## 4 Discussion
NanoSIMS50 is characterized by low background noise from the electron multipliers and ubiquitous electrical noise [56]. In our control images, the overall noise is low (about 0.23 counts per second, i.e., an average of one count per 80 pixels). The challenge is now to distinguish NPs' signals from noise. Since, using SIMS, TiO\({}_{2}\) NPs' ion yield is low, the expected signal of an isolated nanoparticle is low. The risk of confusing it with background noise is high. Several methods have been reported to reduce background noise. The simplest method is to apply a threshold, e.g., to consider only pixels associated with two or more counts. Such a threshold would have resulted in the removal of most of the positive pixels in our images, restricting the study to large aggregates only. Statistical approaches have been developed to denoise images using a Fourier transformation [57] or statistical characterization of local noise [47; 56]. In this study, these methods were not applicable due to the small size of the objects to be detected and their weak signals. We, therefore, applied an SCP filter to reduce image noise and, thus, highlight the signal from the NPs. It was justified by an SRIM sputtering simulation. Considering the sputtering yield (about 1.3 Ti atoms sputtered per cesium primary ion), the number of Ti atoms per NP (about 57,000), the incident cesium dose (about 12,000 Cs for a 2 pA beam and 1 ms dwell time), we estimated that 27% of an NP (15,600 Ti atoms) can be sputtered. Then, an isolated NP cannot be entirely sputtered during the dwell time corresponding to a single pixel. Consider an isolated NP and the overlapping of the ion beam (as expressed above); the NP is sputtered in at least two contiguous pixels. Depending on the 3D orientation of the NP in the resin, the signal can spread across two or more contiguous pixels in at least one of the three dimensions. By removing non-contiguous positive pixels, the SCP filter effectively denoises images while preserving relevant signals (true positive pixels). In summary, we proposed in this paper a procedure to correlate TEM and SIMS images. This process involves (1) data acquisition, (2) SIMS alignment of planes, (3) TEM and SIMS alignment, (4) SCP filter application, and (5) the co-identification of nano-objects. Considering all the data, it is possible to localize in 3D nano-objects within a section thickness of a few tens of nanometers. According to us, this work applied a correlative imaging approach. It is difficult to propose a universal definition of correlative imaging. Under the term correlative imaging, numerous studies have combined analytical imaging (of a specific signal) with structural imaging. The aim is to optimize the specificity (reliable detection), the sensitivity, and/or the detection limit of the signal of an object to be localized as precisely as possible within a clearly identified biological matrix. An example of correlative imaging is the combination of SIMS microscopy as an analytical technique and TEM as structural imaging. This approach has been previously applied in biology [27; 30; 31; 33; 34; 37; 38; 43; 58; 59]. However, most of these studies compare images without overlaying them. Here, we overlaid the TEM and SIMS images obtained at two different masses: \({}^{12}\)C\({}^{14}\)N, for the recognition of biological structures in complementarity with the TEM image, and \({}^{48}\)Ti\({}^{16}\)O, for the specific detection of TiO\({}_{2}\) NPs. In our study, we were able to detect and localize isolated NPs or small aggregates in contact with biological tissue. Data complementarity and image overlay (an alignment procedure and filter application) show that some NPs, even if isolated, can be detected using both techniques; some can be detected via TEM but not SIMS, and others can be detected via SIMS but not TEM. These data were used to interpret the location and/or distribution of NPs in the depth of the sample section. Our correlative approach provided more information than each technique considered in isolation.