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Conjugated Polymer Nanoparticles Based Fluorescent Electronic Nose for the Identification of Volatile Compounds. Peng Zhao, Yusen Wu, Chuying Feng, Lili Wang, Yun Ding, and Aiguo Hu Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b00273 • Publication Date (Web): 10 Mar 2018 Downloaded from http://pubs.acs.org on March 11, 2018

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Analytical Chemistry

Conjugated Polymer Nanoparticles Based Fluorescent Electronic Nose for the Identification of Volatile Compounds. Peng Zhao, Yusen Wu, Chuying Feng, Lili Wang, Yun Ding*, Aiguo Hu* Shanghai Key Laboratory of Advanced Polymeric Materials, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.

Corresponding Author：Aiguo Hu * Tel: +86-021-64253037; E-mail: [email protected]

ABSTRACT: A fluorescence sensing array (or fluorescent electronic nose) is designed on disposable paper card using 36 sets of soluble conjugated polymeric nanoparticles (SCPNs) as sensors to easily identify wide ranges of volatile analytes, including explosives and toxic industrial chemicals (amines and pungent acids). A 108-dimensional vector obtained from the fluorescent color change in the sensing array is defined and directly treated as an index in standard chemical library (30 kinds of volatile analytes and a control group). Hierarchical clustering analysis (HCA) and principal component analysis (PCA) indicated the diversity in electronic structures, saturated vapor pressure and miscibility of analytes are keys in differentiating the analytes, with electron-rich arenes and alkylamines enhancing fluorescence while electron-deficient analytes attenuating fluorescence. Support vector machine (SVM) works well to predict an unknown sample, reaching 99.5 % accuracy. The excellent fluorescence

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stability (no fluorescence quenching after exposed in air for one month) and high sensitivity (emission color changes within minutes when exposed to analytes) suggest that the fluorescent polymer-based electronic nose will play an important role in field detection and identification of a wide spreading of hazardous substances. KEYWORDS: Fluorescent array, Electronic Nose, Nanoparticles, Vapor sensors, Hierarchical clustering analysis. There is a pressing need for rapid, sensitive and highly portable identification techniques for explosive vapors and toxic industrial gases, not only for state security, but also for the use in the fields of environmental protection and industrial chemical workplace.1-5 For example, 2,4,6-Trinitrotoluene (TNT) and its byproduct dinitrotoluene (DNT), the principal constituents of military and civil explosives, have been the first choice of terrorists.6 The failure to timely detect the hidden explosives causes disastrous casualties and sufferings, urging a need for on-site explosive chemosensory devices that not only complement the conventional methods (ion mobility spectrometry7, GC-MS8,9 etc.), but also provide the advantages of low cost and instrumental mobility.1,6,10 In addition, the threats to public health from toxic industrial chemicals are supposed to be catastrophic. These active chemicals tend to damage the functional enzymes (for example, formic acid inhibits mitochondrial cytochrome oxidase I) or cause cell lysis (for example, hydrogen fluoride dissolve cytomembrane and create pulmonary oedema).3,11 Timely detection and identification of these hazardous substances is therefore of great importance. Imitating the olfactory system of mammals, where the odor specifity originates from the fact that unique response pattern would be generated from several hundreds of olfactory receptors12, the arraybased electronic noses have emerged as a sort of potentially powerful kit towards the specific identification of wide ranges of analytes.13-15 Cross-responsive sensors in the array produce composite response pattern unique to each odorant (analyte) with no need of sensor specific to any single odorant.


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Changes in the properties like acoustic wave and resistance of some kinds of sensors in an array are often used to specifically identity analytes in air.16-18 Optoelectronic noses based on colorimetric arrays have also been thoroughly covered by Suslick’s group using HCA of RGB (red, green, and blue color) difference vectors between the “before” and “after” images to precisely classify and identify a wide range of volatile compounds, showing the powerful discernment ability of array technique.3,5,19-22 Compared with small molecular fluorescent or colorimetric sensors, fluorescent polymers are particularly attractive in the chemical sensing field due to their inherent fluorescence amplifying effect (FAE) and easy fabrication and integration into devices through a spin-coating process. Fluorescent polymers serve as molecular wires which are capable of transporting photo-induced electrons and providing extended electronic communication and transport. If the analyte binding produces a resistive element in the wire, then all the electrons flowing through the molecular wire will experience an impediment and caused more fluorescence quenching than that of small molecule fluorescence dyes. In addition, there are more chances that the transport of many conducting electrons among the polymeric polyreceptor system can be reduced by a single binding event than that afforded by a similar interaction in an analogous small monoreceptor system.23 Thus fluorescent conjugated polymers typically achieve higher sensing sensitivity in comparison with small molecule system.24,25 Fluorescence polymer-based arrays have been widely used in the form to match liquid phase system (“electronic tongue”).4,26-30 For vapor sensing techniques, however, the story is entirely different. Most attentions have been focused on recognizing some specific analytes using standalone linear fluorescence polymers (casted in films)1,31-35 or fabricating fluorescence arrays by embedding small molecular fluorescent dyes that have no FAE effect in transparent polymers.36-39 To generate highly efficient sensing arrays with fluorescent polymers fully taking advantage of their FAE effect, however, still constitutes great challenge.40,41 In other words, preparation of tens to hundreds kinds of fluorescence polymer sensors free of aggregation-caused


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quenching (ACQ, a main problem for fluorescence sensors) problem is the key to resolve this issue. An effective strategy to tackle the ACQ dilemma of fluorescent polymers is to build branched and rigid substructures, where the sterically crowded branch segments help weaken the ACQ effects in a way of suppressing the intermolecular π-π interactions in solid state. We have been dedicated to developing soluble conjugated polymeric nanoparticles (SCPNs) in confined nanoreactors.42,43 A large number of SCPNs were facilely prepared with combinatorial chemistry method, and tunable fluorescence emissions were achieved by simply varying the monomers and terminals.44 Herein, we report an array consisting of 36 sets of SCPNs and the vector of every analyte in the full 108-dimensional space was used for the identification of volatile compounds, including nitroaromatics, aromatic aldehydes, arenes, alkyl alcohols, amines and acids (30 kinds of analytes). PCA and HCA indicated that the diversities of electronic structures, miscibility and saturated vapor pressure of analytes were keys in differentiating these analytes. Excellent fluorescence stability in air (30 days) and high sensitivity (within minutes) convince us that the SCPN array-based recognition machine will show general applications in industrial plants and environment conservation fields.

Figure 1. The chemical structures of monomers and terminal groups used in the confined polycondensations for the preparation of the SCPN sensors.


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RESULTS AND DISCUSSION 36 sorts of SCPNs with similar particle size (~4 nm, Table S1) are facilely obtained through Suzuki or direct (thiophene) arylation polycondensations plus a postmodification with terminals (A(x)+B(y)+T(z), Fig. 1) in confined nanoreactors, according to our recently published work.42,44,45 The synthesis details and fundamental characterization of the SCPN sensors are provided in Supporting Information (Fig. S1~S3). These SCPNs are incorporated with different skeletons (phenyl, spirobifluorene, triphenylamine, 1,1,2,2-tetraphenylethene, pyrene and 3,4-disubstituted thiophene) and terminals (phenylboronic acid, anthracene, naphthalene, methyl benzoate and benzophenone) to tune fluorescence emission, LUMO energy level and even end group polarity of SCPNs, essential for cross-responsive sensing in array-based technique. The absolute molecular weight of a representative SCPN, prepared from Suzuki polycondensation of 1,3,5-triiodobenzene (A(2)) and 1,4-phenylenebisboronic acid (B(1)), is measured as 4428 g mol-1 by using a Multidetection Gel Permeation Chromatography apparatus with online multi-angle laser light scattering (Dawn Heleos) and refractive index detectors (OPTILAB T-rEX, Wyatt), close to that of a dendritic poly(bisphenylene) of generation 3.5-4.0 (Fig. S4). All the SCPNs exhibit strong fluorescence emission from sky blue to orange red in solid state (Fig. S5). When casted in films, negligible red shift of the absorption/emission peaks and slight change of the fluorescence quantum efficiency in comparison with that in solution were observed (Fig. S6 and Table S2), indicating that the weak π-π interactions between SCPNs and demonstrating that the strategy of hyperbranching and rigidifying SCPNs worked well to avoid the ACQ effects.31 The 36 sorts of SCPNs are therefore readily to be adopted as array sensors. The interactions between the structural-varied SCPNs (sensors) and analytes include π-π interaction,46 hydrogen-bonding,47 π-dipolar interactions,30 and even van der Waals interaction. Such a diversity of interactions is essential to the development of array technology.20


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Filter-paper printed with black background and 6*6 unprinted circles by a laser printer was selected as the substrate (Fig. S7), considering its wide availability and excellent absorption capability to all the SCPN sensors. Meanwhile, the numerous pores in the filter paper substrate may also serve as in-situ preconcentrators for analytes. A capillary dipping method of SCPN solutions guarantees the uniform fluorescence spot (sensors) distribution in the array (Fig. 2) and all SCPNs could be well dispersed on the substrates (Fig. S8). After dried in vacuo for 24 h, the filter-paper-based fluorescence array of the isolated 36 sensors was ready for vapor sensing. The chemical structure of all the SCPN sensors and their position information in the array are shown in Table 1 and Fig. S7.

Figure 2. Fluorescence image of array-based electronic nose irradiated at 365 nm and representative structure of an integrated SCPN sensor. The fluorescence array was excited at 365 nm and then pictured with a digital camera before and after the exposure to analytes. To produce color-difference patterns, subtraction of the RGB values of the “after images” with those of the “before images” from seven parallel experiments were conducted and the average subtraction values were used to regenerate the molecular fingerprint patterns, which are supposed to effectively identify the analyte and tell us the odor.3 Thirty kinds of representative analytes were chosen to evaluate the performance of the electronic nose, including nitroaromatics and toxic industrial chemicals (amines, arenes and pungent acids). As shown in Figure 3, the colorful patterns permit visual detection and identification of the 30 representative compounds even with naked eyes. In


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addition, the unique difference pattern of each analyte could be treated as a flag of an unknown odor. For instance, the difference pattern for a new analyte (amylamine, Fig. S9) is similar to that of alkylamines, thus it’s reasonable to consider the analyte as a member of alkylamine family. In order to analyze the difference patterns digitally and to set up a standard chemical library, a 108dimensional difference vector (36 sorts of sensors times color changes in RGB values respectively, 36 × 3) was defined as the subtraction of RGB values of the “before” fluorescence images from the “after” ones, with the values in every dimension ranging from -255 to +255. Seven parallel experiments of 30 sets of analytes and control group were run (Table S6) and the formed vectors were directly used for the subsequent analysis. Such a high dimensional data is expected to provide enough information to differentiate wide ranges of analytes; however, it is difficult to analyze it directly. To this end, multivariable analysis methods, such as principal component analysis (PCA) and hierarchical clustering analysis (HCA), are employed to classify the analytes.

Table 1. The structure information of the SCPN sensors in different positions of the array. Note that “SCPN(xyz)” means the SCPN is prepared from the polycondensation between monomer A(x) and B(y) in confined nanoreactors44 and post-modified with terminal group T(z) (which is not afford for sensors in A-D columns). Code/SCPN(xyz) F

E

D

C

B

A

1

A(1) +B(2) + T(3) A(1) +B(2) + T(1) A(1) +B(4) A(1) +B(3) A(1) +B(2) A(1) +B(1)

2

A(3) +B(2) + T(3) A(3) +B(2) + T(1) A(2) +B(4) A(2) +B(3) A(2) +B(2) A(2) +B(1)

3

A(5) +B(2) + T(3) A(5) +B(2) + T(1) A(3) +B(4) A(3) +B(3) A(3) +B(2) A(3) +B(1)

4

A(1) +B(2) + T(4) A(1) +B(2) + T(2) A(4) +B(4) A(4) +B(3) A(4) +B(2) A(4) +B(1)

5

A(3) +B(2) + T(4) A(3) +B(2) + T(2) A(5) +B(4) A(5) +B(3) A(5) +B(2) A(5) +B(1)

6

A(5) +B(2) + T(4) A(5) +B(2) + T(2) A(6) +B(4) A(6) +B(3) A(6) +B(2) A(6) +B(1)


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Figure 3. Color difference patterns of the SCPN-based electronic noses towards 30 representative volatile compounds. NT, NB, DNT, TNT, Me-Ph-CHO, Et-Ph-CHO and Pro-Ph-CHO represent 2nitrotoluene,

nitrobenzene,

2,4-dinitrotoluene,

2,4,6-trinitrotoluene,

4-methylbenzaldehyde,

4-

ethylbenzaldehyde, and 4-propylbenzaldehyde, respectively. PCA was initially conducted as a general method to dramatically reduce the spatial dimensionality.48,49 PCA of the 30 volatile analytes shows that 90% of the total variance involves 7 dimensions, and with 10 dimensions, it explains 95 % variance (Fig. 4a), much less than the initial number of 108 dimensions. Moreover, around 67 % total variance of the array was captured when the dimensionality decreased to two. As shown in Figure 4b, all the analytes are segregated in seven rather narrow regions according to their electronic structures, which caused diversified distinctions of HOMO and LUMO energy levels between SCPNs and analytes.44,50,51 More PCA details, please see Fig. S10.


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For example, nitroaromatics, aromatic aldehydes and aliphatic acids with electron-deficient substituents tend to quench the fluorescence of SCPNs and locate on the left bottom in the PCA biplot, while electron-rich benzene derivatives and alkylamines benefit enhancing fluorescence so locate on the top right. The strong inorganic acids like HNO3 and triflic acid, which have a strong electron-deficient structures, are also able to quench the fluorescence of SCPN sensors (on the top left). The alkyl alcohols show relatively weak fluorescence response (Fig. 3) towards most of the sensors, probably due to the low miscibility of these analytes with the SCPNs.52 As a result, they are close to the control group (in which the array was simply exposed in fresh air) and the (0, 0) point in the biplot. Unfortunately, overlap between aliphatic acid and nitroaromatics is observed in the biplot generated with PCA. It is not surprising due to the loss of some differentiating information during the dimensionality reduction process. To classify these analytes better, HCA approach is utilized.

Figure 4. a) PCA of 30 volatile analytes shows that our array has a reduced spatial dimensionality, where only 7 dimensions are required to define 90% of the total variance. b) The PCA biplot, obtaining a total variance of 52.4 % in the first linear discriminant and 15.1 % variance on the second one. HCA is a general statistical classification method based on appropriate measure of the distances between pairs of observations under a linkage principle. It specifies the dissimilarity of different sets


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using some pairwise distances as a function to build a hierarchy of clusters. In our work, the squared Euclidean distance between different vectors was employed in order to generate a response dendrogram using minimum variance method (Fig. 5). Delightfully, all the 30 volatile analytes and a control group in 217 trials (31 × 7) were successfully classified and obviously distinguished against one another with no misclassifications, possibly attributed to the high dimensionality (108) of the array. Alkyl alcohols, aliphatic acids, aromatic aldehydes, nitroaromatics, inorganic strong acids, arenes and alkylamines form distinct branches in the hierarchical tree. It is evident that electron-rich organic analytes (alkylamine and arenes) cluster together, while analytes with electron deficient substituents (nitroaromatics, aromatic aldehydes, and aliphatic acids) form separate groups. Alkyl alcohols, causing little fluorescence change, cluster together with control group, and inorganic strong acids, which tend to robustly quench the fluorescence of SCPN sensors form another cluster. This trend is similar to the results obtained from PCA analysis. In both cases (PCA and HCA), the separations of nitroaromatics relative to the control group are narrower than others, possibly caused by lower saturated vapor pressure of these nitroaromatics even though they are quite electron-deficient for fluorescence quenching. And for aliphatic alcohols, only slight fluorescence change was observed probably due to the weak miscibility of SCPNs and aliphatic alcohols.52 Overall, electronic structures, saturated vapor pressure and miscibility are the primary factors in recognizing analytes through this array-based technique. For most analytes, the fluorescence response of the SCPN sensor array quickly reached equilibrium within 6 minutes at room temperature (Table S3). For example, the Euclidean distance of the representative analytes relative to the control group reached a plateau when the exposure time was only 3 min for HF, and 6 min for nitrotoluene (Fig. 6). The fast response of the array towards analytes is important in field detection, and our results are comparable to the work reported by others.3,46,53 With some in-line test setup, the saturation time could be further shortened.54 The fluorescent response of


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these SCPNs for most analytes is primarily based on equilibrium interactions between the SCPN sensors and analytes. Each concentration of an analyte has a separate pattern that can be used to evaluate a limit of detection (LOD). As shown in Figure S11, colour-change pattern for three sets of concentrationvaried analytes are given and the LODs of nitrobenzene, HF and ethanediamine are respectively ~65, 7 and 60 ppm, respectively, much lower than their immediately dangerous to life or health concentration (1000, 30 and 5000 ppm),3 indicating the potential of the fluorescent electronic nose in field detection and identification of a wide spreading of hazardous substances.

Figure 5． HCA for 30 volatile analytes and 1 control group. In these experiments, the squared Euclidean distances are defined by the average vectors of every analytes in the full 108-dimensional space (changes in red, green and blue values of the 36 SCPNs in the sensor array).


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Although the fluorescence arrays are designed to be eco-friendly and disposable, they are still reusable for many analytes. For example, when an array was used to detect octylamine or nitrotoluene (Fig. S12) vapor, the Euclidean distance reached 750 or 560, respectively. After that, it was treated with airblowing for several minutes, almost getting the whole array recovered (Euclidean distance back to about 70). After five consecutive detection circles, the arrays were still active for the next sensing measurements, demonstrating the high reusability. In addition, the fluorescence arrays have excellent fluorescence stability. No change of the fluorescence pattern was observed even when it was exposed in ambient environment for one month (Fig. S13). When the exposure time lasted for two months, two dark color spots appeared, possibly caused by interference from the air albeit showing negligible impact on PCA, and HCA even SVM process. Such a long-time stability of the fluorescence array is very important for field detection.

Figure 6. a) Euclidean distance between the analytes (HF and NT) and the control group plotted versus exposure time at room temperature. b) Reusable arrays used to detect octylamine. An air-blow operation almost recovers the whole fluorescence array after five detection circles. To check the prediction accuracy of the electronic nose, a support vector machine (SVM) pattern recognition algorithm was used. And based on the 217 trials, linear SVM model was set up and tested with widely-used round-robin leave-one-out cross-validation,55,56 where each trial in 217 was evaluated


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to classify an “unknown” analyte using a linear SVM model trained by the rest 216 trials. The prediction results are presented in Table S4, and the Arabic numerals mean the frequency of the predicted analytes and all analytes were independently predicted in septuplicate. A classification score of 99.5 % (216/217) was easily achieved for all the analytes and control group, except for a misclassification for a nitrobenzene trial which mistakes nitrobenzene as 2-nitrotoluene. Even for an unknown analyte like amylamine, which is not included in the standard library, the newly-established model using all 217 trials as training data identified the analyte as octylamine, reasonable to consider the similarity of amylamine and octylamine. All these results indicated the excellent recognition capability of the fluorescent electronic nose. We also tried to tentatively identify some analytes from a gas mixture consisting of two sets of analytes like ethanol/toluene, ethanol/DNT, ethanol/octylamine, toluene/benzene or toluene/nbutylamine (Table S5). Toluene, DNT and octylamine could be respectively distinguished from ethanol/toluene, ethanol/DNT, or ethanol/octylamine mixture. But for toluene/benzene mixture, some misclassifications were observed, possibly due to the fact that weak fluorescence response from alkyl alcohol caused slight impact on analytes with strong fluorescence influence on SCPNs (benenze derivatives and alkylamines). And obvious interference occurs when using two analytes with comparable fluorescence influence on SCPNs. Thus the identification of analytes from gas mixture is still challenging and further investigations are underway.

CONCLUSIONS In conclusion, we have designed a fluorescent-polymer electronic nose using structural diversified SCPNs as sensors in a 6×6 array. The color-difference pattern was unique for each analyte and could easily be distinguished by naked eyes. HCA and PCA indicated the diversities of electronic structures, saturated vapor pressure and miscibility of analytes were keys in differentiating these analytes, including


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explosives (nitroarenes) and toxic industrial chemicals. A linear SVM works well to predict the volatile analytes, reaching 99.5 % accuracy. Additionally, the reusable and eco-friendly fluorescence electronic nose exhibits high sensitivity (within minutes) and excellent fluorescence stability after exposed in air for months. All these findings convince us that the SCPN-based electronic nose will have general applications in industrial plant monitoring and environment conservation fields.

EXPERIMENTAL SECTION Preparation of nanoreactors.57 H2PdCl4 solution in acetone (0.5 M, 2 ml) was mixed with SS-CNMs58 (400 mg) under vacuum. After ultrasonication for 10 min, the mixture was frozen in liquid nitrogen and vacuum dried at -18 ºC for 20 h. Then the black powder was carefully rinsed with excessive deionized water until a colorless filtrate was observed. The resulting residue was treated in hydrogen/argon (10/90) atmosphere in tube furnace at 300 ºC, giving Pd@SS-CNMs (nanoreactors, 376 mg).44 General procedure for the preparation of the SCPNs through Suzuki polycondensation. Preparation of SCPN(31)42: 2,2‟,7,7‟-tetrabromo-9,9‟-spirobifluorene (94.8 mg, 0.15 mmol), Pd@SSCNMs (10 mg, 1.5×10-3 mmol of Pd), p-phenylenediboronic acid (50 mg, 0.3 mmol) and tetrabutylammonium fluoride hydrate (663 mg, 2.1 mmol) were carefully added to a 10 ml sealed tube and dried at room temperature for 30 min. DMAc (2 ml) was dropped into the tube under nitrogen atmosphere. After three freeze-pump-thaw circles, the tube was sealed at atmosphere of nitrogen. The mixture was stirred at 100 °C for 120 h. After removal of the heterogeneous catalysts by centrifugation, the supernatant was concentrated and added dropwise into methanol (60 ml). The precipitate was isolated through centrifugation and washed with methanol and n-hexane for three times and then dried under vacuum overnight (38 mg, 55 %). Other 5 SCPN(x1) members (x=2~6) were prepared in a similar manner.42,45


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General procedure for the preparation of the SCPNs through direct (thiophene) arylation polycondensations. Preparation of SCPN(53): Tetrakis(4-bromophenyl)ethene (A(5), 96.6 mg, 0.15 mmol), Pd@SS-CNMs (10 mg, 1.5×10-3 mmol of Pd) and tetrabutylammonium fluoride hydrate (663 mg, 2.1 mmol) were successively added to a 10 ml sealed tube and vacuum dried at room temperature for 30 min. Pivalic acid (10 μl, 0.13 mmol) and N,N-dimethylacetamide (DMAc, 2 ml) were carefully dropped into the tube under nitrogen atmosphere. After two freeze-pump-thaw circles, the mixture was frozen by liquid nitrogen and 3,4-ethoxylene dioxy thiophene (B(3), 32 mL, 0.3 mmol) was injected with a microsyringe under nitrogen atmosphere, then several vacuum pumping and nitrogen filling cycles were conducted. Finally the tube was sealed in nitrogen atmosphere. The mixture was stirred at 110 °C for 120 h. After removal of the heterogeneous catalysts by centrifugation, the supernatant was concentrated and added dropwisely into stirred methanol (60 ml). The formed precipitate was isolated through centrifugation and rinsed with methanol and n-hexane for three times, and then dried under vacuum at 25 °C overnight to obtain a brown solid (62 mg, 68 %). Other 17 SCPN(xy) members (y=2,3,or 4) were prepared in a similar manner.57 General procedure to synthesize terminal-modified SCPN through direct (thiophene) arylation reaction. SCPN(533): SCPN(53) (30 mg), methyl 4-iodobenzoate (T(3), 83 mg, 0.32 mmol), Pd(OAc)2 (3 mg, 0.013 mmol), KOAc (13 mg, 0.13 mmol), TBAB (18 mg, 0.056 mmol) were added to a 10 ml sealed tube. DMAc (2 ml) was carefully dropped into the tube under nitrogen atmosphere. After three freeze-pump-thaw circles, the tube was sealed under N2 atmosphere and stirred at 90 °C for 5 h. The resulting red mixture was diluted with DCM (40 mL), washed with saturated NaCl aqueous solution (3×40 mL), and dried over anhydrous MgSO4. The filtrate was concentrated and reprecipitated with


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methanol. The formed precipitate was isolated through centrifugation and rinsed with methanol for three times and then dried under vacuum at 25 °C overnight to obtain orange red solid (32 mg). For other SCPN(xyz) (x=1,3,5, y=3, z=3 or 4), a similar procedures was used. General procedure to synthesize terminal-modified SCPN through Suzuki reaction. SCPN(531): SCPN(53) (20 mg), anthracen-9-ylboronic acid (88.8 mg, 0.4 mmol), K3PO4 (127 mg, 0.6 mmol), Pd(PPh3)4 (10 mg, 0.01 mmol) were successively added to a 10 ml sealed tube and DMAc (2 ml) were carefully dropped into the tube under nitrogen atmosphere. After three freeze-pump-thaw circles, the tube was sealed. The mixture was stirred at 100 °C for 4.5 h. The resulting mixture was diluted with DCM (40 mL), washed with saturated NaCl aqueous solution (3×40 mL), and dried over anhydrous MgSO4. The filtrate was concentrated and reprecipitated with methanol. The formed precipitate was isolated through centrifugation and rinsed with methanol for three times and then dried under vacuum at 25 °C overnight to obtain orange solid (11.5 mg). For other SCPN(xy) (x=1,3 or 5, y=3, z=1 or 2), a similar procedures was used. Array preparation. Medium speed quantitative filter paper was adopted as the array substrates where all regions are printed in black with a laser printer except 36 blank spots (diameter of 2 mm). Then 36 sorts of SCPNs were integrated onto the blank spots through carefully dipping the SCPNs solutions (tetrahydrofuran, 2 mg/ml) with the help of transparent glass-based capillary (0.5×100 mm). Once integrated, the prefabricated arrays were dried under clean air for at least 24 hours and ready for the following sensing experiments. Measurement methods. A modified UV LED flashlight (UPF100, Uvata, Shanghai) with a constant voltage power source (4.2 V) was fixed at approximately an angle of 60 degrees with the array holder, and a shape-matching groove was designed for an array card to insert in and an overhead digital camera helped collect the fluorescence photographs. Saturated vapors of the analyte was obtained in


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weighing bottles (25 cm × 40 cm) where 0.5 ml liquid analytes or 200 mg solid analytes were firstly added, and then cotton was placed to maintain a constant vapor and avoid direct contact between the array card and the analyte. Then the setup was sealed with tetrafluoroethylene sealing film. For hydrogen fluoride, saturated vapor was generated in a disposable plastic cup with a plastic supporter and the cup mouth was sealed tightly with tetrafluoroethylene sealing film. After vapor equilibrium for 72 h, all the arrays were exposed to the saturated vapor of analytes for 6 min at room temperature except that TNT and aromatic aldehydes for 2 h. Data analysis. Color difference patterns were obtained by taking the difference of red, green and blue values from the center of every fluorescent spot from the „before‟ and „after‟ images using ImageJ software. To eliminate the possible deviations caused by color variations near the spot edge, only the center area was selected. For the color difference patterns, the difference value less than 32 was automatically set as zero, considering the pattern simplification and data fluctuation (All the fluctuations of each trial are less than 32). Subtraction from the “after” images yields a color-difference vector of 108 dimensions (36 changes in RGB color values), with each dimension ranging from -255 to +255 for eight-bit color imaging. All experiments were run in septuplicate and multivariable analyses (PCA and HCA) were carried out using the MultiVariate Statistical Package (Computing v. 3.22, Kovach, UK). For PCA analysis, the accuracy was set as 10-7 and Kaiser‟s rule was used to extract axes. SVM analysis was conducted with R software (3.4.2) and package e1071 was invoked. ASSOCIATED CONTENT Supporting Information. The following files are available free of charge. Experimental procedure, fundamental characterization, confusion matrices from linear SVM model and


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vector data (PDF) AUTHOR INFORMATION Corresponding Author * [email protected] ORCID Peng Zhao: 0000-0003-2380-2869 Yun Ding: 0000-0002-7012-5621 Aiguo Hu: 0000-0003-0456-7269 Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Funding Sources Notes The authors declare no competing financial interests. ACKNOWLEDGMENT Dedicated to Prof. Ji-tao Wang on the occasion of his 100th birthday. The authors gratefully acknowledge the financial support from National Natural Science Foundation of China (21674035) and Shanghai Leading Academic Discipline Project (B502). AH thanks the “Eastern Scholar Professorship” support from Shanghai local government. REFERENCES


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(1) Novotney, J. L.; Dichtel, W. R. Acs. Macro. Lett. 2013, 2, 423-426. (2) Nagarkar, S. S.; Joarder, B.; Chaudhari, A. K.; Mukherjee, S.; Ghosh, S. K. Angew. Chem., Int. Ed. 2013, 52, 2881-2885. (3) Lim, S. H.; Feng, L.; Kemling, J. W.; Musto, C. J.; Suslick, K. S. Nat. Chem 2009, 1, 562-567. (4) Woodka, M. D.; Schnee, V. P.; Polcha, M. P. Anal. Chem. 2010, 82, 9917-9924. (5) Rakow, N. A.; Sen, A.; Janzen, M. C.; Ponder, J. B.; Suslick, K. S. Angew. Chem., Int. Ed. 2005, 44, 4528-4532. (6) Germain, M. E.; Knapp, M. J. Chem. Soc. Rev. 2009, 38, 2543-2555. (7) Creaser, C. S.; Griffiths, J. R.; Bramwell, C. J.; Noreen, S.; Hill, C. A.; Thomas, C. L. P. Analyst 2004, 129, 984-994. (8) Fan, W.; Almirall, J. Anal. Bioanal. Chem. 2014, 406, 2189-2195. (9) Chajistamatiou, A. S.; Bakeas, E. B. Talanta 2016, 151, 192-201. (10) Zhang, C.; Che, Y.; Zhang, Z.; Yang, X.; Zang, L. Chem. Commun. 2011, 47, 2336-2338. (11) Liesivuori, J. Encyclopedia of Toxicology 2014, 33, 659-661. (12) Mombaerts, P. Science 1999, 286, 707-711. (13) Buck, L.; Axel, R. Cell 1991, 65, 175-187. (14) Wang, J.; Luthey-Schulten, Z. A.; Suslick, K. S. Proc. Natl. Acad. Sci. U. S. A. 2003, 100, 30353039. (15) Röck, F.; Barsan, N.; Weimar, U. Chem. Rev. 2008, 108, 705-725. (16) Freund, M. S.; Lewis, N. S. Proc. Natl. Acad. Sci. U. S. A. 1995, 92, 2652-2656. (17) Kittlesen, G. P.; White, H. S.; Wrighton, M. S. J. Am. Chem. Soc. 1984, 106, 7389-7396. (18) Grate, J. W. Chem. Rev. 2000, 100, 2627-2648. (19) Zhang, C.; Suslick, K. S. J. Am. Chem. Soc. 2005, 127, 11548-11549. (20) Rakow, N. A.; Suslick, K. S. Nature 2000, 406, 710-713. (21) Lin, H.; Suslick, K. S. J. Am. Chem. Soc. 2010, 132, 15519-15521. (22) Askim, J. R.; Li, Z.; LaGasse, M. K.; Rankin, J. M.; Suslick, K. S. Chem. Sci. 2016, 7, 199-206. (23) Swager, T. M. Accounts Chem Res 1998, 31, 201-207. (24) Fan, L.-J.; Zhang, Y.; Murphy, C. B.; Angell, S. E.; Parker, M. F. L.; Flynn, B. R.; Jones Jr, W. E. Coordin. Chem. Rev. 2009, 253, 410-422. (25) Thomas, S. W.; Joly, G. D.; Swager, T. M. Chem. Rev. 2007, 107, 1339-1386. (26) Miranda, O. R.; You, C.-C.; Phillips, R.; Kim, I.-B.; Ghosh, P. S.; Bunz, U. H. F.; Rotello, V. M. J. Am. Chem. Soc. 2007, 129, 9856-9857. (27) Han, J.; Wang, B.; Bender, M.; Seehafer, K.; Bunz, U. H. F. Acs. Appl. Mater. Inter 2016, 8, 20415-20421. (28) Bajaj, A.; Miranda, O. R.; Phillips, R.; Kim, I.-B.; Jerry, D. J.; Bunz, U. H. F.; Rotello, V. M. J. Am. Chem. Soc. 2010, 132, 1018-1022. (29) You, C.-C.; Miranda, O. R.; Gider, B.; Ghosh, P. S.; Kim, I.-B.; Erdogan, B.; Krovi, S. A.; Bunz, U. H. F.; Rotello, V. M. Nat Nano 2007, 2, 318-323. (30) Elci, S. G.; Moyano, D. F.; Rana, S.; Tonga, G. Y.; Phillips, R. L.; Bunz, U. H. F.; Rotello, V. M. Chem. Sci. 2013, 4, 2076-2080. (31) Yang, J.-S.; Swager, T. M. J. Am. Chem. Soc. 1998, 120, 11864-11873. (32) Shi, L.; Fu, Y.; He, C.; Zhu, D.; Gao, Y.; Wang, Y.; He, Q.; Cao, H.; Cheng, J. Chem. Commun. 2014, 50, 872-874. (33) Amara, J. P.; Swager, T. M. Macromolecules. 2005, 38, 9091-9094. (34) Fu, Y.; Shi, L.; Zhu, D.; He, C.; Wen, D.; He, Q.; Cao, H.; Cheng, J. Sensor. Actuat. B-Chem. 2013, 180, 2-7.


19


Page 20 of 21

(35) Rochat, S.; Swager, T. M. Angew. Chem., Int. Ed. 2014, 53, 9792-9796. (36) Dickinson, T. A.; White, J.; Kauer, J. S.; Walt, D. R. Nature 1996, 382, 697-700. (37) Levitsky, I.; Krivoshlykov, S. G.; Grate, J. W. Anal. Chem. 2001, 73, 3441-3448. (38) Dickinson, T. A.; Walt, D. R.; White, J.; Kauer, J. S. Anal. Chem. 1997, 69, 3413-3418. (39) Bosch, P.; Catalina, F.; Corrales, T.; Peinado, C. Chem. Eur. J. 2005, 11, 4314-4325. (40) Cho, J.; Anandakathir, R.; Kumar, A.; Kumar, J.; Kurup, P. U. Sensor. Actuat. B-Chem. 2011, 160, 1237-1243. (41) Albert, K. J.; Myrick, M. L.; Brown, S. B.; James, D. L.; Milanovich, F. P.; Walt, D. R. Environ. Sc.i Technol. 2001, 35, 3193-3200. (42) Deng, S.; Zhi, J.; Zhang, X.; Wu, Q.; Ding, Y.; Hu, A. Angew. Chem., Int. Ed. 2014, 53, 1414414148. (43) Huang, B.; Zhao, P.; Dai, Y.; Deng, S.; Hu, A. J. Polym. Sci., Part A: Polym. Chem. 2016, 54, 2285-2290. (44) Zhao, P.; Dai, Y.; Wu, Y.; Wang, L.; Huang, B.; Ding, Y.; Hu, A. Polym. Chem. 2017, 8, 57345740. (45) Dai, Y.; Zhao, P.; Wang, L.; Ding, Y.; Hu, A. Rsc Adv 2017, 7, 25740-25745. (46) Liu, X.; Xu, Y.; Jiang, D. J. Am. Chem. Soc. 2012, 134, 8738-8741. (47) Gole, B.; Song, W.; Lackinger, M.; Mukherjee, P. S. Chem. Eur. J. 2014, 20, 13662-13680. (48) Wold, S.; Esbensen, K.; Geladi, P. Chemotr.Intell. Lab. 1987, 2, 37-52. (49) Jolliffe, I. T.; Cadima, J. Philos.T. R. Soc. A. 2016, 374. (50) Shi, L.; He, C.; Zhu, D.; He, Q.; Li, Y.; Chen, Y.; Sun, Y.; Fu, Y.; Wen, D.; Cao, H.; Cheng, J. J Mater Chem 2012, 22, 11629-11635. (51) Toal, S. J.; Trogler, W. C. J Mater Chem 2006, 16, 2871-2883. (52) Liao, H.-C.; Hsu, C.-P.; Wu, M.-C.; Lu, C.-F.; Su, W.-F. Anal. Chem. 2013, 85, 9305-9311. (53) Janzen, M. C.; Ponder, J. B.; Bailey, D. P.; Ingison, C. K.; Suslick, K. S. Anal. Chem. 2006, 78, 3591-3600. (54) Askim, J. R.; Mahmoudi, M.; Suslick, K. S. Chem. Soc. Rev. 2013, 42, 8649-8682. (55) Cawley, G. C.; Talbot, N. L. C. Patter Recogn 2003, 36, 2585-2592. (56) Elisseeff, A.; Pontil, M. Leave-one-out error and stability of learning algorithms with applications Stability of Randomized Learning Algorithms Source, 2002; Vol. 6. (57) Zhao, P.; Dai, Y.; Wu, Y.; Wang, L.; Huang, B.; Ding, Y.; Hu, A. Polym. Chem. 2017. (58) Yang, X.; Li, Z.; Zhi, J.; Ma, J.; Hu, A. Langmuir. 2010, 26, 11244-11248.


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SYNOPSIS Conjugated Polymer Nanoparticles Based Fluorescent Electronic Nose for the Identification of Volatile Compounds. Peng Zhao, Yusen Wu, Chuying Feng, Lili Wang, Yun Ding, Aiguo Hu*


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Conjugated Polymer Nanoparticles Based ... - ACS Publications

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