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Article pubs.acs.org/JPCC

Multi-Technique Characterization through Multivariate Statistical Analysis of Copper Phthalocyanine Kinetic Activated Growth by Supersonic Molecular Beam Deposition Rossana Dell’Anna,*,† Roberto Canteri,† Nicola Coppedè,‡ Lia Vanzetti,† Aderemi Babatunde Alabi,‡,⊥ Enzo Cazzanelli,§,∥ Marco Castriota,§,∥ Salvatore Iannotta,‡ and Massimo Bersani† †

Fondazione Bruno Kessler - Center for Materials and Microsystems, Via Sommarive 18, I-38123 Povo (Trento), Italy Institute of Materials for Electronics and Magnetism (IMEM), National Research Council (CNR), Parco Area delle Scienze 37/A, I-43124 Parma, Italy § Dipartimento di Fisica, Università della Calabria, Ponte P. Bucci, cubo 31c, I-87036 Arcavacata di Rende (Cosenza), Italy ∥ C. N. I. S. M., Via della Vasca Navale 84, I-00146 Roma, Italy ‡

ABSTRACT: Supersonic molecular beam deposition is a far-from-thermalequilibrium kinetic activated growth technique, which allows the fine control of the kinetic energy of molecular species. We present a study of the growth of very thin layers of copper phthalocyanine on nanostructured surfaces of titanium dioxide nanograins; the study combines time-of-flight secondary ion mass spectrometry, Raman, X-ray photoelectron spectroscopy, and multivariate statistical analysis. Different kinetic energies and layer thicknesses were considered to investigate bond formation and surface interaction between the organic molecules and the inorganic nanograins. This study allowed the clarification of the key role of the energetic properties of the supersonic beam in the surface activation, enabling bond formation, not available with other processes at equilibrium. In particular, high kinetic energy regimes for copper phthalocyanine molecules in high-dilution seeded beams allow the formation of stronger bonds at the interface, which is useful for producing innovative nanohybrid materials having specific improved structural and chemical characteristics. The comparison among different surface characterization measurements orchestrated by multivariate statistical analysis proved to be a valuable approach for surface interaction study and interpretation.

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INTRODUCTION The ability to control the interaction at the interface of organic thin films on nanostructured substrates is crucial for improving their electric and structural properties and for enhancing their performance in devices.1 In particular, the formation of strong chemical bonds, with respect to the weak interaction of simple physisorbtion, is a determinant for achieving specific transport properties at the interface and for improving adhesion and structural order of successive layers.2 To achieve effective control over bond formation at the interface, a crucial requirement is the ability to tune, during the growth, the different energetic properties of the molecules (internal energy, kinetic energy, angular momentum), allowing the synthesis of nanohybrid materials with innovative tailored functionalities.3 Among different techniques used for organic thin film growth, supersonic molecular beam deposition (SuMBD) takes advantage of the supersonic expansion in vacuum of a carrier neutral gas seeded with organic molecules to obtain molecular beams with higher kinetic energies, an improved alignment, and a narrower angular momentum distribution.4 As described in many different works,5 the physical properties of the beam can be finely tuned by varying peculiar source parameters, in particular the molecular degree of seeding in the gas. We were © 2014 American Chemical Society

interested in the ability of this deposition technique to access different growth conditions and regimes also far from thermal equilibrium, promoting in this way the formation of different kind of bonds at the interface.6,7 With this aim, we present a sample case study: the organic molecular growth of copper phthalocyanine (CuPc) on nanostructured grains of titanium dioxide (TiO2). Metal phthalocyanine are π-conjugated organic molecules widely studied for their appealing optical and electronic properties which, combined with their high thermal and chemical stability, make them interesting and promising candidates for applications in electronic devices,8 organic and hybrid solar cells,9 and gas sensor functionalization.10 The molecular formula of CuPc is C32H16CuN8; the structural formula is shown in Figure 1. TiO2 nanograins form a nanostructured substrate of great interest especially for dyesensitized solar cells,11 ensuring for them the highest performances; moreover, this substrate is useful for photocatalysis,12 gas sensing,13 and biomedical applications.14 The interest in studying the interface between an organic oligomer Received: February 24, 2014 Revised: April 29, 2014 Published: May 1, 2014 10883

dx.doi.org/10.1021/jp501912s | J. Phys. Chem. C 2014, 118, 10883−10892

The Journal of Physical Chemistry C

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using a He carrier gas pressure in the range of 100−200 kPa. The central part of the beam is selected by skimming the free jet expansion via a sharp edged conical collimator, which separates the source from the deposition chambers (base pressure 10−8 mbar). Here, the molecular beam is intercepted by the substrate that can be temperature-controlled. In this work all CuPc films were grown at room temperature. A quartz microbalance was used to measure the deposition rate; we chose the same rate of 0.5 nm/min for all depositions. The source operating conditions were tuned to always remain at 15 eV for all the films grown at the highest kinetic energy, whereas an estimated kinetic energy of 0.1 eV was maintained for the samples grown at the lowest kinetic energy. The in-line ToFMS coupled to a laser (fourth harmonic of a Nd:YAG laser at 266 nm) multiphoton ionization measured the kinetic energy and also monitored the intensity, purity, and stability of the beam. The duration of the deposition was set to produce films of two different thicknesses (9 and 4.5 nm). We used CuPc row material coming from the same batch (Syntec-Sensient GmbH) for all experiments. We purified it by repeated vacuum gradient sublimation cycles, after which the ToF-MS spectra did not show any significant residual contamination. The different combinations of kinetic energy used and nominal thicknesses of CuPc growth, which characterize the samples discussed in this paper, together with the number of positive spectra acquired through time-of-flight secondary ion mass spectrometry (ToF-SIMS) from each sample surface (described in next subsection) are outlined in Table 1.

Figure 1. Structural formula of CuPc.

and a nanostructured metal oxide is related to different applications, involving effects on charge transfer, Schottky barrier formation at the interface, and structural degree of order in the organic layers, always depending on the formation of different kind of chemical-physical bonds at the interface.15 This work therefore compares the effects on the interface interaction of two different kinetic energies (15 and 0.1 eV) used during the CuPc film growth on a TiO2 substrate, the highest one being associated with a complete out-ofthermodynamic-equilibrium process, as well as of two different nominal CuPc film thicknesses (4.5 and 9 nm). With this aim, different surface analytical techniques were used to characterize the first upper layers of growth to understand the role of the kinetic energy of the beam in surface bond formation.

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EXPERIMENTAL AND MULTIVARIATE STATISTICAL METHODS CuPc Growth by Supersonic Molecular Beam Deposition. Samples of nanostructured TiO2 were deposited on silicon−silicon oxide substrates using a pulsed microplasma source,16 finding the chemical, structural, and morphological characteristics of samples already described in detail in previous papers by some of the authors.3,17 In short, the source allowed the growth of TiO2 films that were subsequently characterized by X-ray diffraction (XRD) and atomic force microscopy (AFM) measurements.17 They exhibited clear evidence of crystallinity, with nanostructured, almost spherical, grains of 10−20 nm diameter having a stoichiometric anatase crystal phase. In addition, AFM analysis showed that the grain packing produced hollows, typically of 50 nm size, thus defining a mesoporous surface. The complete covering of the silicon substrates was ensured by depositing 500 nm of TiO2 nanograin films. The growth of CuPc thin films was performed in a tailormade SuMBD apparatus, described previously in more detail.5 It basically consists of a differentially pumped supersonic beam, a time-of-flight mass spectrometer (ToF-MS), and a deposition chamber. The supersonic beam source, placed in a high-vacuum chamber, is made of a quartz tube closed at the front end, where a nozzle (typically 50−130 μm in diameter) is present. An inert carrier gas is injected into the source at a controlled pressure (2−3 bar); the organic material is sublimated by Joule heating and dispersed at very low concentrations into the gas expanding through the nozzle. When some working parameters (nature and pressure of the carrier gas, sublimation temperature, nozzle diameter, and temperature) are changed, key properties of the molecules in the supersonic beam can be finely controlled, i.e., kinetic energy, momentum, and degree of cooling of internal degrees of freedom, typically induced by expansion. In our experiment, the source typically operated

Table 1. Samples Used in This Study sample name

Ec (eV)

thickness (nm)

no. of ToF-SIMS spectra

HE45 HE9 LE45 LE9 LEB HEB

15 15 0.1 0.1 −

4.5 9 4.5 9 −

3 3 3 3 3

Throughout this paper, CuPc films deposited with 4.5 and 9 nm thickness by using the highest kinetic energy (15 eV) are named HE45 and HE9, respectively. Similarly, CuPc films deposited using the lowest kinetic energy (0.1 eV) are labeled as LE45 and LE9. The sample set is completed by two bare substrates (low-energy bare substrate (LEB) and high-energy bare substrate (HEB)), namely, the two nanostructured TiO2 films, deposited on silicon−silicon oxide substrates, used in the CuPc deposition process at the two different kinetic energies. Time-of-Flight Secondary Ion Mass Spectra Acquisition and Preprocessing. ToF-SIMS is a well-known surfacesensitive analytical technique18 used to characterize surface chemistry and composition of both inorganic and organic compounds (metals, dielectrics, polymers, self-assembled monolayers) as well as biomaterials (for example, amino acids, tissue samples). ToF-SIMS is also attractive because of its excellent chemical specificity and surface sensitivity.19 The large number of peaks in ToF-SIMS spectra makes data interpretation and sample comparison a complex task, requiring the application of multivariate analysis techniques, which maximize the usable information contained in the spectra.20 In this work, principal component analysis (PCA), heat map (HM) analysis, and feature selection21 have been applied to highlight existing variations among different spectra with respect to the fragmentation pattern of CuPc, providing in 10884



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PCA. The spectral matrix D is of size s × m, i.e., s = 18 spectra, each one containing the same m = 355 mass peaks. PCA was used to reduce the dimensionality of this mdimensional data set, while preserving most of the original information. This was done by calculating the principal components (PCs),24 which are uncorrelated, linear combinations of all the original m features, solving the eigendecomposition problem for the spectra covariance matrix; that is, Cov(D) = UΛUT, where U is the orthogonal m × m matrix of PCs (eigenvectors), and Λ = diag(λ1,..., λm) contains the nonnegative eigenvalues, which are the PC variances. Because Tr(Cov(D)) = Tr(Λ), the PC variances can be treated as components of the total variance in the original data set, and by sorting the PCs in descending order of their eigenvalues, PCA actually provides a principal axis rotation of the original features (peaks) about their means that optimally describes the data set in terms of its variance. Therefore, by choosing the first n < m PCs that explain most of the data variance (cumulative percentage of captured variance), it is possible to reduce the dimensionality of the original m-dimensional data set without losing significant information. PCA represents discovered relationships among spectra and peaks of the data matrix through score and loading plots. In this work we always chose n = 2, and because PCs are oriented to best describe the spread in the data, projecting the multidimensional spectra onto this two-dimensional PC plane (score−score plot) allowed a graphical visualization of relationships and patterns among the high-dimensional ToFSIMS spectra, as explained by the captured variance. In addition, by direct comparison with the scores, the loading plots allowed the identification of group patterns of peaks responsible for groups and differences highlighted in the corresponding score plots. With this aim, the loadings simply correspond to the correlation of each PC with each of the original features. HM. HMs are false color maps, which can be used to give a simultaneous graphical representation of a set of spectral measurements, allowing visual at-a-glance multiple comparisons.25,26 In the HMs discussed herein, which are based on the spectral data matrix D, each row of the grid corresponds to one spectrum and each column to one peak of the list. Different colors map the entire range of intensities of each peak across all ToF-SIMS spectra: relative to its mean value, lower intensity values tend toward brighter green tones whereas higher values tend toward brighter red tones. The ordering of spectra and peaks is separately determined by two dendrograms, which group them into clusters of similar entries. The dendrograms result from hierarchical cluster analysis (HCA);27 in this work, it was carried out choosing the average linkage clustering algorithm and the metrics based on the Pearson correlation coefficient. The dendrograms allow the visualization of existing patterns or groups in the set of spectra and a graphical understanding of them in terms of covariation of peak groups across all sample measurements. X-ray Photoelectron Spectroscopy. X-ray photoelectron spectroscopy (XPS) measurements were performed to confirm the compositional characteristics of the samples. Moreover, XPS contributed in the discussion of the adhesion of the organic molecules on the nanostructured oxide surfaces. XPS spectra were recorded using a Scienta Esca-200 system, equipped with a monochromatized Al Kα (1486.6 eV) source. An overall energy resolution of 0.4 eV is routinely used. The measurements were performed at 90° emission angle where the

this way information about the distribution and bonding of CuPc molecules on the nanostructured substrates. ToF-SIMS measurements were performed on an ION TOF TOF-SIMS IV Reflectron time-of-flight secondary ion mass spectrometer (IONTOF, GmbH, Muenster, Germay) equipped with a gallium liquid metal ion gun (LMIG). For each sample, three positive ion spectra were acquired over an area of 200 × 200 μm2 by using primary ions operating at 15 keV with a pulse width of 24 ns and a repetition rate of 10 kHz. The analysis area was rastered by primary ions, the dose of which was maintained below 1012 ions cm−2 to ensure static SIMS conditions. The mass resolution (M/ΔM) at C7H7+ (m/z = 91) was usually more than 7000 for all samples. Before carrying out multivariate analysis, the peaks present in each spectrum were integrated, corrected for dead time effects of the registration system using the Poisson formula, normalized to the primary ion fluencies, and mass calibrated. A list of 355 entries, also called features, was then created, gathering the m/z values of all integrated peaks from all spectra. This list allowed the building of a spectral matrix D, consisting of 18 rows and 355 columns, i.e., 18 ToF-SIMS spectra (6 samples × 3 measurement replicas), each one containing intensity values of the same 355 peaks, to be used in subsequent multivariate analysis. As discussed in ref 21, some of the heaviest CuPc species showed an easily identifiable isotopic pattern in ToF-SIMS spectra. Keeping in mind the final multivariate analysis to be carried out on those spectra, a subselection of some of the peaks inside this pattern, through inferential statistical analysis, would not be informative; therefore, it was avoided, summing as one the intensities of all those peaks. The obtained values were associated in the peak list to the label “Clusterx”; x corresponds to the mass unit of the most intense peak of the pattern. Multivariate Statistical Analysis. In this paper PCA and HM graphical representations of ToF-SIMS spectra are exploited to investigate the effects on CuPc film growth of different deposition conditions. Given the high-throughput nature of ToF-SIMS data, to extract relevant information from the spectra, we applied a statistical automatic peak selection procedure based on a nested ANOVA with permutation test (NAPT), extensively described in a previous work.21 In this section we only briefly describe the main concepts behind NAPT, PCA, and HM. Further details may be found in provided references. Univariate and multivariate statistical analyses were carried out in the R computing environment22 (version 2.15.2) by using packages and the in-house code described in ref 21. Before PCA and HM processing of ToFSIMS spectra, these were normalized with respect to the sum of their peak intensities.23 Each column of the spectral data matrix was then mean-centered, i.e., transformed to have zero mean. Nested ANOVA with Permutation Test. NAPT is a modified ANOVA test, applied in this work for ToF-SIMS peak selection; it must be carried out when the experimental design consists of measurement replicas coming from the same sample.21 This univariate procedure separately and automatically selects those peaks statistically significant, above the within-sample and among-sample variability, discriminating between CuPc films grown on TiO2 substrates and uncovered TiO2 samples. The process deposition effects are thereby described with minimal and not redundant information: in this work, from all the 355 acquired ToF-SIMS peaks, the selection procedure reduced them to 62. 10885



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sampling depth is approximately 10 nm. For each sample, Cu 2p, O 1s, Ti 2p, N 1s, and C 1s core levels were collected. All core level peak energies were referenced to the saturated hydrocarbon in C 1s at 285.0 eV. Micro-Raman Spectroscopy. The multi-instrument characterization carried out in this work comprises micro-Raman spectroscopy (μ-Raman) from the very first layer of CuPc growth. It is well-known that μ-Raman analysis is particularly valuable in studying the formation and evolution of amorphous or crystalline regions, belonging to various phases down to the micrometer length scale, and that the typical spectral line shapes of CuPc are very sensitive to structural order as well as to chemical bond formation.28−30 In this work, to understand the properties of the CuPc molecules at the interface, we compared the Raman spectra of the four considered very thin layers of CuPc on a TiO2 substrate by looking in particular at their distortion, which is function of the type of surface bond formation. With this aim, a Horiba-Jobin-Yvon Labram apparatus was used, equipped with a CCD detector and a He−Ne laser (632.8 nm emission). The experiments were performed at room temperature with a 100× Mplan Olympus objective with a NA of 0.90. The resulting power out of the objective was about 5 mW, focused on a spot of about 3 μm diameter. To avoid structural changes due to laser heating, neutral filters, ranging from OD 2 (1% of allowed transmission) up to OD 0.3 (50% of transmission), were applied to the laser during the Raman spectra collection in such a way so as to have a nonperturbative probe for the samples. Because of the low laser power used, long integration times (minutes) were necessary to have a sufficient signal-to-noise ratio. The resulting spectral resolution of the apparatus was about 2 wavenumbers.

Figure 2. PCA on the complete set of ToF-SIMS spectra, encompassing all the 355 peaks: (a) PC1 versus PC2 score plot and (b) PC1 versus PC2 loading plot.

Therefore, this first analysis of ToF-SIMS spectra shows that the highest-energy deposition conditions produce samples that are definitely more similar to uncovered substrates than to lowenergy samples. This result, together with the lower withinsample similarity of HE45 measurements, is well-outlined by the dendrogram reported in Figure 3 and obtained from HCA

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RESULTS CuPc Film Characterization: Concentration and Thickness. Figure 2 shows the results obtained by applying PCA to the initial set of spectra, encompassing all 355 peaks. The first principal component (PC1) versus the second principal componet (PC2) score plot of Figure 2a describes 99% of the overall data variance. The largest (81.1%) proportion of variance, explained by PC1, is associated with the difference existing between the lowenergy samples (LE9 and LE45), having positive PC1 scores, and all remaining samples (bare substrates LEB, HB, and highenergy samples HE45 and HE9) characterized by negative PC1 scores. Finer structures inside the two groups are instead accounted for by PC2, capturing 17.9% of the overall data variance, that allows distinguishing between LE45, having positive PC2 values, and LE9, having negative values, as well as between HE9/LEB spectra (positive PC2 scores) and HE45/ HEB spectra (negative PC2 scores). In addition, PC2 accounts for measurement reproducibility and shows that the lowest one is associated with HE45 spectra. The corresponding loading plot is reported in Figure 2b; it allows ascribing the differences described by PC1/PC2 scores to a few most important species: those spectra lying in the negative PC1 half-plane show higher counts of a few Ti-based molecules, whereas in those spectra having a positive PC1 score a few Clusterx molecules are more intense. The different loading values for Clusterx and CxHy species determine in addition the differences described by PC2 in Figure 2a. All remaining peaks are essentially irrelevant for this first raw analysis.

Figure 3. HCA on the complete set of ToF-SIMS spectra, carried out using all 355 peaks.

carried out on all spectra as a part of HM data representation (HM results not shown). PCA and HM clearly show that the complete set of ToF-SIMS peaks bears redundant or irrelevant information, preventing a tidy distinction of measurements into LE samples, HE samples and bare substrates. NAPT Feature Selection. NAPT feature selection was therefore carried out to look for a subset of peaks able to statistically discriminate between bare substrates and CuPc covered samples, trying in this way principally to separate, if possible, HE spectra group from uncovered samples LEB and 10886



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HEB. Addressing this issue, NAPT reduced the initial 355 peaks to 62. Multivariate statistical analysis was then repeated using only the selected features, looking in this way for a possible better understanding of the effects of different deposition conditions. The obtained results are illustrated through the HM analysis reported in Figure 4.

Table 2. 62 out of 355 Peaks Selected by NAPT and Divided into Subclusters A and B, as Highlighted by HM Analysis Shown in Figure 4

Figure 4. HM representation of the complete set of ToF-SIMS spectra encompassing only those peaks selected by NAPT. The dendrogram reported to the left hierarchically clusters the spectra, whereas that on the top shows the result of hierarchical clustering of the peaks. A and B allow the easy identification of the two main subclusters of peaks discussed in the text. The color bars on the left identify the three spectra acquired from each sample.

It stands out that after peak selection the hierarchical clustering carried out on the spectra allows the bare substrates to be joined into a first group; to this cluster, first HE45 and then HE9 spectra aggregate, with a decreasing similarity order. Therefore, NAPT succeeds in highlighting the spectral signal bearing differences between HE and bare samples, even though this turns out to be not sufficiently intense to aggregate the high-energy samples into a separate cluster. Figure 4 allows the spectra pattern to be related to the covariation of some peak intensities described by the dendrogram reported on the top, which in fact provides a visualization of the hierarchical clustering sequence of the peaks. Two well-separated main subclusters are shown: cluster A, composed only by fragments distinctly coming from CuPc molecules, and cluster B, containing species not ascribable to the deposited molecule but rather emitted from the substrate. All the peaks selected by NAPT are listed in Table 2 together with their subcluster membership. HM clearly shows that the selected CuPc peaks are intense in LE9 and LE45 measurements while their counts dramatically decrease first of all for bare substrates and for two out of three HE45 spectra and to a slightly lesser extent for HE9 as well as for one HE45 measurement. In a complementary way, the uncovered samples understandably show intense signals coming from substrate molecules; the same signals more or less also characterize HE45 spectra, whereas HE9 measurements show again intermediate intensity values. This HM analysis on selected peaks shows, in conclusion, that (1) the concentration of CuPc on LE9-LE45 surfaces is higher than that on HE9, the CuPc presence being even less on HE45 sample. Therefore, for samples having the same nominal

cluster A

cluster B

CH3N2Cu2 C8H15N2Cu C10H6N2 Cu2N2H2 CN63Cu2 CN63Cu65Cu Cluster591 Cluster605 Cluster291 Cluster561 C9H6N2Cu C7H4Cu C8H4N C10H6NCu C6H14N2Cu 65 Cu 63 Cu C7H4N C9H7N C6H4N2Cu Cluster480

C3H11N2 C5H10NO CH2NO C4H8NO CHO2 C4H5O4 SiC2H Si2C5H3N Si2C3H9 O2 CH4 C2H6O C2H3O CH3O C2H5O CHO C2HOTi CH4O C3H8O CH2O C2H4O C2H4O2 C5H11O C6H11O C4H6O C3H6N3 C5H7N3 C6H7O C2H5O3 C3H3OTi C4H7O2Ti C4H5O C4H9O C4H4O C5H9O CH2 H3O C4H7O C3H6O C2HO2Ti C2 H C5H2

thickness, different deposition conditions do not ensure the same surface activation results. (2) Given the same kinetic energy conditions of the supersonic beam, the two nominal thicknesses are instead in qualitative agreement with the different brightness of the corresponding HM rows (two different red intensities for LE samples, and two different green tones for HE samples), meaning in fact a different quantity of CuPc molecules. (3) The intravariability of HE45 spectra remains the highest one, although reduced, after peak selection; this suggests a nonuniform coverage of the nanostructured inorganic substrate at 4.5 nm and high kinetic energy, also compatible with the existence in the HE45 sample of more intense signals coming from substrate species. XPS. The ToF-SIMS-data-based discussion about CuPc presence on samples is supported by XPS results, shown in Table 3, where the reported element concentrations are 10887



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decreasing with increasing thickness. When XPS results for the same nominal thickness are compared, in agreement with ToFSIMS results, a different CuPc sticking coefficient emerges between low and high kinetic energy samples, with a sensible reduction for the second samples. Surface Activation Enabling Bond Formation. To help understanding the effects of different growth regimes on the interface interaction, PCA was repeated by considering only the samples carrying deposited CuPc (HE9, HE45, LE9, LE45), after having retained only those peaks belonging to subcluster A of Figure 4, that is, only those 21 peaks pointed out by feature selection and related to CuPc. Obtained results are shown in Figure 6a,b.

Table 3. Quantitative XPS Results for the Analyzed Samplesa sample

Cu (%)

O (%)

Ti (%)

N (%)

LE9 LE4.5 HE9 HE4.5 HEB

1.3 0.6 − − −

34.1 44.0 47.8 48.4 49.9

13.7 18.2 18.6 20.3 19.8

6.5 2.7 1.8 1.5 −

a

Copper, oxygen, titanium, and nitrogen concentration (in atomic percentage) were calculated using atomic sensitivity factors.

compared among all the deposited samples and one bare substrate, namely HEB. According to Table 3, as there are no copper and nitrogen on the blank sample, the nitrogen measured in all the other samples is reasonably due to the CuPc molecule. In both HE samples, the copper concentration is below the XPS detection limit (XPS sensitivity is lower than that of ToF-SIMS); as the measured Cu:N concentration ratio is about 1:8, we have evidence of copper presence (and therefore of CuPc molecule) through nitrogen concentration. Therefore, the highest CuPc concentration is in LE9, then LE45, HE9, and HE45, in agreement with ToF-SIMS results. This statement is confirmed by the behavior of titanium and oxygen core levels (the substrate), shown in Figure 5a,b,

Figure 6. PCA carried out only on samples with deposited CuPc film after having retained from the ToF-SIMS peaks selected by NAPT only those related to CuPc molecule: (a) PC1 versus PC2 score plot and (b) PC1 loading plot, where for readability reasons the fragment labels are shown only for loadings l < −0.05.

As the variance captured by PC1 (99.4%) dominates with respect to that described by PC2 (0.5%), the relation among scores and loadings is thoroughly described by PC1 values. The score plot shows a clear distinction between LE and HE samples, while in Figure 6b the presence of only negative values for PC1 loadings asserts that moving from negative to positive PC1 values corresponds only to a decrease of the overall CuPc signal intensity. Not surprisingly, this result matches the color gradient corresponding to CuPc peaks in subcluster A of Figure

Figure 5. (a) Comparison of Ti 2p core level in the samples analyzed by XPS. It is worth noting that the line shape does not change and that titanium is completely oxidized. (b) Comparison of O 1s core level in the analyzed samples. It is worth noting that the line shape does not change; two peaks are visible: one due to O−Ti bonds (530 eV) and the other to C−O bonds (532 eV). 10888



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4. The possible existence of a different relative intensity among peaks in HE spectra with respect to that in LE spectra, if any, is therefore hidden by the existing pronounced different CuPc surface concentration in LE and HE samples. To get rid of this difference, the spectra of Figures 6a were normalized with respect to the total counts of the 21 peaks they retained. Results of subsequent PCA are reported in Figure 7a,b.

Figure 8. Box plot displaying the distribution in LE9 (solid line) and HE45 (dashed line) ToF-SIMS spectra of the intensities of some representative peaks related to CuPc molecule. The peak intensities have been normalized as in Figure 7.

loading values; these representative species are reported on the x axis of all box plots according to their increasing m/z ratio. In Figure 8 we separately consider the contributions coming from LE9 and HE45 samples (located at the two extremes of PC1 axis in Figure 7a); their peak intensities have been normalized to the total counts of all the CuPc peaks selected by NAPT, as was already done for Figure 7. In Figure 9a−d we separately consider for the same peaks the intensities shown by LE9, LE45, HE9, and HE45 spectra, but now the peaks have instead not been normalized. Differently from Figure 8, the scales on the y axis in Figure 9 take into account the different total CuPc concentrations; when Figures 8 and 9 are compared, the box plots show the existence of a different fragmentation process for HE and LE spectra, with a higher yield of cluster-like fragments in LE samples and a higher yield of Cu isotopes and some other light species in HE samples. In more detail, in Figure 9a,b it clearly appears that the box plot analysis for LE9 and LE45 spectra provides the same results, with the only exception being the different overall intensity of the CuPc signal, which is lower in LE45; the relative ratios among peaks remain essentially constant. In the HE9 sample, shown in Figure 9c, the intensities of Clusterx and CNCuy fragments begin to decrease while for some smaller fragments, first of all Cu isotopes and to a lesser extent C6H4N2Cu, C7H4Cu, and C9H7N, they begin to increase; this trend is confirmed and accentuated in the HE45 sample of Figure 9d. In addition, when Figures 8 and 9 are compared, it also appears that the intensity variation of those peaks, which were essentially undifferentiated in PCA, actually depends in box plot analysis only on CuPc concentration. Considering Figure 8 and when Figures 9a,b and 9c,d are compared, the different spread of measurement replicas in samples LE with respect to samples HE also appears. Therefore, the two highlighted different fragmentation patterns suggest a different surface interaction between the organic CuPc molecules and the inorganic TiO2 nanograins for the two kinetic energies of the beam, that is, a weaker one for LE and a stronger one for HE molecules, so that for a given ToF-SIMS primary ion dose and energy, larger CuPc fragments are sputtered more easily from LE sample surfaces than from HE surfaces, whereas in these latter, the CuPc molecule is preferably fragmented into smaller pieces. μ-Raman Spectroscopy. This discussion is confirmed and further clarified by μ-Raman spectroscopy performed on the same samples. In fact, Figure 10 shows the Raman spectra

Figure 7. PCA carried out only on samples with deposited CuPc film after having retained among the ToF-SIMS peaks selected by NAPT only those related to the CuPc molecule; in addition, the spectra have been normalized to the total counts of these retained peaks: (a) PC1 versus PC2 score plot and (b) PC1 loading plot.

Not surprisingly, the variance captured by the selected CuPc fragments is still thoroughly represented by PC1; along this axis can be easily distinguished first spectra LE9, next and very close LE45, then HE9 spectra together with one HE45 and eventually two HE45 spectra, located in correspondence to the highest positive PC1 values. The loading plot in Figure 6b now allows species having negative loading values to associate to LE (first of all LE9 and then LE45) measurements; they typically are heavy fragments like some Clusterx and some lighter CNCux species, whereas some lighter fragments, first of all Cu and then some CxHyNz(Cu) species, dominate in HE spectra (HE45 first and then HE9). There is also a cloud of fragments with intermediate or high mass ratio, whose loading is negligible for LE as well for HE samples. To further investigate these results, box plots of some representative species are shown in Figures 8 and 9a−d. The considered CuPc fragments have been picked among those 21 selected by NAPT (i.e., belonging to subcluster A), taking into account the previously detected different contribution to PCA of species having in Figure 7b higher negative or positive 10889



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Figure 9. Box plot displaying the distribution in the ToF-SIMS spectra acquired from samples with deposited CuPc film of the same peaks of Figure 8: distribution in (a) LE9, (b) LE45, (c) HE9, and (d) HE45 spectra.

respectively, and it is crucial to underline that sample HE45 shows the highest shift. This sample has the highest percentage of molecules interacting with the substrate; hence, the Raman analysis proves that the distortion is higher for those molecules at the interface and is therefore related to a stronger interaction with the TiO2 substrate. In fact, the shift is reduced for those CuPc molecules interacting with other ones of the same nature, as in sample HE9, deposited with the same kinetic energy but carrying a CuPc film 9 nm thick. The samples at lower kinetic energy present in comparison a weaker interaction with the TiO2 substrate, which does not bring about any distortion, both for thickness 9 nm and even more considerably for the molecules at the interface (thickness 4.5 nm). It is worth noting that the distance, along the PC1 axis, among LE9, LE45, HE9 and HE45 in Figure 7a, obtained after proper multivariate processing of the data and related as previously discussed to different fragmentation processes, distinctly recalls the different Raman shifts in Figure 10. In conclusion, Raman and ToF-SIMS analysis show that the formation of strong bonds at the interface clearly depends on the kinetic energy of the molecular beam; with this aim, a supersonic high-energy regime is strictly necessary.

acquired from the samples carrying the deposited CuPc (HE9, HE45, LE9, LE45).

Figure 10. μ-Raman spectra of CuPc layers on TiO2 substrate. LE45 and LE9 are on the bottom, and HE9 and HE45 are on the top. To improve figure readability, HE45 and HE9 spectra have been shifted by 300 and 600 arbitrary units, respectively, along the y intensity scale.

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The peak reported in Figure 10 is related to pyrrole stretching vibration, which is particularly sensitive to molecular distortion, which is generally related to crystalline structure or bonding interactions.31 As demonstrated in previous studies,32 this peak tends to have a rigid shift to lower values for distorted molecules. In Figure 10, the two LE samples present a peak at 1534 nm, independent from their thickness; this energy position is typical of a nondistorted molecule. On the other hand, in both HE9 and HE45 samples the pyrrole peak presents a shift in its position, at 1530 and 1527 nm,

DISCUSSION A multitechnique study was proposed, which compared ToFSIMS, μ-Raman, and XPS analysis of the first layers of growth of CuPc films on a nanostructured TiO2 surface, to understand the properties of different growth regimes carried out by SuMBD. With this goal, two thicknesses and two very different kinetic energy regimes for the CuPc beam were explored. The multitechnique approach together with multivariate statistical 10890



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analysis allowed the creation of a comprehensive picture of the bonds at the interface and the correlation of it with the kinetic energy of the supersonic beam. A first study focused on the characterization of the CuPc growth. In this respect, both ToF-SIMS and XPS results pointed out the existence of a different CuPc sticking coefficient between low and high kinetic energy samples, with a sensible reduction of it for the latter. In fact, both techniques revealed, for a given thickness, a concentration of CuPc on HE lower than that on LE samples, and accordingly, the Raman intensity of the pyrrole peak in HE samples was lower. In addition, ToF-SIMS analysis pointed out the possibility of the deposition of a nonuniform CuPc film at the highest energy in the very first phases of the growth (especially sample HE45), which was also seen before in unpublished AFM characterization measurements of similar samples. When the duration of the deposition time was further increased, uniformity appeared to be essentially reached. Hence, the resulting surface reactivity is different for the outof-thermodynamic-equilibrium regime. The reduction of the sticking coefficient for HE samples could be related to the different aggregation on the surface and to a different reaction for higher kinetic energies to irregular surface angles. In this respect, we previously observed32 for other phthalocyanines the formation of crystallites and demonstrated that this aggregation could be enhanced by modulation in size and form at higher kinetic energies, going from a typical diffusion limited aggregation (DLA)33 model at lower energies to other specific dynamic growth models presenting instead a higher degree of structural order for higher energies. It is evident that by reducing the sticking coefficient the characteristics of the growth change as well, but it is worth noting that for both the considered kinetic energies, the CuPc film present a stable growth,31,32 which makes this deposition process suitable for applications. We next investigated the dependence of the interfacial bond strength on the kinetic energy of the deposited molecules. ToFSIMS and μ-Raman spectroscopy pointed out that a supersonic high-energy regime is necessary for a strong bond interaction at the organic−inorganic interface. This result was obtained by comparing the multivariate analysis of the fragmentation patterns in ToF-SIMS spectra with the CuPc molecular distortion at the interface in Raman spectra. The increasing strength of the interaction for the molecule at the interface, in the case of higher kinetic energy as pointed out by Raman spectroscopy, was coherently confirmed by the existence of a different ToF-SIMS fragmentation process for the activated molecules, which is compatible with a stronger bond formation at the interface. In sum, the application of multivariate statistical analysis techniques on TOF-SIMS data, together with XPS and μRaman analysis clearly illustrated in SuMBD the dominating role of the kinetic energy of the molecules in the beam to control surface activation and enable strong bond formation. These specific, controlled growth conditions pave the way for developments and improvements on many innovative devices, for example, at the interface of organic−inorganic materials for solar cells or at the contact between semiconductors and molecular layers in organic field effect transistors. In these and other devices, specific properties can be obtained by tuning the interface features that are critical issues for increasing device performances, such as the improvement of charge injection efficiency or the lowering of potential barriers.34

Moreover, the leading role of multivariate statistical analysis in multitechnique comparison and integration as well as its innovative role in a more exhaustive exploitation of complex information carried out by ToF-SIMS spectra indicates a template for an adaptable analytical methodology that could be exploited in other studies. Possibile applications include the characterization of hybrid inorganic−organic interface properties and first layers of growth, such as metal−organic molecules, dyes, and other light absorbers on nano-oxides, and the study of innovative interfaces, such as organic molecules on graphene and conductive layers on hybrid perovskites.

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CONCLUSIONS The ability to tune different kinetic activated growth regimes for organic molecular beams is of paramount importance for the synthesis of innovative nanohybrid materials in order to achieve higher control not only on the structural properties at the interface but also on the properties of the interaction of the molecules with the substrate. In this work we demonstrated that the kinetic energy tuning in CuPc molecular supersonic beams allows control of the nature of the bonds at the inorganic−organic interface, ensuring in this way the production of nanohybrid materials having specific improved structural and chemical characteristics. In particular, the application of multivariate statistical analysis techniques on TOF-SIMS data, together with XPS and μ-Raman analyses, pointed out a different surface reactivity for the out-ofthermodynamic-equilibrium regime together with a consequent reduction of the sticking coefficient of the organic molecules on the inorganic nanostructured surface. Moreover, the existence of a different fragmentation process for the activated molecules, related with a rigid Raman shift of one of their characteristic peaks, showed that the controlled beam kinetic energy enables stronger bond formation at the interface.

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AUTHOR INFORMATION

Corresponding Author

*Phone: +390461314673. Fax: +390461314666. E-mail: [email protected]. Present Address ⊥

A.B.A: Department of Physics, University of Ilorin, Ilorin, Nigeria

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This study was supported by the Fondazione Cassa di Risparmio di Trento e Rovereto through the DAFNE project. A.B.A. acknowledges financial support of the Abdus Salam International Center for Theoretical Physics (ICTP). N.C. acknowledges Marco Pola for his technical assistance.

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REFERENCES

(1) Kelley, T. W.; Baude, P. F.; Gerlach, C.; Ender, D. E.; Muyres, D.; Haase, M. A.; Vogel, D. E.; Theiss, S. D. Recent Progress in Organic Electronics Materials, Devices, and Processes. Chem. Mater. 2004, 16, 4413−4422. (2) Barlow, S. M.; Raval, R. Complex Organic Molecules at Metal Surfaces: Bonding, Organisation and Chirality. Surf. Sci. Rep. 2003, 50, 201−341. (3) Coppedè, N.; Nardi, M.; Toccoli, T.; Tonezzer, M.; Siviero, F.; Micheli, V.; Mayer, A. C.; Iannotta, S. Solid State Dye Sensitized Solar Cells Based on Supersonic Beam Deposition of Organic, Inorganic

10891



Article

Cluster Assembled, and Nanohybrid Materials. J. Renewable Sustainable Energy 2010, 2, 53106−1. (4) Milani, P.; Iannotta, S. Cluster Beam Synthesis of Nanostructured Materials; Springer-Verlag: Berlin and Heidelberg, 1999. (5) Toccoli, T.; van Opbergen, M.; Boschetti, A.; Ciullo, G.; Ronchin, S.; Iannotta, S. Synthesis of High Quality Thin Films of Thiophene Based Oligomers from Supersonic Seeded Beams: Optical and Morphological Characterisation. Philos. Mag. B 1999, 79, 2157− 2166. (6) Nardi, M.; Verucchi, R.; Tubino, R.; Iannotta, S. Activation and Control of Organo-Lanthanide Synthesis by Supersonic Molecular Beams: The Erbium-Porphyrin Test Case. Phys. Rev. B 2009, 79, 125404. (7) Coppedè, N.; Calestani, D.; Villani, M.; Nardi, M.; Lazzarini, L.; Zappettini, A.; Iannotta, S. Directionally Selective Sensitization of ZnO Nanorods by TiOPc: A Novel Approach to Functionalized Nanosystems. J. Phys. Chem. C 2012, 116, 8223−8229. (8) Sakaguchi, K.; Chikamatsu, M.; Yoshida, Y.; Azumi, R.; Yase, K. Color Control and White Emission of Organic Light-Emitting Device by External Light. Jpn. J. Appl. Phys. 2007, 46, 345−347. (9) Thalluri, G. K. V. V; Spoltore, D.; Piersimoni, F.; Clifford, J. N.; Palomares, E.; Manca, J. V. Study of Interface Properties in CuPc Based Hybrid Inorganic-Organic Solar Cells. Dalton Trans. 2012, 41, 11419−11423. (10) Siviero, F.; Coppedè, N.; Taurino, A. M.; Toccoli, T.; Siciliano, P.; S. Iannotta, S. Hybrid Titania−Zincphthalocyanine Nanostructured Multilayers with Novel Gas Sensing Properties. Sensor Actuat. B-Chem. 2008, 130, 405−410. (11) O’Regan, B.; Grätzel, M. A Low-Cost, High-Efficiency Solar Cell Based on Dye-Sensitized Colloidal TiO2 Films. Nature (London, U.K.) 1991, 353, 737−740. (12) Hashimoto, K.; Irie, H.; Fujishima, A. TiO2 Photocatalysis: A Historical Overview and Future Prospects. Jpn. J. Appl. Phys. 2005, 44, 8269−8285. (13) Sorescu, D. C.; Lee, J.; Al-Saidi, W. A.; Jordan, K. D. CO2 Adsorption on TiO2(110) Rutile: Insight from DFT Calculations and STM Experiments. J. Chem. Phys. 2011, 134, 1−12. (14) Peñ a, J.; Vallet-Regí, M.; San Román, J. TiO2-Polymer Composites for Biomedical Applications. J. Biomed. Mater. Res. 1997, 35, 129−134. (15) Gullu, O.; Turut, A.; Asubay, S. Electrical Characterization of Organic-on-Inorganic Semiconductor Schottky Structures. J. Phys.: Condens. Matter 2008, 20, 045215. (16) Iannotta, S.; Toccoli, T. Supersonic Molecular Beam Growth of Thin Films of Organic Materials: A Novel Approach to Controlling the Structure, Morphology, and Functional Properties. J. Polym. Sci., Part B: Polym. Phys. 2003, 41, 2501−2521. (17) Toccoli, T.; Capone, S.; Guerini, L.; Anderle, M.; Boschetti, A.; Iacob, E.; Micheli, V.; Siciliano, P.; Iannotta, S. Growth of Titanium Dioxide Films by Cluster Supersonic Beams for VOC Sensing Applications. IEEE Sens. J. 2003, 3, 199−205. (18) ToF-SIMS: Materials Analysis by Mass Spectrometry, 2nd ed.; Vickermann, J. C., Briggs, D., Eds.; SurfaceSpectra: Manchester, U.K. and IM Publications: Chichester, U.K., 2013. (19) Surface Analysis-The Principal Techniques, 2nd ed.; Vickerman, J. C., Gilmore, I. S., Eds.; John Wiley & Sons Ltd: Chichester, UK, 2009. (20) Graham, D. J.; Wagner, M. S.; Castner, D. G. Information from Complexity: Challenges of TOF-SIMS Data Interpretation. Appl. Surf. Sci. 2006, 252, 6860−6868. (21) Dell’Anna, R.; Canteri, R.; Coppedè, N.; Iannotta, S.; Bersani, M. The Issue of Pseudoreplication when Applying a Statistical Exploratory Approach to Extract Relevant Features from ToF-SIMS Spectra. Surf. Interface Anal. 2013, 45, 1197−1205. (22) R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2012. Available at http://www.r-project.org (accessed February 18, 2013). (23) Wagner, M. S.; Graham, D. J.; Ratner, B. D.; Castner, D. G. Maximizing Information Obtained from Secondary Ion Mass Spectra

of Organic Thin Films Using Multivariate Analysis. Surf. Sci. 2004, 570, 78−97. (24) Jackson, J. E. A User’s Guide to Principal Component; John Wiley & Sons, Inc: Hoboken, NJ, 2003. (25) Vezzaro, A.; Boschetti, A.; Dell’Anna, R.; Canteri, R.; Dimauro, M.; Ramina, A.; Ferasin, M.; Giulivo, C.; Ruperti, B. Influence of Olive (cv Grignano) Fruit Ripening and Oil Extraction under Different Nitrogen Regimes on Volatile Organic Compound Emissions Studied by PTR-MS Technique. Anal. Bioanal. Chem. 2011, 399, 2571−2582. (26) Monti, F.; Dell’Anna, R.; Sanson, A.; Fasoli, M.; Pezzotti, M.; Zenoni, S. A Multivariate Statistical Analysis Approach to Highlight Molecular Processes in Plant Cell Walls through ATR FT-IR Microspectroscopy: The Role of the α-Expansin PhEXPA1 in Petunia Hybrida. Vib. Spectrosc. 2013, 65, 36−43. (27) Gordon, A. D. Classification, 2nd ed.; Chapman and Hall/CRC; Boca Raton, FL, 1999. (28) Mizuguchi, J.; Rihs, G.; Karfunkel, H. R. Solid-State Spectra of Titanylphthalocyanine as Viewed from Molecular Distortion. J. Phys. Chem. 1995, 99, 16217−16227. (29) Castriota, M.; Cazzanelli, E.; Fasanella, A.; Teeters, D. Electrical Conductivity and Raman Characterization of V2O5 Grown by Sol-Gel Technique Inside Nanoscale Pores. Thin Solid Films 2014, 553, 127− 131. (30) Caruso, T.; Castriota, M.; Policicchio, A.; Fasanella, A.; De Santo, M. P.; Desiderio, G.; Ciuchi, F.; La Rosa, S.; Rudolf, P.; Agostino, R. G.; et al. Thermally Induced Evolution of Sol-Gel Grown WO3 Films on ITO/Glass Substrates. Appl. Surf. Sci. 2014, DOI: 10.1016/j.apsusc.2014.01.154. (31) Coppedè, N.; Toccoli, T.; Pallaoro, A.; Siviero, F.; Walzer, K.; Castriota, M.; Cazzanelli, E.; Iannotta, S. Polymorphism and Phase Control in Titanyl Phthalocyanine Thin Films Grown by Supersonic Molecular Beam Deposition. J. Phys. Chem. A 2007, 111, 12550− 12558. (32) Coppedè, N.; Castriota, M.; Cazzanelli, E.; Forti, S.; Tarabella, G.; Toccoli, T.; Walzer, K.; Iannotta, S. Controlled Polymorphism in Titanyl Phthalocyanine on Mica by Hyperthermal Beams: A MicroRaman Analysis. J. Phys. Chem. C 2010, 114, 7038−7044. (33) Coluccio, M. L.; Gentile, F.; Francardi, M.; Perozziello, G.; Malara, N.; Candeloro, P.; Di Fabrizio, E. Electroless Deposition and Nanolithography Can Control the Formation of Materials at the Nano-Scale for Plasmonic Applications. Sensors 2014, 14, 6056−6083. (34) Cahen, D.; Kahn, A.; Umbach, E. Energetics of Molecular Interfaces. Mater. Today (Oxford, U.K.) 2005, 8, 32−41.

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