\stackrel{\mathrm{Neglecting}\mathrm{doubly}\mathrm{small}\mathrm{terms}}{.fwdarw.}\sqrt{{?}_0?t}{n}_a?{?}_a{\mathrm{Bt}}_s\exp \left[-\frac{n_s?{?}_s{\mathrm{Bt}}_s}{2}\right]\end{array}$

[0059] We can maximize the SNR, and thus the sensitivity, when the product of the number of bounces and the SEIRAS factor (?B) is as shown:

[00005]$\mathrm{SNR}\mathrm{is}\mathrm{maximized}\mathrm{when}\frac{?\mathrm{SNR}}{??B}=0.fwdarw.?B=\frac{2}{n_s{?}_s{t}_s}=\frac{2}{?t_s}=\frac{1}{2?{\mathrm{kvt}}_s}$

[0060] To get a sense of reasonable values, we visualize the optimal ?B product for the common sorbent PDMS for 2 cases of the sorbent thickness, t.sub.s=160 nm and t.sub.s=20 nm, (See FIG. 6).

[0061] We note that even for fairly thick sorbents, the optimal ?B product is fairly large, on the order of 1000 or greater in regions of the spectrum that do not have infrared modes. To achieve such a large factor with either of these techniques alone would require an impractical number of bounces or highly resonant SEIRAS, which would likely compromise spectral bandwidth unacceptably. Thus, for best performance, it is preferable to use both methods together. It turns out that using both techniques together is straightforward: The thin prism required for multi-bounce ATR can be produced by cutting and polishing the prism chamfers on a standard infrared transparent wafer-preferably silicon or germanium. Such a wafer is the preferred substrate for depositing/lithographing/patterning nanostructures.

Sorbent-Kinetic Signal Enhancement

[0062] Within the column, the sorbent has the effect of concentrating the analytes from the gas phase. That is to say, the sorbent itself leads to a signal enhancement when probed optically. The concentration increase (relative to the gas phase) is equal to the analyte partition ratio in the sorbent at a particular temperature. The simplest way to make use of this effect is to choose a sorbent with a high partition ratio, and thus a high affinity, for the analytes of interest. This serves to concentrate the analyte within the sorbent relative to the gas phase.

[0063] Another useful approach is to introduce a third sorbent phase, the sensing phase which may be held at a lower temperature than the conventional stationary phase. The result of this (relative) cooling is to dramatically increase the concentration of the analyte in the sensing phase, where it will be measured, relative to the stationary phase. It would be ideal if this approach did not appreciably alter the analyte peak (or band) dynamics, so that the temperature of the stationary phase can be used to control the separation, and the temperature of the sensing phase can be used to provide an effective type of variable gain to concentrate the analytes to near saturation in the sensing sorbent. A schematic overview of this approach is presented in FIG. 7.

[0064] That the sensing sorbent layer does not appreciably alter peak dynamics holds provided the following two conditions are met: [0065] 1.) A similar fraction of total analyte molecules reside in the mobile phase to mitigate any change to the peak velocity. This situation can be realized given the partitioning sketched in FIG. 7: while most of the analyte molecules are dissolved in the stationary phase sorbent, as usual, some (small) fraction is concentrated in the thin sensing phase sorbent. Thus, we estimate the upper limit of practical sorbent gain to be the case when the number of analyte molecules in the stationary phase N.sub.stationary is roughly equal to the number of analyte molecules in the sensing phase N.sub.sensing. For sorbent films of thickness d, the maximum practical sorbent gain

[00006]$?=\frac{c_{\mathrm{sensing}}}{c_{\mathrm{stationary}}},$

where C denotes the concentration of analyte in either phase, is roughly:

[00007]$\frac{N_{\mathrm{sensing}}}{N_{\mathrm{stationary}}}>\frac{d_{\mathrm{sensing}}\exp \left[-\frac{E_b}{k_b{T}_{\mathrm{sensing}}}\right]}{d_{\mathrm{stationary}}\exp \left[-\frac{E_b}{k_b{T}_{\mathrm{stationary}}}\right]}.fwdarw.{?}_{\max }=\frac{\exp \left[-\frac{E_b}{k_b{T}_{\mathrm{sensing}}}\right]}{\exp \left[-\frac{E_b}{k_b{T}_{\mathrm{stationary}}}\right]}$ [0066] Where E.sub.b is the binding energy between the analyte molecule and the sorbent, T is the temperature and N denotes the number of adsorbed analytes in either sorbent. We note that ? can be quite dramatic, given the exponential nature of the Boltzman factors and the high binding energies that can be achieved with targeted sorbents. [0067] 2.) The time for analytes to diffuse through the stationary phase sorbent of thickness d should be the limiting (slowest) time constant ? of the system. This ensures that the introduction of the sensing phase does not delay the system from achieving thermodynamic equilibrium. Otherwise, analytes would stick in the sensing phase sorbent after the bulk of the analyte peak has eluted further down the column. Assuming the diffusion constant

[00008]$D?\frac{d^2}{?}$

is proportional to T, as predicted by the Stokes-Einstein equation,

[00009]$??\frac{d^2}{T}.$

Thus,

[0068] [00010]${?}_{\mathrm{sense}}?{?}_{\mathrm{stationary}}.fwdarw.\frac{T_{\mathrm{sense}}}{T_{\mathrm{stationary}}}?\frac{d_{\mathrm{sense}}^2}{d_{\mathrm{stationary}}^2}$ [0069] This relation places a limit on how much the sensing phase can be cooled relative to the stationary phase. More extreme relative cooling is possible when the sensing phase sorbent is thin.

[0070] Thus, both conditions imply that a thin sensing phase sorbent is advantageous for this approach. We note the high synergy between this technique, which requires a very thin sensing phase sorbent, and the optical signal enhancement techniques discussed previously which permit a strong infrared signal to be collected despite such a thin sorbent.

Data Analysis

Top Level Data Analysis

[0071] Unfortunately, the raw spectragram is quite difficult for a human to interpret. Thus, the critical element for analysis of chromatoscopy data is to split the spectragram back into its chromatographic and spectroscopic halves, revealing the distributions of each analyte at different points along the columnthe chromatography half- and the corresponding spectra of each analytethe spectroscopy half. This can be achieved through the various algorithmic methods of handling the Cocktail Party Problem, which is concerned with factoring matrices like so:

[00011]$\underset{\mathrm{Data}\mathrm{Matrix}''Spectragram''}{\underset{?}{D_{\mathrm{xv}}}}=\underset{\mathrm{Mixing}\mathrm{Matrix}''Chromatography''''''''Half''}{\underset{?}{M_{\mathrm{xp}}}}\underset{\mathrm{Spectral}\mathrm{Matrix}''Spectroscopy''''''''Half''}{\underset{?}{S_{\mathrm{pv}}}}$

[0072] Of the many solutions to the cocktail party problems, which include familiar methods such as principal component analysis (PCA) and independent component analysis (ICA), the one that makes the most appropriate assumptions for absorption spectroscopy is Non-negative matrix factorization (NMF) in that it reasonably assumes that all elements should be positive, and does not make any assumptions that would force the inferred spectra or chromatograms to be orthogonal, which they do not need to be. A demonstration of the Monte-Carlo version of NMF on noised data is shown in FIG. 8. The Monte-Carlo/Bayesian approaches are preferred because they quantify the uncertainty of the inferred parameters. It's worth noting that even though the SNR in the example shown in FIG. 8 is quite low, the effective SNR of the infrared spectra and chromatograms is much higher. This is a testament to the power of taking a large amount of data, and analyzing it with a statistical model that is consistent with the underlying physical reality.

[0073] One subtlety of the matrix factorization is that the number of analytes (the p [peak] index in the above equation), which is known as the order of the matrix factorization, is not known a priori. Instead, it must somehow be inferred from the spectragram. Failure to use the correct order tends to give nonphysical and absurd results. Inferring the correct order falls under a field of data science known as model comparison. In our tests so far, we've seen that simple heuristic forms of model comparison, such as choosing the order that results in the best Bayesian Information Criterion (BIC) works well. If need be, more exact approaches to model comparison such as bridge sampling are possible. The heuristic approaches are highly preferred because more exact approaches can be extremely computationally expensive. An example of model comparison with synthetic data is shown in FIG. 9.

Hierarchical Data Analysis

[0074] Once the data has been split into spectroscopic and chromatographic halves, it is natural to perform further analysis, what statisticians would call a hierarchical model, to extract information of interest from either field. Potential examples of such further analysis are discussed below:

Spectroscopic Analysis

[0075] The most natural use of the infrared spectra is to compare them to a standard library of analytes. This would be useful for identification. Quantification can also be performed in this way if the infrared library is quantitative, i.e. the infrared library has meaningful y-axis units such as imaginary index k, absorption coefficient ? or infrared cross section ?.sub.IR.

[0076] The spectra may also be decomposed into chemical functional groups. This is particularly useful in detection applications because it would permit the system to alert on novel chemical analyte threats that are not already in the library provided they contain functional groups that are known to be present in harmful chemicals.

Chromatographic Analysis

[0077] Further analysis of the chromatographic data (the mixing matrix) is necessary for automating the separation. Given a time series of spectragram data, it should be clear how one might infer the velocity of each peak. Of particular interest is the velocity of each peak as a function of temperature. The velocity of each peak v(T) is of primary interest to the automation of the separation process, and its use will be discussed further in the automation section. However, there is interesting information to be inferred that resides one level further down in the hierarchical analysis.

[00012]$v\left(T\right)=\underset{\text{"}\mathrm{Mobile}\mathrm{Phase}\mathrm{Velocity}}{\underset{?}{v_m}}\underset{\mathrm{Partition}\mathrm{Ratio}}{\underset{?}{K\left(T\right)}}=v_m\exp \left[\mathrm{Aa}\left(T\right)+\mathrm{Bb}\left(T\right)+\mathrm{Ee}\left(T\right)+\mathrm{Ss}\left(T\right)+c\left(T\right)\right])$

[0078] One can approximate the temperature dependent partition ratio using a parameterization of the sort shown above in a parameterized solvation equation, reminiscent of the Michael Abraham solvation parameterization. The lowercase letters (e,s,a,b,l,c) are the solvation parameters for the sorbent (solvent phase), while the uppercase parameters (A, B, E,S) characterize the analyte (solute). For our purposes, it is useful to force the solvation parameters of the analyte to be temperature independent by construction, as shown above. This is a reasonable simplification to make because each of the products in the sum characterize a distinct interaction that will have its own temperature/energy scale that should depend more on the fundamental physics of the interaction than the chemical details of the analyte. Since the sorbent is the same for all analyses with a given device, these values and their temperature dependence can be well characterized a priori. Moreover, the temperature independent analyte parameters can serve as a useful secondary fingerprint for each analyte that can be used to supplement the spectrum, the primary fingerprint, for identification process. Thus, this method could be used to distinguish isomers that have identical infrared spectra, but vary in how they interact with the sorbent. For example, this sort of secondary fingerprint could be used to distinguish between different alkanes that will have very similar infrared spectra.

Automation

Controlling the Separation

[0079] Consider a signal peak or band corresponding to an unseparated mixture within a column. Our task is to choose the temperature that will give this peak the best chance to separate into its components in the finite length of the column L and some finite tolerable measurement time t. It turns out that for the best chance of separating a peak in finite length, keeping the location of the peak or band as cold as possible, within the tolerance of elution velocity time, is advantageous. This is because the sorbent solvation parameters mentioned in Chromatographic Analysis above are larger at lower temperatures, leading to more marked differences in the degree of analyte interactions with the sorbent stationary phase. However, for the best chance of separating a peak in a limited finite time, one should heat the peak or band as much as possible. However, in the usual case where both column length L and measurement time t are important, if the temperature we choose is too low, none of the components will measurably or sensibly move forward in the column, and the components will not separate in an acceptable time. If the temperature is too high, the mixture components will move together in the carrier gas through the column such that they do not separate before they exit the finite length column. Thus, the proper temperature for our one unseparated peak is simply the one that makes it move at a velocity of approximately

[00013]$v_{\mathrm{peak}}?\frac{L}{t}.$

It turns out that this strategy is suitable for any resolved peak we observe within the column, because there is no way to tell a priori if the peak is separated or not.

[0080] Some modification is in order when there is more than one observable peak or band within the column (i.e. our order selection algorithm indicates that the number of analytes is greater than one). In this case, the target velocities of the peaks should be altered in such a way that they will be clearly resolved at the end of the experiment. For example, the slower peaks can be slowed so that each peak ends up clearly resolved, i.e. separated by several multiples of the peak width ?.sub.peak.

Temperature Control

[0081] In the traditional process of chromatography method development, the primary control variable is a single global temperature at any particular time for the entire column. However, given the above discussion, it would be desirable to control the velocity of each peak individually. To this end, we need a way to control the temperature as a function of position along the column. Thus, we propose the structure shown in FIG. 10 be placed on the prism surface:

[0082] This structure accomplishes the goals of signal enhancement (through the SERIAS regions) and temperature control through resistive heating. While only four heater traces/wires are shown for clarity, many more such traces can be used to ensure even heating over the structure. Nonuniformity in the temperature of the sorbent would degrade the quality of the separation. By monitoring both the current and voltage across each structure, the heater wires can be used not only as the heating element, but also as a resistive thermometer to close the temperature control loop. To simplify the fabrication process, it is desirable to etch/lithograph the metallic features from the same gold layer.

Chromatoscopy in Complex Microfluidic Analytical Systems.

[0083] To be compatible with chromatoscopy, all components of a microfluidic system should be able to withstand relatively high temperatures (?200? C.) and all materials in contact with the analytes should be chemically inert to avoid undesirable adsorption/reaction. Preferable materials are stainless steel, ceramic, or polyetheretherketone (PEEK). This material pallet provides a metal and an insulator that will enable electrical isolation/connection and an inert plastic, which can be used for dynamic seals. For static seals, stainless steel and ceramic can be braised and have matched thermal expansion coefficients, minimizing the strains induced when the assembly is heated. To route gas flow between components, the channels can be precision machined into stainless steel, and then sealed with a second layer welded (preferably via laser welding) to the first. In some cases, one may machine the channels through a stainless steel sheet, and then weld appropriate sealing layers to the top and bottom. This construction is similar in function to a PCB in electronics, in that it significantly reduces the size, fragility, and complexity of the final assembly (see FIG. 11). Static seals between this board and individual components can be achieved via welding or brazing as appropriate.

[0084] Micro-pumps and multi-port valves permit the construction of very flexible analysis systems. Presently, we describe a zero dead volume valve, a chromatography friendly micropump that is appropriate for chromatoscopy based microfluidic systems. Further discussion of the various components follows below. Schematics of each are shown in FIG. 11.

Multi-Port Valves

[0085] Eliminating dead volume is a common design goal in chromatography systems, since dead volume can cause tailing of the peaks. The usual valves used in chromatography are large, stand-alone devices that are not well suited for integration into chip-based designs. Meanwhile, while microfluidic valves are designed to minimize dead volume, eliminating it entirely is generally not required in these applications.

Micro-Pump

[0086] The traditional micropump design involves a layered structure, passive valves, and is driven by a piezoelectric actuator.

[0087] The effect of the micropump on the peaks is shown in FIG. 12. To minimize the perturbations, one should design the micropump to minimize the cycle time T (maximize the operating frequency f.sub.pump) and maximize the compression ratio

[00014]$\mathrm{CR}=\frac{?V}{V_0},$

where ?V is the change in volume of the pump chamber over a cycle, and V.sub.0 is the minimum volume of the pump chamber. The effect of the micropump is to discretize the (time domain) peak into chunks of size

[00015]$T=\frac{1}{f_{\mathrm{pump}}}.$

After each cycle, some fraction of the material will remain in the pump chamber and get transferred into the next time slice. Micropumps of the sort shown in FIG. 11 can be designed to have Compression ratios approaching 90% and operating frequencies greater than 100 Hz. For a typical chromatography peak with a characteristic width of around a second, the perturbation induced in each pass through the pump is negligible.

[0088] The above descriptions are those of the preferred embodiments of the invention. Various modifications and variations are possible in light of the above teachings without departing from the spirit and broader aspects of the invention. It is therefore to be understood that the claimed invention may be practiced otherwise than as specifically described. Any references to claim elements in the singular, for example, using the articles a, an, the, or said, is not to be construed as limiting the element to the singular.