Sensor Arrays

Introduction (skip this if you know the basics): From the chemistry perspective, the world we live in is a fairly complex affair. When we consider analyzing chemicals, we realize that most analytes comprise multiple chemicals of interest (multi-analytes or complex analytes) that need to be analyzed. Unfortunately, there are no chemical sensors that can respond to all analytes. This suggests, that multiple sensors are frequently required to interrogate complex analytes. This led to the idea of integrating various chemical sensors into one sensor device called sensor array.

In the past, the leading paradigm for the design of artificial receptors and sensors was the lock & key concept proposed originally Emil Fischer in 1894, was based on the idea that chemical sensors should be shape-complementary to the target analyte to achieve high selectivity and specificity.

This, in supramolecular and analytical chemistry is usually achieved by synthesizing a preorganized receptor (attached to a signaling device such as dye or fluorophore or a microelectrode, etc., to make a sensor), which has a shape and molecular properties of binding cavity highly complementary to the target analyte to achieve the perfect recognition and binding. This is pretty hard to do! And, within the context of multianalytes, one has to have several (perhaps many) of these hard-to-get sensors to address the composition of the multianalyte of interest.

Nature mastered this over millions of years and now we have various natural protein receptors, enzymes, antibodies, etc. These are expensive, require gentle conditions (no bases, acid, metal ions, varying temperature, etc.). And then, their shelf-life is relatively low. Naturally, we would prefer to have artificial receptors made of as few atoms as possible (to be inexpensive and stable) or polymers. But now we are in the realm of organic synthesis and materials chemistry!

Using few atoms (compared to thousands of atoms in a few kDalton of protein) makes artificial receptors and sensors (when you attach a signaling device to the receptor) cheaper and sturdier. Also, it is harder to achieve the perfect recognition and binding with such limited resources!

Therefore, driven largely by the interest in complex analytes, the focus in molecular sensing is shifting from selective sensors toward cross-reactive sensors, that are not selective (i.e., are cross-reactive). One such cross-reactive sensor is prone to responding to several analytes within the multianalyte. This is what is happening, for example in our nose or tongue, which has only five types of taste buds and can distinguish only among five distinct qualities of taste. But the key part of the process is that several of the taste buds send a combined signal to the brain, and the brain remembers that this combination of biases corresponds to a specific taste. This means that what is being recognized is the composite signal, which we call response pattern. In chemistry, we have something similar: the so-called chemical noses and tongues, etc. These are arrays of sensors that utilize cross-reactive recognition elements to identify an analyte based on the response pattern that originates from a number of non-selective sensors.

Sensor arrays & Optical Sensor Arrays: One of the important outcomes of the supramolecular analytical chemistry is the understanding of molecular aspects of molecule or ion - receptor recognition and binding and knowledge how to integrate receptors and signaling devices (molecular devices) to obtain an unambiguous signal. We call this modestly sensor design. The design of a sensor array starts with considering the multianalyte under study (liquid, gas), enumerating the components, and a carefully considering whether all the analytes are known to us of whether there might be an unexpected or unknown component (such as unknown toxin). In our group, we are analyzing mostly liquieds (although we have worked on analyses of gasses - air comprising gasses of explosives from deteriorating landmines, Chem. Eur. J. 2012, 18, 12712-12718; Org. Lett., 2008, 10, 3681-3684).

Another important decision relates to the nature of the signal that will be observed. This will inform our choice of signalling molecular device. If the observed signal is current, a conductive polymer or a short-range spacer comprising sulfur atoms that bind to gold electrodes as it is, for example, in some of our OTFT-based sensors may of advantage, Anal. Chem. 2016, 88, 1092-1095; Chem. Com. 2015, 51, 17666-17668. For fluorescence-based read-out signal, a bright fluorophore will be desired. We prefer to work with fluorescence as it is sensitive and simple to read (spectra or images) compared to, for example colorimetry.

The most important aspect for design of sensor array are the nature of the individual sensors, the receptor-fluorophore chemicals to be used. Fortunately, we can get a lof of that information from binding studies (NMR, fluorescence, UV-Vis, NIR spectroscopy and MS spectrometry). Then we have a good idea about the binding affinity and stoichiometry, magnitude of the response, etc. Do we need to do that? No. But it will inform our selection of the sensor chemicals. Still, we don't have to do that. But we will need to employ some literature precedents and perhaps also chemical intuition, and most importantly a larger number of individual sensors to build in some robustness and redundancy which will help to cover varying affinity, various stoichiometries and perhaps low magnitude of response (Chem. Soc. Rev. 2010, 39, 3954-3979). An array with a large number of sensors will quickly generate a large amounts of data we will have to deal with during the statistical analysis. After all, the statistical analyses, analysis of variance, and artificial intelligence embedded in various chemometric tools are quite likely to be able sort the important and junk data. One may even start with the brute force (many sensors) and use the chemometrics tools to weed out the sensors that do not provide important data and optimize the array for maximum analytical performance with lowest number of sensors (J. Am. Chem. Soc., 2013, 135, 7705-7712; J. Am. Chem. Soc. 2008, 130, 10307-10314).

However, a good knowledge of analyte and carefully conducted binding studies will allow us to use few of the semi-finalist sensors selected for large response and binding affinity to match the analyte profile. Even then, some redundancy may be desirable. Other aspects that need to be considered is the volume of the analyte, solvent (water or organic), approximate concentration, presence of solids (field drainage, river mud), etc. This is simply because if one uses organic solvents, the plastic array substrate might be compromised (multi-well plates), glass bottom unglued, etc. In such a case, glass or quartz multi-well plate may need to be used. On the other hand, "muddy" analytes may still be used, but because nobody want to filter the analyte, a bottom reading will be used (assuming that the dirt stays on top of the sensor).

However, a good knowledge of the analyte and carefully conducted binding studies will allow us to use few of the semi-finalist sensors selected for large response and binding affinity to match the analyte profile. Even then, some redundancy may be desirable. Other aspects that need to be considered is the volume of the analyte, solvent (water or organic), approximate concentration, presence of solids (field drainage, river mud), etc. This is simply because if one uses organic solvents, the plastic array substrate might be compromised (multi-well plates), glass bottom unglued, etc. In such a case, glass or quartz multi-well plate may need to be used. On the other hand, "muddy" analytes may still be used, but because nobody want to filter the analyte, a bottom reading will be used (assuming that the dirt stays on top of the sensor).

Examples of sensor arrays from our laboratory: As noted above, a sensor array could be a commercially available multi-well plate (see below), a bundle of optical fibers (Science 2000, 287,451-452) or many proprietary microchip-like formats (Illumina Microarray Technology comes to mind), or even a collection of data obtained from different sensors during different measurements at different times (by different people). This is not entirely desirable as it could lead to potentially deleterious artefacts and introduce inaccuracies.

We work with two types of arrays. The first is a glass-based microscope slide with ultrasonically drilled micro-wells (diameter: 950 um, depth 250 um, analyte volume 200 nL) (A). Similarly, a quartz plate holds 1536 micro-wells in a spatial arrangement that matches the distribution/arrangement of wells in a standard 1536-well plate. This means that this plate can be read by multi-well plate readers used in high-throughput screening (HTS) (A). A large plate with several fluorescence-based arrays is being inspected under UV-light by a student (B). Small microscope slide-based array with deposited sensor materials (C). Confocal image of a single well shows the individual slices stacked up to form a 3D image of the micro-well (D). The last slice shows the diameter of the well at the opening (D). This allows calculating the well volume: 200 nL.

What is important on the images (B), (C), and (D) is that the sensor compounds are actually dissolved in a hydrophilic polymer (Chem. Eur. J., 2013, 19, 8497-8506; J. Am. Chem. Soc. 2007, 129, 7538-7544). The sensor molecules are colored dust and would fall out from the wells upon handling. The sensor therefore doped into a hydrophilic polymer which the forms a 10 um thick film (D) that sticks to the glass, absorbs water with the analyte components, which then interact with the actual sensor molecules. This way, one can use muddy water as analyte, whole urine (Chem. Com. 2016, 52, 1827-1830; J. Am. Chem. Soc. 2013, 135, 7705-7712) (no filtration of the cells or proteins is necessary), or even whole blood serum (Angew. Chem. Int. Ed. 2007, 46, 7849-7852; Org. Biomol. Chem., 2016, 14, 7459-7462). Once again, the lipids, proteins, etc., are stuck to the top of the sensor-polymer film while the analytes (in our case phosphates such as ATP) diffuse through the polymer with the water. The change in fluorescence is then measured from the bottom using a UV scanner.

When the analyte with the sensor molecules embedded in the polymer film, the optical properties of the sensor molecules change. Such changes can be captured by scanner. It looks like this: The image of the array chip is captured by a scanner (A). The images of the actual responses (A), i.e., the change in color from the wells for the sensors 1-8 are cut out from the image (B), the colors of the image are inverted (to obtain black background) and the resulting color image is deconvoluted to individual color channels (Red/Green/Blue). That means that one color image is split into three images, each with different intensity of the lighter spots (non-zero pixels; the zero is black=the background). Then the non-zero pixels are integrated, i.e., converted into a number of pixels that are not black. The result is a group of numbers for each well for each of the RGB channels (C). These numbers from all analytes or concentrations for each repetition go to a spreadsheet for statistical program for evaluation. Note that the array (A) shows only one trial. These arrays must be run in many copies (8-12) so that one has the repetitions to be able to judge how robust is the analysis.

The result of all that data transformation (A) is a dataset (spreadsheet), which is then analyzed by chemometric methods. We use principal component analysis (PCA) (B) which is a non-supervised method that allows you to explore the data (variance, clustering, et.), or linear discriminant analysis (LDA), which is slightly different in the sense (because it is a supervised method) and allows for determination of classification accuracy. We also use hierarchical clustering analysis (HCA) (C) which allows for classification but most importantly provides into similarities of the properties observed. The large the distance on y-axis (Distance) the more dissimilar the behavior (closer=similar). For nice explanation see this site.

One thing you might have not noticed: This simple array of cross-reactive sensors comprised 8 sensors. And yet, it was able to identify 10 analytes! Somewhere along the way, we have beaten the selective "lock-and-key" sensors at this game. Because with the great selective and expensive sensors, you would need 10 of them to analyze 10 analytes. And that is not all! Below we show how the very same array also recognizes different concentrations of analytes. So, 8 sensors recognizes 10 analytes and also their concentrations! That is the power of the weak. Yes, the individual sensors are not selective (are cross-reactive) and are less difficult/expensive to synthesize. But they deliver an analysis superior analysis to the best sensors. This is the power of the weak!

The above showed a qualitative determination of analytes, i.e., helped to classify (identify) the analytes. But one can also perform a quantitative analyses using arrays. If you look really carefully at the wells, you see that the color changes from top down (as the analyte concentration increases) (A). For example, sensor 1 is orange without analyte and becomes read. Sensor 8 started as pink, but upon addition of the analyte turned green. These changes can be quantified (colors deconvoluted and non-zero pixels integrated) and graphs that are very similar to binding isotherms are obtained for each sensor and each color channel and each concentration (in many copies). Resulting (large dataset) is then analyzed by machine-learning analysis (that is the Artificial Intelligence) and your computer gives you the results in ca 30 sec. All this is very nicely explained in J. Am. Chem. Soc. 2007, 129, 7538-7544 and Chem. Soc. Rev. 2010, 39, 3954-3979.

The quantitative analysis, which showed isotherm-like dependence of color on the analyte concentration matches closely the isotherms obtained during the binding studies. This confirms that the array behaves reasonably, i.e. follows the supramolecular behavior of the sensors and confirms that our array design was correct and that we understand the chemical behavior correctly. It also means that it is unlikely that we measure some unknown artifacts.

But as good as the above graphs are, at the end of the day we have 8 sensors with three channels (RGB) and many analytes provide a large response datasets. As mentioned above, these can be easily analyzed by machine learning (and embedded Artificial Intelligence) and other methods of chemometrics provide us with graphical outputs we can easily understand.

In drug analysis, we also developed arrays of sensors that are not only colorful and change their color in the presence of analytes, but the sensors are at the same time also fluorescent. In the figure below, you see the colorimetric response (and there is an observable pattern of changes) (A). But there is the same array under UV light, and entirely different pattern of changes emerges! This array can determine non-steroidal drugs as well as a metabolite of drug Ritalin (ritalinic acid, major metabolite of the psychostimulant drugs methylphenidate, brand name Ritalin). But the important point is that we can double, quadruple the information obtained from a single array! This makes the array mote robust and more sensitive. After all, the fluorescence is more sensitive than colorimetric response: You can see the difference in the colors, but they are weaker at low analyte concentration. The response pattern is clearer under the UV light, isn't it?

Speaking of fluorescence, we will make a small detour. The colorimetric sensor arrays in glass microchips are slightly bit difficult to read (scanning, REG channel deconvoluting, etc.). A large number of arrays are performed in multi-well plates, which have standard dimensions, you can purchase a simple inexpensive plate reader to obtain the read-out data, etc. For array purposes, the 96 (working vol 100-300 uL, 384 (30-100 uL), and 1536-well (2-5 uL) plates are the most practical for high-throughput screening (HTS) (Figure below, A). Most plate readers are compatible with 96-1536 well formats. For colorimetric readings you need the clear-bottom plates (B), for fluorescence-based assays the plastic-only black ones (C). Then you just pipette in your solvent, sensor, and an analyte and use a plate reader to read the absorbance or fluorescence at wavelengths that correspond to your sensors, the plate reader computer writes the data and statistical software then analyzes the data for you.

The sensor arrays are not useful only for identification and determination of analytes such as anions (Chem. Eur. J. 2018, 24, 4879-4884; Chem. Sci. 2013, 135, 7705-7712), metals (Angew. Chem. Int. Ed. 2012, 51, 2345-2348; J. Am. Chem. Soc., 2008, 130, 10307-10314; Anal. Chem. 2008, 80, 7451-7459), drugs (J. Am. Chem. Soc. 2017, 139, 14954-14960; J. Am. Chem. Soc. 2013, 135, 15238-15243), toxins (J. Am. Chem. Soc. 2012, 134, 20021-20024), etc. The arrays could be set up also to elucidate and predict, for example, yields and enantiomeric excess in organic chemistry transformations.

The arrays technique was, among others, used to measure enantiomeric aexcess in various chemicals including amines, amino alcohols, or diols (Nature Protocols 2020, 15, 2203-2229.). The following figure shows the construction of self-assembled sensor from a chiral diol (BINOL), formylphenyl boronic acid (FPBA) (A) and a chiral amine under study (B). Because the enantiomerically pure BINOL+FPBA for a chiral pre-complex which is chiral. Incorporation of a chiral amine leads to a mixture of diastereomers. And because each of the diastereomers (C) displays different fluorescence intensity (D), one can easily determine the enantiomeric excess (ee) in the diamine (Chem. Eur. J. 2016, 22, 10074-10080).

The same analysis of ee can be performed to determine enantiomeric excess in chiral drugs such as Atorvastatin (brand name Lipitor). Once again, each of the diastereomers display a different fluorescence (A,B), but only R,R-enantiomer is the drug (C). Chemometric analysis of the spectral data using a pattern recognition method called linear discriminant analysis (LDA) shows a clear definition and separation of the data clusters (20 repetitions) representing various enantiomeric purity from pure S,S- through the racemic mixture to the pure R,R-enantiomer (D). Inset (E) shows results of the linear regression and simultaneous analysis of two samples (red dots) of unknown ee. The error of prediction was 1.9% (plus minus 0.95%). A calibration curve for the same analysis shows simultaneous determination of unknown ee values in four different samples (the black x in the graph F). This analysis shows, how arrays can be used to evaluate ee, for example for quality control (QC) of pharmaceuticals (Chem. Eur. J. 2017, 23, 10222-10229).

Sensing arrays can be also used in optimization of reactions in a high-throughput screening (HTS) setting. The last example shows a simultaneous measurement and prediction of both product yield and enantiomeric mixture of an asymmetric reaction. For this purpose, we used the Noyori asymmetric transfer hydrogenation of benzoin (Organic Letters 1999, 1, 1119-1121.) following the Scheme below (A). Because each of the stereoisomers formed in the reaction displays a different fluorescence changes with the sensor (B), one can analyze the output data using linear discriminant analysis (LDA) (C) and determine the concentrations of individual stereoisomers.

Most interestingly, however, one can simultaneously determine the yield of the reaction as well as enantiomeric excess. In the figure below, upper left panel (A) shows the results of the determination of ee at a certain (constant) concentration. But the yield (i.e., the concentration) of the dihydrobenzoin affects the fluorescence signal and the higher the concentration of dihydrobezoins, the higher the slope of the traces in (Figure B). Naturally, because the sensor measures the concentration. At the same time, we can see that the intensity of the signal also depends on the ee. Thus, we have two independent variables (yield, ee) and fluorescence output as a dependent variable. Fortunately, when we provide these data as a training dataset to an artificial neural network (ANN) to make calibration hyperplane (not a calibration curve anymore). When we then provide the unknown sample, the artificial intelligence is able to place the data in the correct position on the hyperplane and read the answer. This is called prediction. So, what we do then is to run the reaction for different time, with different catalyst (R,R- or S,S- or a racemic catalyst R,R- : S,S- 1:1). The reading before the reaction and with the racemic catalyst are control experiments (to see if the ANN will be confused). It was not. In the Table C, we see that from each reaction we determined the ee by NMR: The data are in the pink column. Note that the yield cannot be determined so easily because NMR needs a substantial concentrations to be able to measure the ee. Then, we determined the yield and the ee fluorescence assay to figure out the yield and ee. These are the data in blue column. They match the NMR data. Then we took the fluorescence data and gave them as "unknown" samples to the ANN to see what she thinks. This is the pink column in Table C. One can clearly see that the predictions are very accurate. There is one important to point to make: The reaction samples were analyzed both as crude (including the catalyst, traces of solvents, formic acid, triethylamine) as well as recrystallized. As you can see, the assay does not care about impurities and faithfully measures only the dihydrobenzoins. Note, that with exception of the NMR, everything is done in a high-throughput screening (HTS) fashion, and to write about it takes much more time to accomplish the assay! You can read more in Chem. Eur. J. 2017, 23, 10222-10229.

As you can see, sensor arrays are very interesting and also an important topic for many different endeavors including high-throughput screening (HTS) in analytical chemistry, assays and bioassays, reaction methodology and development (to quickly screen successful substrates, conditions, catalysts) and quality control (QC). The proof that this is interesting is that you have read all the way to the end. Please, feel free to contact us if you are curious or you want to learn more by working with us.

Bioassays

In the past, we have developed an assay for inhibitors for carbonic anhydrases (CA). Human carbonic anhydrases (hCA) are a family of enzymes (15 isoenzymes in mammalian cells localized in various tissues and cell compartments) that catalyze the conversion of CO₂ and water to HCO₃^- and back thereby contributing to regulation of pH and CO₂ levels. Isoenzymes hCA IX and hCA XII are maintaining acidic pH in solid tumors thereby contributing to unfavorable prognosis. A selective hCA IX and hCA XII inhibitors would be a boon to treatment of many cancers, and a significant effort toward the synthesis of such small-molecule inhibitors has been expended. Numerous potential inhibitors must be tested for all CA isoenzymes because we need drugs that inhibit the cancer associated CAs (hCA IX and hCA XII), but not the other CAs. This is an immense challenge because all hCAs have very similar structure. While we do not develop new drugs in our laboratory, we have developed a fluorescence-based assay based on a competitive mechanism whereas our probe is bound by the enzyme (fluorescence is quenched) and upon addition of competitive inhibitor the fluorophore (probe) is released, and its fluorescence is regenerated. The figure shows the structure of hCA (A), the structure of the probe (B) as well as one of the inhibitors and the docking of the probe in the binding pocket of the enzyme (C). We can clearly see how the aromatic surface of the probe preferentially interacts with the hydrophobic amino acids (shown by the red color).

While the probe is bound within the enzyme, its fluorescence is quenched (D). However, upon addition of the inhibitor, the probe is released and becomes brightly fluorescent, which is visible by naked eye (E). A titration by the inhibitor allows for calculation of the binding constant (K_assoc) (F). Numerous potential inhibitors can be tested and compared (G) to identify promising leads. Leads that inhibit the cancer-associated hCAs but not the other isoenzymes.

Medicinal chemists must generate large numbers of potential inhibitors that have to be tested using all pertinent isoenzymes. This requires a rapid and reliable assay capable of addressing K_assoc in the range of 105 - 1010 M^-1. Reference: Chem 2017, 2, 271-282.

Recently, we focused on determination of conversion of ATP into ADP+Pi or into AMP+PPi. This is because numerous biological processes perform these reactions and we feel that our receptors and sensor can address these needs. Recently, during the Covid-19, a need for running millions PCR was realized. But the traditional DNA stains such as SYBR Green I are not always reliable due to variable fluorescence to different dsDNA sequences, sensitivity to additives, and reacts to loops/palindromes in ssDNA. Hybridization probes utilizing dual labeling such as TaqMan (gold standard in Covid-19 diagnosis) are sensitive to storage conditions, and expensive to the point that it cannot be available for large populations in economically developing countries. We have embarked on research to develop fluorescence-based sensors that selectively and reversibly bind PPi (product of PCR) in the presence of large concentration of dNRPs. Furthermore, the sensor cannot interact with the primers, DNA polymerase regardless of the origin (TaqDNA, PfuDNA, etc.) and must utilize the same wavelengths of dyes to be compatible with all the current PCR instruments. The idea of Zn(II) based receptors combined with fluorophores is not entirely new (e.g., Chem. Commun. 2016, 52, 8463-8466), but the problem is that most of the sensors interfere with the polymerase function! We are currently testing the first sensors, and the comparison with SYBR Green is favorable.

Polymer Nanochemistry

How could one practice chemistry and carry out any chemical reactions in a way that it is absolutely safe? Yes, this is a rhetorical question, but the problem is not an entirely artificial one. We surmised that the solution to the problem is to carry out reaction at such a small scale, that the amount of the products cannot harm anybody. Hypothetically, when we react one molecule of A with one molecule of B to yield one molecule of product C, can the single C molecule harm us? The answer is a resounding NO! C would have to be a super-efficient catalyst! Sadly, we do not know ANY catalyst that effective. It appears that if we carry out reactions at a scale of few hundred molecules, even the very dangerous products could be safe. In a way, this is similar to natural radioactivity background: Radioactive species are everywhere around us, but at the normal levels are not harmful. Even the most toxic compound on earth, botulinum toxin, needs roughly 8x10¹¹ molecules to kill a 100kg professor!

Well, then. We decided to develop a method that would allow running reactions on a scale of 500-1000 molecules. This could be easily done using so-called high-dilution method when extremely small amounts of reagents are reacted in large volume (i.e., at very low concentration). The problem here is that reaction times in high-dilution methods are extremely long (proportionally to the dilution). Second approach is to use very small reaction vessels. Such super-small reaction vessels are difficult to fabricate, and they are very expensive. This problem has been partly addressed by nano/microfluidic reactors, in which tiny droplets of immiscible liquids were carried through the channels (see the figure below, Left), mixed, and their solutes reacted (Nature 2006, 442, 368-373. doi:10.1038/nature05058). Next figure actually shows two water droplets separated in oil-like liquid can coalesce in a nano/microfabricated channel (Left), droplets comprising each two micro-droplets of different dyes, and finally a microfluidic super-reactor chip. However, in the microfluidic setups, the flow and droplets must be synchronized to meet, and the reactors with products collected in a receiver reservoir, where the amounts of products accumulates to exceed the safe amount of products. Also, there is the issue of nano/microfluidic chip fabrication, pumps to bring in the liquids, etc. All in all, it may be possible, but faaaar from easy-to-do. Thus, we felt that the nano/microfluidic approach does not provide the solution to the problem we wanted to explore. After all, we are organic chemists, and at the end of the day we want reactions and products!

Our approach, on the other hand, is simple. The following figure shows the principle of the nanofiber reactors. The nanofibers are doped with reagents, but are not hollow (i.e., the reagents do not flow through the fiber). The red object at the nanofiber junction is the idealized representation of the attoliter reactor (a). The SEM images show the fibers overlaid (b) before they are softened. After the softening (c and d) the fibers fuse and flatten a little. This is when the reagent molecules start to migrate through the atto-space of the junction (atto-reactor).

When the nanofibers are softened, the reagent molecules diffuse and percolate through the interstitial space (between the polymer chains), meet and react. Because seeing is believing, we show a reaction between two non-fluorescent starting materials which form a fluorescent product in the attoreactor. The figure bellow shows three microscope images: Brightfield image (A) where you cannot see the fluorescence; Darkfield image with only UV-light excitation, which shows only the fluorescence from the reactor (turquoise-colored dot in the center)(D), and the combined Brightfield + UV excitation, which shows that the fluorescence indeed comes from the attoreactor and nowhere else (see the figure below).

Higher density fiber mats can be fabricated if a larger number of reactors is required (Right) and products harvested for traditional analysis (e.g., MS, NMR). Furthermore, we can also print larger (tens-of-micrometers) fiber-like “lines”, that can be probed directly by MALDI-TOF HR-MS from the single reactor. Again, brightfield image (A), UV excitation (D), mixed excitation (C), and black-white image with contrast (C). In this case, the reaction is a Diels Alder addition of acetylene to aryl-substituted cyclopentadienone to form substituted terphenylene derivative (see the figure below).

Needless to say, this method is general and is not limited to fluorescent products (their formation is just so nicely observed by fluorescence confocal microscope.

We are now actively pursuing this avenue of research to explore what kind of reactions can be carried out this way and study the activation and kinetic parameters of these new reactions.

For more information, see Nat. Chem. 2009, 1, 80-86. Angew. Chem. Int. Ed. 2012, 51, 2345-2348. This research has also been highlighted in The New York Times and Nature.

Photonics and Electronics Materials

Due to its relatively low energy consumption and small environmental footprint, Organic Light-Emitting Diodes (OLEDs) are not only useful for fabrication of displays (TVs, cell-phone displays) hold a promise, for example, for solid-state lighting (SSL). The design of highly-efficient OLEDs requires matching of frontier orbital (HOMO-LUMO) energy of the different charge-transporting layers, high-charge mobility for the charge-transporting layers and a high quantum yield for the emissive material. The most efficient OLEDs are based on phosphorescent dyes. Phosphorescent emitters, however, also require the use of a matrix host in which the phosphorescent dye is dispersed. The hosts must possess excellent charge mobilities properties, preferably bipolar-behaviour, and triplet energy higher than the emissive compound. In the case of blue-emitting phosphorescent compounds, these requirements are even harder to obtain since the high-triplet energy needed can be achieved with compounds with short-conjugation.

An important part of our research is in synthesis and investigation of materials than can transport excitons (generated by electron-hole recombination) in electroluminescent devices. Exciton migration is a process of fundamental importance for a number of processes of high importance, including organic electroluminescence (EL), organic photovoltaics (OPV), photoconductors, and others. Example is the synthesis of Al(III) quinolinolates that act both as emitter and semiconductor hosts for other electroluminescent compounds.

Our work on photonic and electronic materials is yet another showcase of how organic synthesis and materials chemistry can address the large societal problems. An excellent example is shown below. In fact, researchers who mater organic chemistry are capable of solving scientific problems beyond their immediate research focus. Below we show a synthesis of several new Al(III) complexes. The goal of this research wa optimize the substituents on the quinolinolate (8-hydroxyquinoline) ligand to achieve a blue emission (fluorescence) for application in white-light emitting OLEDs. Note that the parent Al(III)quinolinolate (R₁=R₂=H) emits a green fluorescence (max 520 nm). Because the quinolinolate displays charge-transfer excited state where the electron-rich phenolate moiety provides a charge to the electron-poor moiety, we decided to introduce electron-withdrawing substituents (F, CF₃, CN) on the phenolate to destabilize the HOMO and electron-donating methyl on the pyridine to destabilize the LUMO. The combination of two electronic effects that destabilize the charge transfer and make it more energy-demanding which results in a blue-shifted emission. This worked great - see the results below!

The Al (III) complexes were synthesized from the corresponding ligand by reaction with tris-isopropoxy aluminum(III). The synthesized semiconductor-emitters were then used for white-light emitting OLEDs (like the ones you would need for solid-state lighting (SSL). The electroluminescence spectra of the Al(III) compounds (A) and the photographs of the corresponding OLEDs are shown on the left (B). Co-deposition of the blue-emitting AL(III) complex with the red-emitting Ir(III) allows for singlet excitons to be emitted from the Al(III) complex while the diffusive triplet exciton migration results in a red emission (C). Combination of blue and red emission which generates a warm white light (D). For more information see Chem. Eur. J. 2011, 17, 9076-9082.

For other work on OLEDs and organic semiconductors, see ACS Appl. Electron. Mater. 2021, 3, 3365-3371; J. Mater. Chem. C. 2020, 8, 11988-11996; Advanced Optical Materials 2020, 8, 0191282.

Our work on organic semiconductors also find an application in OFETs. Our OTFT materials are used in the devices fabricated and tested by our collaborators in Japan. For example: Anal. Chem. 2016, 88, 1092-1095; Chem. Com. 2015, 51, 17666-17668; and Chem. Eur. J. 2014, 20, 11835-11846.