# Revealing the molecular structure of copper phosphonate groups anchored inside SBA-15 silica channels: Theoretical and experimental study

Sara El Houbbadi, Magdalena Laskowska, Alain Walcarius, Marek Doskocz, Alexey Maximenko, Zbigniew Olejniczak, Łukasz Laskowski

► To cite this version:

Sara El Houbbadi, Magdalena Laskowska, Alain Walcarius, Marek Doskocz, Alexey Maximenko, et al. Revealing the molecular structure of copper phosphonate groups anchored inside SBA-15 silica channels: Theoretical and experimental study. Applied Surface Science, 2024, 669, pp.160425. 10.1016/j.apsusc.2024.160425. hal-05072301

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Full length article

# Revealing the molecular structure of copper phosphonate groups anchored inside SBA-15 silica channels: Theoretical and experimental study

Sara El Houbbadi<sup>a,b</sup>, Magdalena Laskowska<sup>a,*</sup>, Alain Walcarius<sup>b</sup>, Marek Doskocz<sup>a</sup>, Alexey Maximenko<sup>c</sup>, Zbigniew Olejniczak<sup>a</sup>, Łukasz Laskowski<sup>a,*</sup>

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

### Keywords:

SBA-15 functional mesoporous silica

Copper phosphonate

X-ray absorption spectroscopy (XAS)

K-edge of copper

Electronic and molecular structure

Machine learning techniques to analyze
experimental XANES data

## ABSTRACT

This study aims to thoroughly understand the local structure of copper phosphonate groups covalently attached via propyl arms to pores interior of SBA-15 mesoporous silica. The studied sample contains 20% of propyl-POO₂Cu groups attached to the mesoporous silica matrix as host material. X-ray absorption near-edge spectroscopy (XANES) and extended X-ray absorption fine structure (EXAFS) at the copper K-edge were utilized to discern the valence state and geometric arrangement of copper sites within the pores. Various machine learning techniques, including principal component analysis, K-means clustering, and neural networks, were applied to theoretically calculated XANES spectra. This facilitated our quantitative analysis of the experimental spectrum and the extension of the EXAFS equation fit to the second shell. Additional molecular structure analysis was performed based on Raman scattering supported by numerical simulations. Nuclear magnetic resonance aided in analyzing the silicon and phosphorus environment. Results indicate that copper atoms are coordinated by two phosphonate groups, forming a pseudo-bi-trigonal pyramid with surrounding oxygen atoms. These results contribute to a better understanding of the physical properties of the studied nanocomposite material. This, in turn, makes it possible to optimize the molecular structure of materials to optimize their antimicrobial and photonic properties, making them useful in practice.

## 1. Introduction

The growing human demand for new devices that exhibit special features has created an urgent need to develop new materials, depending on the targeted application, sometimes requiring breakthroughs to see the emergence of new technologies [1]. Creating materials with several different functionalities can be achieved thanks to developments in nanotechnology and molecular engineering [2], enabling the same specimen to be used in various fields simultaneously. For example, we realized from our own experience that looking for a material that could be used primarily in optoelectronics also exhibited strong biocidal properties. What is more, these properties will result from its catalytic properties, and it can be used additionally as a single-atom catalyst (SAC). All of this will be described below after presenting how the material can be designed.

The process of designing functional nanocomposites often follows a predefined scheme, involving the selection of appropriate functional groups and matrices, and further developing a method for anchoring functional groups in the matrix in the intended manner [3]. The method

of anchoring functional molecules in the matrix is crucial, as it determines their mutual interactions and possible changes in their molecular structure [4].

For the reason described above, we considered a composite material consisting of an inert matrix and functional groups distributed in it properly. A promising matrix, in our opinion, was silica of the SBA-15 type [5–7]. The most important advantage of SBA-15 silica for us is that it can be functionalized in many ways [8], while keeping the functional molecules accessible to external reagents. One promising group appears to be propyl phosphonate, which can bind numerous divalent metal ions to form functional nanocomposites [9–11]. Moreover, by applying direct synthesis methods (co-condensation) [12], such propyl phosphonate units can be controlled as far as their distribution is concerned. By capturing a copper atom, such a functional group becomes strongly polar [13] and, together with the silica matrix, forms a system that interacts strongly with the incident light, responding with the generation of the second and third harmonic components of light [14,15]. Such a material, showed in Fig. 1, having nonlinear optical (NLO)

<footnote><sup>a</sup> Institute of Nuclear Physics Polish Academy of Sciences, Krakow, 31-342, Poland</footnote>

<footnote><sup>b</sup> Université de Lorraine, CNRS, LCPME, Nancy, F-54000, France</footnote>

<footnote><sup>c</sup> SOLARIS National Synchrotron Radiation Centre, Jagiellonian University, Krakow, 30-392, Malopolskie, Poland</footnote>

<footnote>* Corresponding authors.</footnote>

<footnote>E-mail addresses: magdalena.laskowska@ifj.edu.pl (M. Laskowska), lukasz.laskowski@ifj.edu.pl (Ł. Laskowski).</footnote>
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** The schematic representation of the general structure of investigated material: SBA-15 mesoporous silica containing copper ions bonded inside pores by the propyl phosphonate units.</figcaption></figure>

properties, can find numerous applications, mainly in optoelectronics and photonics [14]. It has a form of amorphous silica with long, 2D hexagonally arranged mesochannels (about 5 nm in diameter), with functional units placed inside. The optical properties of such material should be determined by the geometry and configuration of internal functional units, and the generation of the third- and second-order optical response is expected to depend strongly on the coordination of copper ions inside silica pores. Such properties can be applied to the tuning of the NLO response, making the material unique and highly desirable in practical application.

Looking at the structure of the material discussed here, an association with Single Atom Catalysts immediately comes to mind [16,17], being one of the most active new frontiers in heterogeneous catalysis. Such well-separated copper ions can promote electrochemical CO₂ reduction, CO₂ cycloadditions, methane oxidation to methanol [18], or, most importantly, oxygen reduction [19]. This latter reaction, in the presence of air, can lead to the formation of H₂O₂ molecules, which will accumulate inside the pores of silica. Such material will have a strong biocidal effect in contact with pathogenic microorganisms [20].

Theoretically, therefore, we have a multifunctional material — applicable in optoelectronics, medicine, and catalysis. However, for it to be truly effective in these applications, its structure should be optimized, requiring a thorough understanding of the exact configuration of functional groups and the relationships between their structures and mutual interactions based on their distribution (directly resulting from their concentration).

Such a material has already been prepared and preliminarily examined, confirming its assumed properties [15,21]. However, the investigated properties were found to strongly depend on the concentration of functional groups and in a non-monotonic manner. The effect of the concentration of functional groups, especially with the highest possible one, on their configuration has not yet been fully clarified, which is a key issue in optimizing the structure for efficiency in a specific application. Based on the synthesis route, we can only predict the assumed structure of functional units, and these predictions do not necessarily reflect reality. Due to the unique synthesis procedure, reference materials are not available for comparison. Moreover, we found no studies in the literature that could provide information on this topic. For this reason, the present study aims to fill this scientific niche and reveal the detailed molecular structure of the internal copper-containing units in the SBA-15 silica. Thanks to access to synchrotron techniques and artificial intelligence algorithms, such an analysis is finally possible, as we describe in this publication, on the basis of an SBA-15 material containing the highest content of functional units compatible with keeping its ordered mesostructure (i.e., 20% of silicon bonded to phosphonate units). This seems to be the most interesting composition to investigate into the local structure of the metal ion inside the SBA matrix due to possible intermolecular interactions.

## 2. Materials and methods

The material under consideration within this work's framework is SBA-15 mesoporous silica containing copper ions coordinated inside

pores by propyl phosphonic units (see: Fig. 1). The content of phosphonic molecules is 20% in molar proportions toward silica groups (one propyl phosphonic unit per five silica molecules). The schema of a single functional unit coordinating copper atom can be seen in Fig. 1, at the right side. Here, we must remark that the figure is very schematic and gives only a general view of the copper-containing molecules. The exact configuration and geometry of such molecules will be presented further. In this place, we present only a starting point for our consideration.

### 2.1. Material's synthesis

The material was prepared in a powder-like form. Synthesis was carried out in the protective atmosphere of argon.

The overall diagram of the synthesis can be seen in Fig. 2.

In the first stage, 14.97 g of tetraethyl orthosilicate (TEOS – 71.84 mmoles) and 6.10 g of Phosphonate propyl diethyltriethoxysilane (PPTES – 17.96 mmoles) were added to the solution of 8 g of Pluronic P123 surfactant (EO – 20PO₇₀EO₂₀, where EO is poly-ethylene oxide and PO is polypropylene oxide) in 360 ml of aqueous HCl solution (pH=1.5). After stirring for two hours, 151 mg of NaF was added to this mixture to induce hydrolysis and polycondensation. The materials were then immediately heated to 60° C in a hot oil bath and stirred at this temperature for 48 h under a reflux condenser. The resulting materials were filtered and washed with ethanol. The surfactant was removed by extraction with hot ethanol in a Soxhlet apparatus for eight hours. The materials obtained contained groups of the ester of phosphonic acid units (see Fig. 2).

In order to carry out a silylation procedure (to protect the phosphonic acid groups from the side reactions during hydrolysis [22]), the resulting powder was treated with a solution of 1.2 g of chlorotrimethylsilane (CITMS) in 50 ml of toluene for each gram of silica material under a reflux condenser for 12 h, then washed with toluene and dried.

In order to convert ester units into phosphonic acid groups, selective two-step hydrolysis with the use of bromotrimethylsilane (BrTMS) was applied [23]. This was achieved by mixing the samples with a solution of BrTMS in toluene (two equivalent parts of BrTMS per molar number of functional units: 0.77 g of BrTMS and 50 ml of toluene per one gram of silica powder) under a reflux condenser for 24 h. The powder was then washed with toluene, dried, and placed under a reflux condenser again, this time with methanol. After 5 h, the resulting powder was filtered and dried. After this procedure, the sample contained phosphonic acid groups anchored inside the pores of the silica with propyl chains (SBA-PO(OH)₂).

To functionalize SBA-PO(OH)₂ with copper ions, 0.85 g Cu(acac)₂ was added to each gram of silica powder (slightly more than the necessary 2.5 mmoles) and placed the mixture under a reflux condenser overnight with tetrahydrofuran (about 50 ml for each gram). The resulting solid (SBA-POO₂Cu4), where the number 4 is the number of TEOS molecules per single PPTES molecule) was recovered quantitatively by filtration and washed several times with THF to remove excess copper salts, and then dried under vacuum in 100°C.
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** The schematic presentation of the synthesis route of the scientific material: SBA-15 silica containing copper bounded inside channels by phosphonate units at a concentration of 20% of a molar. Nomenclature: TEOS — tetraethyl orthosilicate, PPTES — phosphonate propyl diethyltriethoxysilane. The SBA-15 means the SBA-15 silica structure (Santa Barbara Amorphous.) Sample names used in the following text are highlighted in red under the material diagrams.</figcaption></figure>

In addition to the target material, a series of reference materials necessary for proper Raman and Nuclear Magnetic Resonance results analysis were taken in various stages of synthesis (see: captions for synthesis steps in Fig. 2). Detailed descriptions of all the reference samples can be found in Supplementary Information† (Section 1).

## 2.2. Experimental methods and procedures

Synchrotron radiation-based methods like X-ray absorption near edge spectroscopy (XANES) and X-ray absorption fine structure (EXAFS) at the K-edge can bring valuable information about the valence state of the copper centers in functional units placed inside silica channels and their geometrical environment.

XAFS spectra at Cu K-edge were collected at the SOLARIS National Synchrotron Radiation Center (Poland) at ASTRA beamline, which operates in the energy range from 1 to 15 keV. For the measurements, the incident photon beam was monochromatized by Ge(220) Lemonnier type double crystal monochromator. A copper metal foil was used for energy calibration in a way that the maximum of the first derivative of the absorption spectrum was made at the energy 8979 eV. The measurements were made in transmission mode with ionization chambers and a sample chamber filled with argon gas at pressure 52 Torr. The sample of interest was prepared by pressing the powder sample inside a rectangular sample holder folded between a Kapton tape, which gave a 300 µm of the sample's thickness. The reference powder samples were ground with mortar and pestle and spread on the Kapton tape. All spectra were collected at room temperature.

The sample of interest (SBA-POO₂Cu4) and copper-containing references (copper foil, CuO, Cu₂O) were measured in the above-described conditions. The reference samples were used for rough estimating of the copper oxidation state and to get more insights about the coordination geometry of the copper ions in the sample of interest.

Athena software was used to process the raw data (calibration, normalization, background subtraction) and to extract the EXAFS signal that will be analyzed by Artemis software [24].

The recorded EXAFS spectra were processed as follows:

1. the spectra were normalized by subtracting a linear pre-edge and post-edge background,

2. the $\chi(k)$ function was extracted from the raw data by subtracting the atomic background using piece-wise splines to approximate a smooth background function,

3. data were Fourier transformed (FT) to isolate individual frequencies in the $\chi(k)$ spectra ($k$ range for FT was (3 – 11.5 Å⁻¹)).

Raman spectra were recorded using a WiTec confocal Raman microscope (CRM alpha 300) equipped with an air-cooled solid-state laser ($\lambda$ = 532 nm) and a CCD camera. An air Olympus MPlan (100×/0.90 NA) lens was used. Raman scattered light was focused onto a multimode fiber and a monochromator with a 600 line per mm grating. The Raman spectra were accumulated by ten scans with an integration time equal to 5 s and 3 cm⁻¹ resolution. The spectra were collected at room temperature for the wavelength range of 100–4100 cm⁻¹. The baseline correction, cosmic ray removal, and peak fitting analysis were performed using the WiTec Project software package.

The solid-state, high-resolution magic-angle spinning nuclear magnetic resonance spectroscopy (MAS-NMR) was used as an additional technique to study the coordination of silicon and phosphorus atoms. The MAS-NMR spectra were measured using an APOLLO console (Tecmag) and a 7 T/89 mm superconducting magnet (Magnex). A high-speed MAS Bruker HP-WB probe equipped with a 4 mm zirconia rotor and a Kel-F cap was used to spin the sample. The deconvolution of the MAS-NMR spectra was carried out using an automated procedure, without making any a priori assumptions about the positions of the peaks
or their widths. The minimum number of components that yielded satisfactory residuals was selected.

The $^{29}$Si MAS-NMR spectra were measured at a resonance frequency of 59.515 MHz, rotating the sample at 6 kHz. A single 3 µs rf pulse was used, corresponding to a flip angle of π/2. The acquisition delay in accumulation was 30 s, and the number of scans ranged from 1500 to 4000 to achieve a sufficiently high signal-to-noise ratio. The $^{29}$Si spectra were referenced to the $^{29}$Si frequency of tetramethylsilane (TMS) defined as 0 ppm.

The $^{31}$P MAS-NMR spectra were measured at the 121.264 MHz resonance frequency, spinning the sample at 8 kHz. The acquisition delay in accumulation was 5 s, and 256 scans were acquired. The ppm frequency scale was referenced to the $^{31}$P resonance of 85% $H_3PO_4$.

## 2.3. Numerical models and simulation methods

To support the experimental results and ensure their correct interpretation, we have prepared numerical models of the structures we expect inside the silica pores.

Initially, we designed many possible configurations of the copper-containing functional units, which were used as starting points to optimize geometry. These structures were created when optimizing the systems by freezing one of the parameters: the distance between the copper atom and phosphorus (molecules series named Axx), the dihedral angle between four oxygen atoms (molecules series named ARxx and AAxx), or the distance between the copper atom and oxygen phosphorus P=O (molecules series named Bxx).

Geometry optimization and vibrational spectra calculations of model molecules were carried out with Gaussian G16 Revision C.01 program package [25] using the Becke's three-parameter (B3) hybrid functional [26] with Lee-Yang-Parr (LYP) [27] correlation functional [28] with the standard def2-QZVPP basis set [29]. Considering the coherence of further interpretation, this choice seems to be justified.

After geometry optimization, we obtained sixty-five possible arrangements of molecules in which local coordination of the copper atom could be trigonal planar, T-shaped, tetrahedral, square planar, seesaw, trigonal bipyramid, or octahedral.

After the geometry optimization, IR and Raman vibrational spectra calculations were performed for all the systems. We used the same method and basis set as in the case of geometry optimization. By using relaxation optimization with one parameter frozen, we can assume that a given point is closest to the energy minimum on the multidimensional potential energy curve [30].

We used the same optimized numerical models of expected functional unit structures to generate theoretically calculated X-ray absorption spectra. The real-space Green's function (RSGF) approach [31] was used as one of the most sophisticated theoretical tools to ensure that the analysis and interpretation of experimental data are reliable and the correspondence between theory and experiment is satisfactory.

FEFF10 code was implemented for ab initio multiple scattering calculations of XANES spectra [32,33]. Using optimized models of the assumed molecules, we generated input files for the FEFF10 with the program WebATOMS [24,34]. As input parameters in WebAtoms for the optimized structures, a cubic crystal cell above 13 Å was used. The large crystal cell allows us to avoid problems of spatial arrangement and interactions between atoms from different cells (we must emphasize that we treat our functional units as separated structures).

The calculation parameters in FEFF10, such as the choice of radii and software parameters, were tailored to the system under study and the desired level of accuracy [35-37]. Spectra were calculated in the framework of the full multiple-scattering approaches using the MT radii(muffin-tin radius) of 0.2 and 0.9 Å for hydrogen and oxygen, respectively [38]. A self-consistent-field (SCF) potential with the Hedin-Lundqvist exchange-correlation function and the final-state rule screening were used for the core-hole potential calculation. A spherical radius was chosen to be 6, and the corrections card has been used

to adjust the energy edge shift (the real energy shift comes from the inaccurate estimation of Fermi level in FEFF10) and to add some broadening to the result of simulation to correct the experimental resolution.

The obtained numerical models were also used for the calculation of NMR chemical shift as well, using B3LYP with basis set IGLO III for atoms H, C, O, P, and Si and basis set def2-QZVPP for Cu, with the Gauge-Independent Atomic Orbital (GIAO) method [39]. Additionally, we applied a few additional models for the clarification of some phenomena occurring in NMR spectra. Additional models can be found in Supplementary Information† (Section 4).

The structural analysis of the output models was carried out to determine the environment of the copper atom. We applied complete-linkage clustering [40] for dividing the obtained structures into groups of molecules representing the environment surrounding the copper atom. The analysis was based on a matrix of distances between oxides that are close to the copper atom in the model molecule.

Principal component analysis (PCA) was used to find the position of the experimental XANES spectrum relative to the theoretical spectra. The training set was all theoretical spectra, and the test set was the experimental spectrum. The region analyzed for the theoretical spectra was from 8968 to 9067 eV. The shift of the theoretical spectra to the experimental spectrum was from -10 to 40 eV in 0.1 eV increments. The closest theoretical spectrum to the experimental spectrum was found by calculating the Euclidean distance in the PC1-PC2-PC3 and PC1 to PC7 projections.

Various Machine Learning was performed on a personal PC: CPU i5-4430, RAM 16G, GPU RTX 3060.

Sixty-five base spectra were used to randomly generate 60,000 spectra consisting of two spectra in different proportions. Spectrum descriptors can be used to reduce the size of the collection [41]. We, however, used a simpler method to lower dimensionality based on PCA to speed up calculations. The set obtained in this way was divided into a teaching set (70%) and a test set (30%). The trained models did not turn out to be very good at predicting the composition of the experimental spectrum. The correlation coefficient between the experimental spectrum and the obtained Pearson correlation coefficient was below 0.96. Based on this analysis, 5 base spectra were selected, from which another 60000 spectra were generated. The calculation of the model was carried out with the given parameters: dense: 2, optimizer: adam, loss function: mean squared error, batch size equals 64. We used the early stop method, which allowed us to find the model after calculating 28 epochs.

We used experimental and theoretical data to prepare a model that ascribes the Raman signal to a specific oscillation. First, we performed web scraping of available spectra in related scientific publications, as well as information about the assigned signals. The spectra were processed with the Webplotdigitizer program [42]. We analyzed the publication text manually due to the elimination of errors from natural language processing (NLP). We analyzed 19 spectra with graphically described signals. The spectra were decomposed into individual signals that were assigned to specific oscillations in the scientific papers. For specific individual peaks, we applied fitting via Voigt distribution functions (the Voigt profile is a probability distribution given by a convolution of a Cauchy-Lorentz distribution and a Gaussian distribution). In this way, we were able to account for small changes in signal shifts from interactions, measurement errors, or apparatus differences. We did not analyze signals that were present in the spectra and were omitted from the description. We obtained 85 signals. To this, we added signals obtained by molecular modeling scaled by a calculated factor of 0.9462, analogous to similar functional bases [43]. The obtained information is in the form of a matrix in which Y is a description of the signal, and X is a synthetic spectrum consisting of one signal in the space corresponding to the frequency from 0 to 4150 cm⁻¹.

All programming associated with data analysis: PCA, machine learning, clustering, fitting, smoothing, normalization, structure analysis,
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** The stable structures of (CH<sub>3</sub>-PO(OH)O)<sub>2</sub>Cu (a). Distance between oxygen and copper are 1.89 and 2.07 Å. Another view of the A01 structure with marked dihedral angle τ O-O-O-O (b). The functional unit with the fragment of SBA-15 silica (c).</figcaption></figure>

and plot visualization was performed by Python version 3.11.2 and standard scientific packages such as Pandas, SciPy, NumPy, Pylab, Statsmodels, Matplotlib, Keras, TensorFlow, Ray, Modin and Jupyter-Lab [44–47].

# 3. Results and discussion

## 3.1. Numerical simulations

### 3.1.1. Design of the molecular model

The copper ions in the investigated material are located inside the silica pores (see Fig. 1) and are assumed to be ionically bound via phosphonium propyl groups. The pore diameter (about 7 nm) is about 100 times larger compared to the length of the –(CH<sub>2</sub>)<sub>3</sub>–PO(OH)OCu chain. We can say that copper ion is bonded to the uneven silica surface on the atomic scale. The local coordination of the copper can be linear, pseudo-tetrahedral, square planar, pseudo-octahedral [41], or even adopt compact five-coordinate square-pyramidal geometry, which is confirmed by XANES and EXAFS measurements [48]. The geometrical properties of the considered molecular system take from the electronic transitions of the 3d electrons of the ion Cu<sup>2+</sup>. The copper ion can directly interact with oxygen atoms localized at the phosphonate groups: O<sup>-</sup> ion, the atom of =O, oxygen atoms at the silica part: Si-O-Si, Si-O-H, or oxygen atoms from the water or air [21].

The considered system is very complicated on the atomic scale. For this reason, we assumed some simplifications. The starting point for considering the copper-containing functional units inside SBA-15 pores was a structure containing two phosphonate units with a single copper atom, as seen in Fig. 3a. In this case, the local coordination of the copper atom is square planar. Distances between copper and oxygen are 1.89 and 2.07 Å. The angles O-Cu-O equals 106° and 74°.

Such a structure has a limited modification possibility, however. For this reason, we added two water molecules to create an octahedral structure. After the geometrical optimization, hydrogen bonds O–H···O proved so stable that the structure evolved to the planar square configuration, as it can be seen in Fig. 3b (structure named A01). In this case, the distances between copper and neighboring oxygen atoms were 1.92 Å and 1.98 Å, while angles O-Cu-O were 88° and 92°.

The propyl phosphonate units anchored inside silica pores can be placed at various distances from each other and have various spatial configurations. To simulate such a possibility, we carried out numerical simulations for 23 points, in which we incremented the distance between copper and phosphorus from 3.13 Å to 5.72 Å, and the systems were optimized. The energy plot (Fig. 4) shows the changes in the total energy and coupled changes in systems geometry. As can be seen,

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** B3LYP/def2qzvpp relaxed scan energy curves varying changing distance between one copper and phosphorus atoms. ΔE values (in kcal/mol) with respect to the lowest minimum energy structure (A01) are plotted.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** B3LYP/def2qzvpp relaxed scan energy curves varying torsion angle, τ see Fig. 3b. ΔE values (in kcal/mol) with respect to the lowest minimum energy structure (A01) are plotted.</figcaption></figure>

the planar layout was reorganized into the configuration, being an intermediate system between the square planar and trigonal planar. In system A08 we obtained the following parameters: the O–Cu distances are 1.83 Å, 1.85 Å, and 1.94 Å respectively, while O–Cu–O angles were 151°, 101°, and 108°. The energy increase is significant, but nevertheless small enough that the type of copper environment can occur in nature if the entire system is stabilized in addition to other interactions. Here, we deal with a system, in which the energy of the interactions between ions is significantly larger than in the classical hydrogen bond in water (about 7 kcal/mol).

We have also analyzed the possibility of forming a tetrahedral copper environment. The initial structure for this task was the A01 model (see: Fig. 3b), which presents the square planar configuration of oxygen atoms. Four oxygen atoms create the dihedral angle, such that the copper ion is in the center of the angle (copper is not taken into account in the dihedral angle). Starting from this arrangement, we carried out a scan, changing the value of the dihedral angle with 15° in both directions. This modification has led to numerous significant stable configurations from square planar to pseudo-tetrahedral, pseudo-trigonal, or pseudo-linear. The changes in total energy were half the size of the previous theoretical experiment, as seen in Fig. 5.

As a subsequent numerical experiment, we attempt to the creation of an octahedral configuration. To this end, we expanded the model of A01 with two additional water molecules. For such a pseudo-octahedral layout, we investigated modifications of the copper environment during the changes in the distance between oxygen and phosphorus in the O=P group. Energy changes were not large and corresponded to the formation or breaking of hydrogen bonds (see: Fig. 6). We also prepared the structure, including a silica fragment, necessary during the interpretation of the Raman spectra (described in a further Section 3.2), which can be seen in Fig. 3c.
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** B3LYP/def2-qzvpprelaxed scan energy curves varying distance between oxygen from P=O group and copper atom. ΔE values (in kcal/mol) with respect to the lowest minimum energy structure (BRO2) are plotted.</figcaption></figure>

### 3.1.2. Relationship between model and copper local coordination environment

The structures obtained possess a variety of local coordination environments of the copper. The classical division into a linear, trigonal planner, T-shaped, tetrahedral, square planar, seesaw, trigonal bipyramidal, and octahedral (see: Fig. 7b) can be challenging to observe since the distances and angles do not correspond perfectly to the model. After the visual inspection, we are convinced that such structures are pseudo- and transition types.

We applied the following procedure to classify the structures concerning the coordination environment of copper ions. We centered each molecule relative to a copper atom. We then calculated the distance of all oxygen atoms from the copper atom (see: Supplementary Information†, Section 2). Analysis of the distance distribution from the copper atom showed unambiguously that the oxygen atoms determining the environment of copper are at a distance of 2 Å. The distance of an oxygen atom from a copper atom alone does not determine the copper's environment. To avoid building models based on coordinate tensors of atoms and the problem of normalization and alignment of the molecule in space, we calculated the distance matrix of the nearest oxygen atoms to each other. In order to simulate the minor modifications in atoms' positions, we superimposed the Gaussian function to the atoms' spatial position. The obtained spectrograph can be seen in Fig. 7a. Unfortunately, we cannot use it for the ideal model, reflecting the distances between atoms in various systems (Fig. 7b). Even though the model was built mathematically and may differ in Cu–O distances from the real ones, superimposing this model would be possible only when the number of signals and the ratio of these signals to each other are preserved. In our case, however, the shape of the spectrograph excludes the existence of a pure model; rather, we are dealing with a superposition of many different models. B-labeled molecules have been excluded from the presentation of results to make the presentation of plots more transparent. The B-labeled models were identified as pseudo square, pseudo trigonal bipyramid, and pseudo octahedral. Also, peak intensities support excluding them from the cluster analysis.

We used cluster analysis with data visualization through a correlation matrix and a dendrogram (Fig. 8) to divide the structures into groups characterized by similar local environments of copper atoms.

With this procedure, we obtained groups that we assigned visually to ligand arrangements. These arrangements do not correspond to pure positions of atoms in space but are deformed. The groups are as follows:

* pseudo t-shape: group 1 (models: A15, A14, A16, A18, A13, and A17), and group 2 (models: A12, A21, A19, and A20);

* pseudo t-shape-triangle group 1 (models: A07, A10, AA11, AR10, A05, A06, AA10, A22, A11, and A23), and group 2 (models: AA12, AA14, AA13, AR13, AR15, AR12, AR14, AR11, AA15, A08, A09);

* pseudo seesaw (models: AA05, AR05, AA07, AR07, AA06, and AR06);

<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Spectrogram of distance distributions in between oxygen atoms (O–O), located in the nearest sphere of the copper atom (a). Model of potential shapes of the copper environment (b). Gaussian functions were applied to each distance value.</figcaption></figure>

* pseudo tetragonal (models: AA09, AR09, AA08, and AR08);

* pseudo square group 1 (models: A03, AA04, AR04, A02, AA03, AR03, A01, AA02, and AR02), group 2 (model A04), and group 3 (models: BRO2 and BR03);
<figure><img src="image_11.png" /><figcaption>**Fig. 8.** Headmap of the correlation matrix along with dendrogram of the division of structures due to surroundings. The correlation matrix from the distance between oxygen atoms in the closest surroundings of copper was used for the analysis. The full-scale picture can be found in Supplementary Information†, Figure S8.</figcaption></figure>

* pseudo trigonal bipyramid (models: BA05, BA06, BA07, BA08, BA09, BA10, BA11);

* pseudo octahedral (models: BA01, BA02, BA03, and BA04);

Such a grouping of structures also corresponds to the energy dia-
grams of molecules, seen in Figs. 4, 5, 6.

## 3.2. RAMAN and NMR analysis — confirmation of the correctness of the synthesis

Raman spectroscopy allowed us to verify that the synthesis resulted
in the intended material. To describe Raman signals automatically,
we developed a narrow AI method using supervised learning. De-
tailed information describing the method we used can be found in
Supplementary Information† (Section 3).

The juxtaposition of the whole range of the Raman spectra of
the investigated sample and all the reference materials (described in
Supplementary Information† — Section 1) is shown in Fig. 9.

The signals in the range 400–1200 cm⁻¹ comes from -O-Si systems,
and are typical for SBA-15 silica [49,50].

Other vibrations confirming the assumed structure of the nanocom-
posite are related to the propyl groups and their connection to the
phosphonate group. These are vibrations of CH₃ groups symmetric
(2885 cm⁻¹), asymmetric (2963 cm⁻¹), and scissors (1382 cm⁻¹) [51–
55]. P-C bond stretching vibrations can be observed at about 704 cm⁻¹
[15,52,55].

Evidence of copper coordinated by phosphonate groups can be seen
in the signal at 912 cm⁻¹ (position based on numerical simulations).
The fine signals in the 0 to 1300 cm⁻¹ range come from complex vi-
brations inside the system with copper, which overlaps with vibrations
of the silica systems and vibrations of the organic part. The probability
of observing vibrations associated with the interaction of the copper
atom with the surroundings on experimental spectra is negligible due
to the low intensity of these peaks.

The results of the Raman experiment are consistent with the analysis
of MAS NMR spectra and prove that the synthesis was carried out
correctly.

The ³¹P solid-state NMR spectra for the samples from each synthesis step (Fig. 10) show results that are in full agreement with the expectations [56]. The spectrum for the SBA-PO(OEt)₂ (before silylation) sample consists of one large signal at 32.86 ppm. This peak is slightly broadened, which may be due to slight hydration of the phosphonium group or interaction with the hydroxyl group from the Si-OH [57]. The signal at -12.66 ppm is the signal from contamination of the sample by the resulting P-O-Si system (concentration less than 0.3%) [57].

For the SBA-PO(OEt)₂ spectra after silylation, a major peak of
30.6 ppm is shifted, and a small broad peak of 20.3 appears. This is
due to the change in the environment of the phosphorus atom after
blocking the Si-OH acid groups and the formation of hydrogen bonds
with water molecules P=O···H-O-H.

The spectrum for SBA-PO(OH)₂ consists of two distinct signals
next to each other, which is due to the two types of phosphorus
environment. The phosphonic acid group can be either an acceptor or
a donor in the hydrogen bond. The signal at δ = 22.46 ppm comes
from ³¹P nuclei more screened and corresponds to phosphonic acid
groups as hydrogen-bond acceptors. The signal at δ = 31.92 ppm can
be attributed to free phosphonic acid groups and/or to phosphonic acid
group hydrogen-bond donors.

Analyzing the spectrum of SBA-POO₂Cu4, we deal with the re-
duction in the donor signal and a significant broadening of the total
signal. The interaction of Cu and P nuclei leads to the splitting of the
signal into four lines — not necessarily symmetrical. In addition to
this, we should take into consideration that hydrogen bonds from water
molecules cause changes in the local environment of the phosphonium.
All these effects cause the signal of phosphorus atoms to become broad.

Analyzing ²⁹Si MAS NMR spectrum for the SBA-15 sample (Fig. 11), we observe the classical shape of the SBA-15 type silica spectrum [58]. The two peaks labeled Q4 (–110.75 ppm) and Q3 (–102.35 ppm) correspond to silicon atoms in the lattice: (Si(OSi)₄) and with the hydroxyl group Q3 (Si(OH)(OSi)₃).

For the SBA-PO(OEt)₂ non-sil sample, we can clearly observe a
signal from Q4 structures at –110.67 ppm, and a signal originating
from Q3 at –101.76 ppm. In addition, next to this peak, one can note
a small signal coming from Q3 groups in which Si-OH participates in
hydrogen bonds with P=O. The introduction of a propylphosphonium
group changes the shape of the main signals. The uneven signal be-
tween –60 and -66 ppm corresponds to the silicon atom that anchors
the Si-CH₂-CH₂-CH₂-PO₃Et₂ diethyl phosphonate group. In addition,
there is a separation of the signal into two peaks because the coupling
constant ²J<sub>P→O→Si</sub> is about 15 Hz [59,60].

In the SBA-PO(OEt)₂ sample after silylation, a signal corresponding
to the silane group appears. The shape of the Q3 and Q4 signals changes
due to the change in pH, the nature of the hydrogen bonds, and thus
the surrounding of the silicon atom.

Looking at the spectrum of the SBA-PO(OH)₂ sample (Fig. 11), it can
be clearly seen that the main signal consists of two signals (similarly
to the previous materials): the first one coming from Q4 (Si(OSi)₄)
around -110 ppm and second originating from the silylated hydroxyl
units (CH₃)₃Si-O-Si(OSi)₃. Additionally, we observe a signal broadening
associated with a change in the silicon environment by forming a
hydrogen bond between the proton of the phosphonium group and the
oxygen atom of Si-O-Si.

After the functionalization of the sample by copper, ²⁹Si MAS NMR signal shape changed significantly. This change could indicate a reduction in the proportion of hydrogen bonds involving silica. The presence of the Cu²⁺ ion caused a change in the shift of signals from silica. The signal from silicon (Si-(CH₂)₃-PO₃Cu) also changed its shape. The decrease in Q3 in the sample could be due to interactions between Cu(II) and residual Si-OH groups, which can remain in the sample even after silylation or form during the sample's contact with atmospheric conditions. What is more, presumably, the propylene group is even less labile and occurs in different positions, which affects the different
<figure><img src="image_12.png" /><figcaption>**Fig. 9.** The juxtaposition of the experimental Raman spectra for SBA-15 based samples. Characteristic bands are marked as green for organic bands, blue for water, OH, N₂. Descriptions and names of reference materials, along with detailed descriptions of the assignment, can be found in Supplementary Information† (Sections 1 and 3).</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>**Fig. 10.** ³¹P solid-state NMR spectra of the SBA-PO(OEt)₂, SBA-PO(OH)₂ and SBA-POO₂Cu4. Ref to 80% H₃PO₄, * - denotes spinning sidebands.</figcaption></figure>

shielding of the silicon atomic nucleus. Therefore, the signal becomes more broad.

A much more detailed analysis of Raman and NMR spectroscopy can be found in Supplementary Information† (Sections 3 and 4). Both studies confirmed that the investigated material had been prepared correctly.

### 3.3. X-ray absorption

#### 3.3.1. Relationship between experimental XANES spectrum and theoretical XANES spectra

By comparing simulated XANES spectra with the experimental spectrum, we can analyze the copper ion environment in detail. We simulated the XANES spectra for the obtained numerical models of possible

<figure><img src="image_14.png" /><figcaption>**Fig. 11.** ²⁹Si solid-state NMR spectra of the SBA calcination, SBA, SBA-PO(OEt)₂ no sil, SBA-PO(OH)₂ and SBA-POO₂Cu4. Ref to TMS.</figcaption></figure>

structures. The position of the experimental and theoretical spectra on the OX axis may be a little offset from each other. This shift corresponds to a change in the overall intensity of the experimental and theoretical electromagnetic field (FEFF computes the photo-absorption cross-sections of molecules and atom clusters for X-ray photons through the RSGF formalism) or may result from imposed errors from calculations and simulations of the structure [35].

To find the shift factor by which to shift the theoretical spectra relative to the experimental ones, we used principal component analysis (PCA). Details of the method can be found in Supplementary
<figure><img src="image_15.png" /><figcaption>**Fig. 12.** XANES stacked plotting spectra. A juxtaposition of XANES spectra for the molecules forming the PCA model and the experimental spectrum shifted by 2.3 eV relative to the measurement. This is the best-found position of the experimental spectrum relative to the theoretical model spectrum.</figcaption></figure>

Information† (Section 5). In the first step, we built a model consisting of theoretical spectra (65 spectra) in the range 8968.0 eV to 9067.0 eV (see: Fig. 12).

We normalized the spectrum so that the mean is 0 for each value, and the standard deviation is 1. We then performed PCA calculations. Cumulative explained variances for ten projections were 75.45, 91.72, 95.11, 96.69, 97.95, 98.56, 99.03, 99.29, 99.50, 99.66%. The first three projections describe samples at 95%, and up to PC7, they describe up to 99%. Analyzing the input spectra on the first three principal components (Fig. 13), it is clear that the most important is the position and shape of the spectrum. Additional essential regions can be seen in subsequent loading spectra, e.g., pre-edge peaks (see: Supplementary Information†, Section 6).

We shifted the experimental spectrum relative to the model in the range of -10 eV to 40 eV in 0.1 eV increments. Each time, we calculated the position of the point in the first three dimensions (PC1 to PC3) and in seven dimensions (PC1 to PC7), as can be seen in Figs. 14a and 14b. In addition, we calculated the Euclidean distance to the closest point (Fig. 14c), representing the given spectrum. The assumption was to find the spectrum closest to the experimental spectrum. An unambiguous answer was obtained only for calculations in seven dimensions (Fig. 14c). Three dimensions gave similar results but were not resistant to interference connected to the subtle shape of the spectrum.

From the analyses, the most probable structure of the functional units was the model BA06, with a shift in the experimental spectrum

<figure><img src="image_16.png" /><figcaption>**Fig. 13.** Input spectra on the first three principal components in the 8968 to 9067 eV spectral region.</figcaption></figure>

relative to the theoretical one of 2.3 eV (Fig. 15a). Relatively good fitting was also found for the structures of BA08 and BA07 near this point. This agrees with the calculations of the environment of copper in the particles. It can be assumed that the primary model corresponding to the experiment is the structure of a deformed trigonal bipyramid. Experimenting with mathematical methods, we have made the same calculation for the first derivative of the spectra. The derivative of the spectrum makes it possible to see additional features not visible on the basic spectrum. Analysis of the first derivative led to finding the same point, even though the PC7 projection resulted in fitting at 97%. The first derivative of the spectrum also revealed that the experimental spectrum is not sharp. This shows that silica contains several very similar structures of functional units (Fig. 15b).

Despite a good fit with a single theoretical spectrum, we performed another, more advanced, analysis. The system under study is not a crystal in which the positions of the atoms are well-defined. The amorphous molecular system we studied can be regarded as a kind of averaging of different types of regular structures. Thus, the observed spectrum is the average of various local surroundings of the copper atom. We assumed that our experimental spectrum could be a mixture of theoretical spectra. To solve this problem, we used a machine learning algorithm (ML) [61–63].

We were able to find a spectrum consisting of 62% BA06, 27% BA09, 21% BA05 model, as the most similar to experimental data (see: Fig. 15). We applied the Pearson correlation coefficient to quantify the spectra's similarity [64]. For the pure BA06 spectrum, we obtained a fitting coefficient equal to 0.99729, while for the composition of spectra obtained from the ML algorithm, the fitting coefficient was equal to 0.99764. The difference seems to be negligible. However, the visual assessment shows better agreement of spectra for the composed theoretical spectrum, especially visible at the rising edge region.

The presented analysis shows that ML algorithms can better match the theoretical spectra in those problems where the calculation of the system's energy and population cannot be applied. The structures of BA06, BA09, and BA05 are similar, as can be seen in Fig. 15. Their superposition reflects the electron structure of the copper atom when analyzing the XANES spectrum.

### 3.3.2. Extended X-ray absorption fine structure spectrum at the Cu K edge

Extended X-ray absorption fine structure (EXAFS) analysis can provide information about the type and number of ligands around the absorbing atom, as well as their radial distance. This study can help validate conclusions drawn from XANES measurements. Moreover, EXAFS allows us to extend the fit obtained from XANES analysis to a second coordination shell, despite the limited number of independent parameters. This approach seems reasonable since we anticipate the presence of phosphorus in the third coordination shell. We conducted this analysis on the data collected from the SBA-POO₂Cu4 sample and fitted it using optimized models and their contributions starting from the XANES outputs.
<figure><img src="image_17.png" /><figcaption>Fig. 14. The PCA score plot PC1–PC2 (a), PC2–PC3 (b) with nearest neighbor marked. The Euclidean distance between experimental spectra and nearest neighbor simulated spectra in 7-dimensional space to the nearest neighbor. Color points corresponds to structures presented in Figs. 4, 5, and 6.</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>Fig. 15. The experimental (a) XANES spectrum (EXP) and the closest to the experimental one simulated XANES spectra (BA06 molecule) with its graphical overview in the inset. The first derivative of the Experimental and the simulated BA06 spectra (b). The spectrum found by the machine learning algorithm consists of 62% BA06, 27% BA09, and 11% BA05 (c), together with a stack plot, showing the shape of the component plots. In the graphs, the theoretical spectra have been shifted by 2.3 eV to adjust to the real electromagnetic field affecting the sample.</figcaption></figure>

From the models, one can see that for the EXAFS analysis of the first and the second coordination shell, the fitting should be performed in the R-space in the range of 1–2.3 Å. The quality of the experimental EXAFS data allows us to set the window in k space between 3 and 11.6 Å⁻¹ (Fig. 16(b)). Therefore, for providing the refinement, we have the number of statistically independent points Nind=6.27 (parameter imposed in the Artemis program, determined by the nature of the spectrum) with five variables to be found (four values of the Cu-O paths distances and one value of energy shift). The DW factors could be set

<caption>Table 1</caption>

Structural parameters achieved from the best fitting of the EXAFS spectrum, R-factor = 0.004, reduced chi-square = 672.91.

<table><thead><tr><th>Bond type</th><th>N</th><th>R (Å)</th><th>DW factor</th><th>ΔE</th></tr></thead><tbody><tr><td>Cu–O</td><td>4.0.7</td><td>1.961 ± 0.008</td><td>0.0046</td><td>3.006 ± 1.77</td></tr><tr><td>Cu–O</td><td>4.0.3</td><td>1.920 ± 0.008</td><td>0.0046</td><td>3.006 ± 1.77</td></tr><tr><td>Cu–O</td><td>1.0.7</td><td>2.238 ± 0.040</td><td>0.0056</td><td>3.006 ± 1.77</td></tr><tr><td>Cu–O</td><td>1.0.3</td><td>2.039 ± 0.077</td><td>0.0050</td><td>3.006 ± 1.77</td></tr></tbody></table>

according to the literature [65]. In this case, the number of numerical models which could be included in the fit is limited to two. The best fit of the SBA-POO₂Cu4 experimental data was obtained using the BA06 and BA09 models derived from XANES analysis. It is worth mentioning that using only one model to refine the experimental EXAFS spectrum was enough to get a good fit. However, we set up the amplitude factor in the EXAFS equation to S₀²·0.7·4 for the fourth nearest neighbors and S₀²·0.7·1 for the farthest fifth oxygen neighbor, using the BA06 model with an input of 0.7 and doing the same with the BA09 model for the contribution of 0.3. This led to an even better fit with lower reduced chi-square and R-squared values in the refinement. Therefore, the use of only two models is justified. Adding a third model did not improve the quality of the fit, as the difference between the BA05 and BA09 models is not significant. Moreover, the phase from the third model does not influence the EXAFS spectrum a lot because the EXAFS is not sensitive enough to small changes in bond lengths and contours [65,66]. More detailed justification for abandoning the BA05 model can be found in Supplementary Information† (Section 7). This approach allowed us to select the most suitable models and their respective weights for the estimated EXAFS parameters, effectively minimizing statistical errors and ensuring a high-quality fit to the experimental spectrum. As we present in Fig. 16(b), excellent fits were obtained for two of the models. The results of the EXAFS data refinements are summarized in Table 1.

For fitting the theoretical model to the experimental data of SBA-POO₂Cu4, a single value of ΔE has been defined for all the paths, but different values of ΔR have been defined for different paths. Debye-Waller factor values have been defined from the literature [65] for each type of scattering path. The input parameter R<sub>bkg</sub> was set to 1.1 Å.

Attempting to add a phosphor scattering path to match the third shell degrades the fit. However, it is important to realize that phosphorus contributes strongly to the spectrum of the real shell (Purple spectrum in Fig. 16a). This confirms that our proposed models are quite reliable, Cu²⁺ ions are coordinated by five oxygen atoms, and the local environment of all copper atoms inside the silica matrix is slightly different for all five-fold copper sites, which is in good agreement with the above XANES analysis.
<figure><img src="image_19.png" /><figcaption>**Fig. 16.** Magnitude of Fourier transform of the EXAFS plotted vs radial distance of the central copper atom to other elements (R). The presented experimental spectrum was juxtaposed with the theoretical fit draw based on two theoretical models (presented at the right) with the assumed contribution and the signals typical for the assumed paths (a). The experimental EXAFS in k-scale together with the fitted spectrum and the data window (b).</figcaption></figure>

### 3.4. Effect of POO₂Cu group configuration on the properties of SBA-POO₂Cu4 material and its application prospects.

Analyzing all the results of our studies, it is clear that the most information about the molecular structure of the functional groups inside the silica channels was provided by XANES and EXAFS synchrotron studies supported by numerical simulations and machine learning. The XANNES research provided detailed information on the configuration of atoms in functional molecules, while EXAFS finally confirmed the result. On this basis, it was possible to reproduce the environment of the copper atom, which is surrounded by a pseudo-bi-trigonal pyramid (deformed). We have found the inside channels of the functionalized SBA-15 mesoporous silica, internal functional units occur as three very similar structures, differing slightly in the bond lengths and the dihedral angle $τ$ O-O-O-O, as shown in Fig. 15c – inset. The proportions between the different configurations were finally determined (based on the XANES study) as 62% of the BA06 model, 27% of the BA09 model, and 11% of BA05. All three structures mentioned here have been attached to this publication as numerical files†.

NMR and RAMAN studies showed the purity of the materials and confirmed the successful synthesis.

Nuclear Magnetic Resonance allowed analysis of Q3 and Q4 cyclic silicate structures forming the silica matrix. The calculation of the ratio of Q3 and Q4 structures was omitted due to the complex nature of NMR signals (such calculations would not be reliable).

Analysis of the ³¹P spectrum in Nuclear Magnetic Resonance showed that the phosphonic acid groups are arranged close enough to each other that dimer formation is possible, let alone stabilization of the copper ion by holding it by two groups. Unfortunately, it was not possible to register the copper NMR spectrum due to the magnetic nature of Cu atoms in the sample.

Determination of the exact surroundings of the copper atom inside the silica matrix made it possible to clarify the role of functional groups in generating certain SBA-POO₂Cu4 material properties. As we presented in our earlier works, copper functionalized silica SBA-15 exhibits strong biocidal properties [21]. At the time, we hypothesized that these properties were the result not so much of the contact between the bacterial cell and the copper atom but of the catalytic action of the copper ion. Now, we can fully confirm our outputs.

According to our analysis and subsequent literature reports on carbonaceous materials [67], such an arrangement of the copper surroundings and the tendency to form the first surroundings (first shell) of copper by five oxygen atoms fits into the SAC-type catalyst. This material is an excellent antimicrobial material because it effectively converts O₂ into the O₂* radical, and this radical promotes oxidative damage to biomolecules. Such copper-based single-atom catalysts can be used to eliminate various bacteria in vitro without generating multidrug resistance.

Similarly, the results obtained here have significantly clarified the interpretation of the nonlinear-optical properties of silica-based materials containing copper phosphonate groups as NLO chromophores. In our previous studies [15], we presented the dependence of the second and third harmonics generated by such materials, depending on the concentration of the functional groups. We then postulated the existence of a quasi-phase transition in the material, involving a change in the molecular structure of the functional groups from single-site coordinated copper through phosphonic acid groups to a double-site coordinated configuration. We then assumed the presence of two copper atoms in each such unit. This thesis should be corrected and refined in light of the current results. The functional units inside the highly doped SBA-POO₂Cu4 material have only one copper atom, not two, as our previous studies indicated. The depolarization of the functional groups, responsible for the disappearance of the harmonics, is not due to the superposition of the two polar units but to the coordination of the copper ion by the two acceptor groups.

The above fact is crucial for the sorption properties of materials based on porous silica of the SBA-15 type. SBA-15 functionalized silica seems to be an ideal material for capturing metal ions in water. With a huge active surface area of up to 800 m²/g, such a material has a huge active surface area for uptake. The amount of metal ions that can be immobilized inside such a material also depends on the concentration of internal anchoring groups. At first glance, one might think that such a high concentration of anchor groups is desirable as possible. However, through the research presented here, we see that this is not true. At low anchor concentrations, it is most likely that each anchor will coordinate (capture) a metal ion. However, with an increase in the concentration of anchor groups, the probability of double coordination of a single ion also increases. Thus, we have a case where there are twice as many anchor groups as immobilized ions. Taking into account the maximum possible concentration of phosphonate groups (20% as in the material investigated here), the captured ions will be only 10% in a molar ratio to the group SiO₂. Therefore, it is reasonable to conduct further research to find such a concentration of functional groups at which the molecular configuration of the metal phosphonate group will be such that each phosphonate group coordinates a single ion. At the same time, this concentration must be the maximum possible, allowing such a desired configuration. For such a case, maximum sorption capacity will be achieved.

As we have shown, the knowledge of the molecular structure has enabled a thorough understanding of the processes taking place in modern nanomaterials and their influence on the properties of such a substance. Such a thorough analysis of the molecular structure of the functional groups in nanocomposite materials opens up new cognitive possibilities, and these enable their further application in industry.
## 4. Conclusions

In conclusion, this study explored the local geometry of copper cations inside mesoporous silica pores using X-ray absorption spectroscopy and theoretically calculated XANES spectra. The results obtained provided valuable information on the structure and environment of copper cations, which is essential for understanding their behavior and electronic activity. The experimental data obtained by X-ray absorption spectroscopy were complemented by the theoretically calculated XANES spectra, thereby validating the experimental observations and deepening our understanding of the interactions between copper cations and the mesoporous silica matrix that will be used later to explain some multifunctional behavior of the hybrid materials. The results suggest that copper cations favor the five-fold geometry found for the hydrated copper ions. The approach adopted in the interpretation of the experimental XAS spectrum opens up new possibilities for extending the calculation strategy we have used to the SBA-Cu samples with different concentrations.

In summary, this study contributes to the advancement of our knowledge of copper-based functionalized silica materials and opens up new perspectives for the design and optimization of more efficient materials. Finally, we would like to draw your attention to the fact that our work goes beyond local structural solutions and delves into a comprehensive theoretical framework for analyzing XANES. It is critical to recognize that our research not only addresses the complexities of local structures but also provides a solid theoretical framework for analyzing XANES when it is challenging to analyze the EXAFS region.

### CRediT authorship contribution statement

**Sara El Houbbadi:** Writing – original draft, Visualization, Investigation, Formal analysis, Data curation. **Magdalena Laskowska:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Funding acquisition, Formal analysis, Conceptualization. **Alain Walcarius:** Writing – review & editing, Writing – original draft, Validation, Supervision, Methodology, Conceptualization. **Marek Doskocz:** Writing – review & editing, Writing – original draft, Visualization, Software, Investigation, Formal analysis, Data curation. **Alexey Maximenko:** Writing – review & editing, Writing – original draft, Methodology, Investigation, Formal analysis, Data curation. **Zbigniew Olejniczak:** Writing – review & editing, Writing – original draft, Visualization, Investigation, Formal analysis, Data curation. **Łukasz Laskowski:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Funding acquisition, Formal analysis, Conceptualization.

### Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Łukasz Laskowski reports financial support was provided by National Science Centre Poland. Magdalena Laskowska reports financial support was provided by National Science Centre Poland. Alain Walcarius reports financial support was provided by National Agency for Academic Exchange. Sara El Houbbadi reports financial support was provided by National Agency for Academic Exchange. Alexey Maximenko reports financial support was provided by EU Framework Programme for Research and Innovation Research Infrastructures and E-Infrastructures. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Data availability

Data will be made available on request.

## Acknowledgments

This work has been supported by the resources of the National Science Centre: 2020/37/B/ST8/03637 (ŁL), and 2021/43/D/ST8/00737 (ML).

Part of the research was done within the framework of the POLONIUM project: BPN/BFR/2022/1/00003/U/00001 (A.W. and Ł.L.)

The authors are grateful to Poznan Supercomputing and Networking Center - PCSS for the opportunity to conduct numerical simulations (Grants id 422 and 579).

The X-ray absorption spectroscopy measurements were made at ASTRA beamline under the provision of the Polish Ministry and Higher Education project "Support for research and development with the use of research infrastructure of the National Synchrotron Radiation Centre SOLARIS" under contract nr 1/SOL/2021/2. The further development of ASTRA beamline at NSRC SOLARIS was supported within the EU Horizon2020 program (952148-Sylinda)

Sara El Houbbadi is grateful to the Polish National Agency for Academic Exchange NAWA for the financial support under the Programme STER– Internationalisation of Doctoral Schools, Project No. PPI/STE/2020/1/00020.

## Appendix A. Supplementary information

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.apsusc.2024.160425.

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