Iphc Filliger

François; IPHC Iphc Didierjean

Ginsz

B De Canditiis

G Duchêne

M H Sigward

M Filliger

F Didierjean

M Ginsz

D Ralet

Full-volume characterization of an AGATA segmented HPGe gamma-ray detector using a 152 Eu source

Keywords: Nuclear Physics HPGe detectors, Full volume characterization, Pulse Shape Analysis (PSA), AGATA, Scanning table, 152 Eu source

Scanning tables use collimated gamma-ray sources to perform full volume characterization of position sensitive detectors. One of such tables is hosted at IPHC Strasbourg. It was designed and built within the AGATA collaboration. It exploits the Pulse Shape Comparison Scanning (PSCS) technique to build databases of pulses used to characterize the response of high purity germanium (HPGe) detectors and perform R&D on such crystals. Ultimately, measured databases could be used by the pulse shape analysis (PSA) algorithms employed in AGATA experiments. The table can perform full volume scans of large volume detectors in short times, with a good spatial resolution and at different energies. Lately, the table was upgraded with a new 152 Eu source, which emits gamma rays in cascades of different energies. A scan with such source is performed for the first time. It allows to build different energy databases in one single scan. The present work aims at testing the performances of the PSCS technique with a multienergetic source and verifying some assumptions of the Shockley-Ramo theorem which are at the base of the PSA algorithms used for gamma-ray tracking arrays.

The Advanced GAmma Tracking Array (AGATA) [START_REF] Akkoyun | AGATA -Advanced GAmma Tracking Array[END_REF][START_REF] Korichi | Tracking gamma rays in highly segmented HPGe detectors: A review of AGATA and GRETINA[END_REF] is a new generation array of high-purity germanium (HPGe) segmented detectors. Designed and built in the framework of an European collaboration, it will be composed by 180 large-volume, encapsulated, asymmetric, a e-mail: bart.decan@gmail.com 36-fold segmented detectors covering a solid angle of ∼ 80% of 4π. Presently, the array is composed by 40 detectors in operation in GANIL [START_REF] Clément | Conceptual design of the AGATA 1π array at GANIL[END_REF] and since 2010 the array is used in experiments to study the structure of atomic nuclei as a function of angular momentum, isospin, and temperature.

The main feature of AGATA is the ability to reconstruct the history of the interactions of a gamma ray scattering in the array, which is possible thanks to dedicated tracking algorithms [START_REF] Van Der Marel | Backtracking as a way to reconstruct compton scattered -rays[END_REF][START_REF] Lopez-Martens | Gamma-ray tracking algorithms: a comparison[END_REF][START_REF] Suliman | Fuzzy clustering algorithm for gamma ray tracking in segmented detectors[END_REF][START_REF] Didierjean | The Deterministic Annealing Filter: A new clustering method for gammaray tracking algorithms[END_REF][START_REF] Tashenov | Tango -new tracking algorithm for gamma-rays[END_REF]. The reconstruction of the gamma-ray trajectories makes obsolete the use of BGO Compton shields. Moreover, it allows a more precise determination of the first interaction which leads to a substantial improvement of the Doppler correction and therefore of the in-beam energy resolution. Tracking also allows the measurement of the linear polarization of gamma rays [START_REF] Bizzeti | Analyzing power of AGATA triple clusters for gamma-ray linear polarization[END_REF], which is useful for the determination of the parity of the nuclear states of interest.

The first step for the tracking algorithm is to determine the positions of each interaction of the gamma rays in the array. This is performed with pulse-shape analysis (PSA) algorithms [START_REF] Venturelli | Adaptive grid search as pulse shape analysis algorithm for gamma-tracking and results[END_REF][START_REF] Bruyneel | Pulse shape analysis and position determination in segmented HPGe detectors: The AGATA detector library[END_REF][START_REF] Mihailescu | Principles and Methods for gamma ray tracking with large volume Germanium Detectors[END_REF][START_REF] Mihailescu | The influence of anisotropic electron drift velocity on the signal shapes of closedend HPGe detectors[END_REF][START_REF] Medina | A simple method for the characterization of HPGe detectors[END_REF][START_REF] Schlarb | Pulse shape analysis for gamma-ray tracking (Part I): Pulse shape simulation with JASS[END_REF][START_REF] Mateu | Simulation of the charge collection and signal response of a HPGe double sided strip detector using MGS[END_REF] that, for a given interaction, compares the experimental pulse shape with a database of calculated pulse shapes associated with spatial coordinates within the volume of the detector.

Databases of pulses are presently obtained numerically using the AGATA Detector Library (ADL) [START_REF] Bruyneel | Pulse shape analysis and position determination in segmented HPGe detectors: The AGATA detector library[END_REF] leading to good tracking performances. However, some characteristics of the detectors such as the real impurity distribution and the thickness of the dead layers are not always correctly known and their implementation is subject to uncertainties. A novel approach to database construction is the implementation of full volume characterization of the detectors via three-dimensional scans realized with dedicated tools called scanning tables [START_REF] Dimmock | Validation of Pulse Shape Simulations for an AGATA prototype detector[END_REF][START_REF] Ha | New setup for the characterisation of the AGATA detectors[END_REF][START_REF] Goel | Spatial calibration via imaging techniques of a novel scanning system for the pulse shape characterisation of position sensitive HPGe detectors[END_REF][START_REF] Hernandez-Prieto | Study of accuracy in the position determination with SALSA, a gammascanning system for the characterization of segmented HPGe detectors[END_REF]. A first kind of such tables [START_REF] Dimmock | Validation of Pulse Shape Simulations for an AGATA prototype detector[END_REF][START_REF] Ha | New setup for the characterisation of the AGATA detectors[END_REF] performs scans of germanium detectors by irradiating them from below with a collimated gamma-ray source. Then the databases are built with the interacting gamma rays that undergo a 90 • Compton scattering and that are detected in coincidence by surrounding scintillator crystals, collimated by narrow slits. The double collimation grants to these tables a great geometrical precision (with voxels of about 8mm 3 ) at the cost of having a low counting rate due to the imposed coincidence condition (∼ 100 signals in total per point).

A second kind of scanning tables [START_REF] Goel | Spatial calibration via imaging techniques of a novel scanning system for the pulse shape characterisation of position sensitive HPGe detectors[END_REF][START_REF] Hernandez-Prieto | Study of accuracy in the position determination with SALSA, a gammascanning system for the characterization of segmented HPGe detectors[END_REF] make use of the two 511 keV gamma rays emitted back-to-back from the β + decay of a 22 N a source to determine precisely the emission direction with an imaging detector. The scanned germanium detector is exposed two times from two orthogonal directions and the database is built by reconstructing off-line the position of crossing interacting gamma rays (voxels of ∼ 8 mm 3 ).

The former technique enables to obtain a ∼ 2000 points database of an AGATA detector in about one to two months, whereas the latter performs a full volume scan in few days.

A scanning table, realized within the framework of the AGATA collaboration, is based at the IPHC of Strasbourg [START_REF] Sigward | Pulse-Shape Comparison Scan of germanium detectors using the IPHC scanning table[END_REF]. This scanning table is designed to perform three-dimensional scans with a large amount of scanned points and relatively short running times implementing the Pulse Shape Comparison Scan (PSCS) technique [START_REF] Crespi | A novel technique for the characterization of a HPGe detector response based on pulse shape comparison[END_REF]. The PSCS technique allows to measure a database by comparing two datasets of pulses obtained by performing two two-dimensional scans, in which the detector is oriented in two different directions perpendicular to each other (e.g. vertical and horizontal). With such technique it is possible to scan an AGATA crystal with a 2 mm pitch in about two weeks, obtaining a database of 45000 to 48000 points depending on the detector geometry (there are four different AGATA crystal geometries). Thus, the IPHC scanning table combines the advantages of both coincidence and imaging scanning tables as it is relatively fast and precise. Moreover, it allows the use of different gamma-ray sources with different energies, leading to versatility in the characterization process as low energies can be used to explore the detector surface while the full volume can be scanned at higher energies.

Currently, three collimated sources are available for the scans. Sources of 241 Am and 137 Cs were already tested with the scanning table [START_REF] Sigward | Pulse-Shape Comparison Scan of germanium detectors using the IPHC scanning table[END_REF][START_REF] Ginsz | Characterization of high-purity, multisegmented germanium detectors[END_REF]. Recently a new 152 Eu source was acquired. As the source emits gammarays with different energies, this opens the possibility to characterize the detector response at different energies with just one scan. A three-dimensional scan of a sym-Fig. 1: Dimensions of the S001 detector (units in mm). The figure shows the numbering of the slices (1 to 6, on the left) and the labeling of the sectors (A to F, on the right) in which the detector is segmented. The blue lines show the reference frame used in the following discussion for PSCS technique results presentation. metrical AGATA (S-type) detector was performed using the 152 Eu source and five different-energy databases were built. This unique measurement is performed for the first time and it aims to explore the new technique and to verify, by comparing the different energy databases, that the pulse shapes are independent of the energy of the gamma ray interaction that generates them. This principle asserted by the Shockley-Ramo theorem [START_REF] Shockley | Currents to conductors induced by a moving point charge[END_REF][START_REF] Ramo | Currents induced by electron motion[END_REF][START_REF] He | Review of the Shockley-Ramo theorem and its application in semiconductor gammaray detectors[END_REF] is at the base of the AGATA PSA algorithms implementation but has never been demonstrated experimentally. In the first part of this paper the experimental setup is described. Subsequently, the data treatment is discussed followed by the experimental results. Conclusions and perspectives are given in the last section.

Experimental setup

S001 detector unit

The measurements described in this work are realized on the S001 AGATA detector unit which is a 36-fold segmented, closed-end, coaxial, n-type HPGe crystal with a tapered hexagonal geometry and a symmetric shape (S-type) [START_REF] Simpson | AGATA[END_REF][START_REF] Canditiis | 3D characterization of multisegmented HPGe detectors[END_REF]. The crystal geometry and its segmentation are represented in figure 1 and described in detail in reference [START_REF] Canditiis | Simulations using the pulse shape comparison scanning technique on an AGATA segmented HPGe gamma-ray detector[END_REF].

From the detector, 36 segment signals plus 1 totalenergy signal are collected, the former on the boron segmented contacts and the latter on the central contact (core). The signals are collected by 37 charge-sensitive preamplifiers [START_REF] Akkoyun | AGATA -Advanced GAmma Tracking Array[END_REF][START_REF] Pullia | An advanced preamplifier for highly segmented germanium detectors[END_REF][START_REF] Zocca | A time-over-threshold technique for wide dynamic range gamma-ray spectroscopy with the AGATA detector[END_REF][START_REF] Pascovici | Low noise, dual gain preamplifier with built in spectroscopic pulser for highly segmented high-purity germanium detectors[END_REF] characterized by low noise performances, large dynamic range for energy detection (0 -5.7 M eV or 0 -20 M eV ) and fast response time of ∼ 25 ns. A large bandwidth ensures good resolution for pulse-shape analysis and good timing properties. Figure 2 shows an example of a signal generated by a 662 keV gamma ray ( 137 Cs source) interacting in segment E3. The signal is presented as a supertrace, defined as a compact, continuous representation of all the leading edges coming from the 36 segments and the core of the detector. The signal is normalized with respect to the core signal amplitude

The PSCS technique and the IPHC scanning table

The PSCS technique [START_REF] Crespi | A novel technique for the characterization of a HPGe detector response based on pulse shape comparison[END_REF][START_REF] Canditiis | Simulations using the pulse shape comparison scanning technique on an AGATA segmented HPGe gamma-ray detector[END_REF] allows to characterize the full volume of a position sensitive detector, such as the S001 unit. This is done by irradiating the detector from two different positions with a collimated gammaray source. The detector is put at first in vertical position and irradiated at different points with a given step forming a regular vertical grid. For each point a dataset of pulses generated along the gamma-beam direction is acquired. The detector is then put in horizontal position and the procedure is repeated ensuring that the horizontal grid intersects the vertical grid. Subsequently, a χ 2 algorithm (later described in section 3) selects the most similar pulse shapes of two given intersecting datasets, that is the ones that are generated by the interactions happening around the intersection point. The algorithm is applied to all the couples of intersecting datasets and a database of pulses is created.

The technique above described is implemented by the IPHC scanning table [START_REF] Sigward | Pulse-Shape Comparison Scan of germanium detectors using the IPHC scanning table[END_REF][START_REF] Ginsz | Characterization of high-purity, multisegmented germanium detectors[END_REF][START_REF] Canditiis | 3D characterization of multisegmented HPGe detectors[END_REF], shown in figure 3, which was designed with the aim of performing scans in short times, with good spatial resolution and variable energies.

The table is formed by two metallic plates that hold the detector in vertical or horizontal position. The detector is actually placed in an adjustment frame that allows precise alignment on the table. Underneath, a cylindrical, metallic collimator sits on two motorized axes that allows its planar movement. The central part of the collimator is swappable allowing to choose collimation diameters of 1 mm, 0.5 mm and 0.2 mm. The collimator can be opened from below allowing the insertion of three source capsules. The characteristics of these sources are given in table 1. The data acquisition is done with TNT2 cards [START_REF] Arnold | TNT Digital Pulse Processor[END_REF], developed at IPHC. These cards cover both the digitizing and pre-processing of the signals done by the standard AGATA electronics [START_REF] Akkoyun | AGATA -Advanced GAmma Tracking Array[END_REF]. As each card has 4 channels, 10 cards are used for scanning an AGATA detector unit, 9 for the 36 segments and one for the core signal used as trigger. Each channel samples the incoming signal with 14 bit resolution and 100 M Hz rate. Subsequently, the energy is measured applying a moving window deconvolution algorithm [START_REF] Jordanov | Digital techniques for real-time pulse shaping in radiation measurements[END_REF][START_REF] Georgiev | An analog-to-digital conversion based on a moving window deconvolution[END_REF]. For the measurements shown in this work, the signals were sampled in a time window of 1.2 µs (120 samples) and a 5.95 µs shaping time was used for the moving window deconvolution algorithm [START_REF] Canditiis | 3D characterization of multisegmented HPGe detectors[END_REF].

3 Data acquisition and treatment

Performed scans

Before proceeding with the scans, the detector is aligned on the scanning table by performing several preliminary scans and precise adjustments. The alignment ensures that the measured tilt of the detector, with respect to the reference frame of the scanning table, is at maximum 0.05 • . A detailed description of the general alignment procedure can be found in [START_REF] Sigward | Pulse-Shape Comparison Scan of germanium detectors using the IPHC scanning table[END_REF][START_REF] Ginsz | Characterization of high-purity, multisegmented germanium detectors[END_REF][START_REF] Canditiis | 3D characterization of multisegmented HPGe detectors[END_REF].

For the work here presented, two three-dimensional scans of the S001 detector were performed with the 137 Cs and 152 Eu sources. Both scans were performed with a pitch of 2 mm and the 1 mm diameter collimator. The 137 Cs scan had a duration of 2.5 minutes per point, leading to a total duration of 6.7 days (2.5 days for the vertical acquisition and 4.2 days for the horizontal acquisition, including the nitrogen refill pauses).

As the PSCS technique using a 137 Cs source has been already tested and validated [START_REF] Sigward | Pulse-Shape Comparison Scan of germanium detectors using the IPHC scanning table[END_REF][START_REF] Ginsz | Characterization of high-purity, multisegmented germanium detectors[END_REF], this scan is used as a reference.

The acquisition times for the 152 Eu scan, instead, were significantly longer due to the fact that the source intensity (0.74 GBq) is 2.5 times lower than the one of the 137 Cs source (1.85 GBq) and is divided among its different emitted gamma rays. Among the peaks of interest for the analysis (122 keV , 245 keV , 344 keV , 779 keV and 1408 keV all gated in a ±2 keV window) the 779 keV has one of the lowest branching ratio. In order to obtain similar statistics between the events at this energy and the ones at 662 keV , an acquisition time of 60 minutes per point was used. To lower the global scanning times, only one single sector (sector B) was scanned. Nevertheless, the total scan duration was 54 days (14 days for the vertical acquisition and 40 days for the horizontal acquisition, including the nitrogen refill pauses).

The 152 Eu events, which are acquired in a single scanning procedure, are sorted and prepared in five separated datasets, one for each energy, and treated independently by the χ 2 selection algorithm, described in section 3.2, leading to the construction of five different databases. For each dataset, only 1-fold events are selected, that is the events in which a gamma ray interacts in a single segment. Moreover, the pulse shapes coming from each segment are normalized and then aligned to their T 50 , the time at which the pulses reach 50% of their maximum amplitude. In particular the transient pulses are normalized and aligned with respect to the signal of the hit segment.

For the 245 keV and 344 keV databases, data from the last slice of the detector (slice 6) was not taken into account. In fact, it is observed that the peak to total ratio relative to these energies drops below 70% [START_REF] Canditiis | 3D characterization of multisegmented HPGe detectors[END_REF] towards the back of the detector. The same considerations are applied to the 122 keV database and only the data in the first two slices of the detector (slice 1 and 2) are considered. Finally, figure 1 shows the reference frame in which the results of the analysis will be presented.

The χ 2 algorithm

The acquired datasets are processed with a code that implements the pulse-shape comparison algorithm. The comparison between two pulses is performed by calculating the χ 2 value with equation

χ 2 = seg∈M i f i=i0 (v seg,i -h seg,i ) 2 M • (i f -i 0 ) ( 1
)
where seg is the segment index, M is the M dimensioned ensemble of segments formed by the Core seg-

F o r P e e r R e v i e w
ment, the hit segment and the neighboring segments of the hit segment (4 segments if the hit segment is in the middle of the detector or 3 segments if the hit segment is at the top or bottom of the detector) and v i and h i are the samples of the vertical and horizontal signals, respectively. Finally, the comparison is performed on a subset of samples in the window [i 0 , i f ] containing the leading edge of the pulse.

Only the signals produced by events happening in the same segment are compared. Each pair of events compared is inserted in a χ 2 -value-ordered list. For a given point, the number of pulses in the compared vertical and horizontal sets are typically of the order of ∼ 10 3 (this number depends on the detector geometry and on the relative source position), leading to about ∼ 10 6 couples of pulses. To ensure both a strict selection and sufficient statistics, at the end of the procedure only the 200 couples with the lowest χ 2 values are selected. Among these, some events may have been selected more than once. The duplicates are discarded from the list before the mean pulse-shape refinement is applied. This procedure (described in detail in [START_REF] Canditiis | Simulations using the pulse shape comparison scanning technique on an AGATA segmented HPGe gamma-ray detector[END_REF]) uses again the pulse-shape comparison to reject the most diverging pulses, with respect to the average pulse, giving a more coherent final selection of events. About 140 pulses are finally selected for each coordinate of the pulse shape database.

The remaining pulses are re-aligned and averaged following the so-called difference minimization algorithm (see section 5.3.2 of reference [START_REF] Unsworth | Characterisation of an Asymmetric AGATA Detector[END_REF]) and the resulting pulse is inserted in the database. The algorithm acts as follows. A first signal is aligned to a fixed point. Subsequently, each other signal is shifted and compared to the first one by calculating the root mean square (RMS) difference. The RMS is calculated in the T 90 10 time window (defined as the time interval in which the amplitude of the considered signal goes from 10% to 90% of its maximum amplitude) of the signal. Although, in the case of the 122 keV database the RMS is calculated in the T 90 25 time window due to lower signal to noise ratio. The shift relative to the minimum RMS is then used as the new alignment point for the signal. All the so aligned signals are then averaged and the procedure is repeated using this preliminary mean signal as the first signal. At the end of the procedure a new average signal is calculated and the resulting pulse is inserted in the database.

Once the database is created, it is important to note that some pulses calculated by the algorithm are associated to points that lay outside the volume of the detector. Such cases can exist because the χ 2 selection algorithm is blind of the geometry of the detector. Since on average the χ 2 values associated to these pulses is higher than the χ 2 values of the pulses selected inside the detector, it is possible to set a threshold in order to filter them and clean the database.

The overall procedure is resumed by the following steps:

1. Two intersecting datasets, corresponding to a given detector coordinate, are selected and their pulse shapes are compared through a χ 2 -like formula: 2. The 200 best-matching couples are selected. 3. Duplicate events are rejected. 4. The remaining pulses are treated with the mean pulse-shape refinement. ∼ 140 pulses are finally selected. 5. The pulses are re-aligned and averaged with the difference minimization algorithm. 6. Steps 1 through 5 are repeated until completion of the database. 7. The database is cleaned from unphysical points.

Experimental results

Comparison of databases of different energies

As a first test, the 662 keV and 779 keV databases are compared, since they are close in energy. It is possible to make a first comparison of the two databases by looking at the distribution of parameters such as the T 90 10 and the left/right image charge anisotropy (ICA l/r ) which both depend from the shape and characteristics of the considered signals. The T 90 10 is defined as above (section 3.2) and the left/right ICA is defined as [START_REF] Ginsz | Characterization of high-purity, multisegmented germanium detectors[END_REF]

ICA l/r = I l -I r I l + I r + 1 (2)
where I l and I r are the integrals of the transient signal of the left and right neighboring segments. The +1 is an offset to make the ICA value range in [0,2]. The ICA parameter give an indication on the location of the interaction relative to the neighboring segments. The distributions of the T 90 10 and ICA l/r values are shown in figures 4 and 5 for both the 662 keV and the 779 keV databases. Distributions at three database slices (Z C = 6 mm, 48 mm, and 80 mm) are presented. They are very similar, hinting that the two scans are comparable.

Indeed, if two pulses with the same coordinates are compared, as in figure 6, it can be seen that they are almost identical as it is shown by the residual values between the two traces (in green). In the example shown in figure 6 the maximum residual value among the two supertraces is ∼ 0.8% of the maximum amplitude reached by the net charge signal. As the maximum residual value is a tool that quantify the maximum amplitude difference of two given pulses, it can be used as an index to observe where and how much two databases diverges the most. Thus, the comparison can be extended to all the points of the 662 keV and 779 keV databases. The distributions of the maximum residuals, in percent, for three database slices are shown in figure 7. The distributions are quite homogeneous in each slice despite some points having high values (dark red) which appear to be concentrated near the core contact and the segmentation lines.

The average value for each database slice can be calculated and the results plotted as a function of Z C as in figure 8a (red markers). The graph shows that the average maximum residual per database slice has a value of 2% to 3% toward the front of the detector and increases slightly towards the back of the crystal where it reaches values up to 6% at the back. Moreover, in correspondence of the segment separation surfaces (Z C = 14 mm, 22 mm, 36 mm, 56 mm and 74 mm) slight value increases are observed. The same analysis is performed by taking into account only the transient pulses. The graph, in figure 8b, shows that the average maximum residual between transient pulses is lower with respect to the net charge signals, ranging in the interval 1% to 3%. The bars in figures 8a and 8b show the standard deviation of the maximum residual distribution for database slices at the front, center and back of the detector. They show a quite large spread, mostly due to single or clusters of points which exhibit large values (as seen in figure 7). These points are mostly concentrated along the segmentation surfaces, where it has been observed [START_REF] Canditiis | 3D characterization of multisegmented HPGe detectors[END_REF][START_REF] Canditiis | Simulations using the pulse shape comparison scanning technique on an AGATA segmented HPGe gamma-ray detector[END_REF] that the PSCS technique presents some limitations.

The above analysis is performed comparing the remaining measured databases in pairs. At first, the 1408 keV database is compared with the 662 keV and 779 keV databases. The results are represented in figure 8 Each point represents the mean value of the maximum residual distribution corresponding to a given database slice (see figure 7). The graphs refers to the comparison, pair by pair, between the 662 keV , 779 keV and 1408 keV databases. In (a) the maximum residuals are calculated with the whole supertrace while in (b) only the transient pulses are used. and green markers, respectively) and show a similar trend, with comparable values, of the average maximum residuals, with respect to the 662 keV and 779 keV compared databases.

Due to their similarity, the three graphs are grouped in a single, average data series and reported with red markers in figure 9. The same operation is performed for the data series corresponding to the comparison between the database of a given energy with the ones of higher energy (i.e. 344 keV versus 662 keV , 779 keV and 1408 keV ). It is, in fact, observed that these data series are comparable as seen for the previous case (i.e. comparison among databases with energies larger than Each point represents the mean value of the maximum residual distribution corresponding to a given database slice (see figure 7). The graphs refers to the comparison, pair by pair, between the 122 keV , 245 keV , 344 keV 662 keV , 779 keV and 1408 keV databases. The data series corresponding to the comparison between a database of a given energy and all the databases of higher energy are regrouped and averaged (see text for detail). In (a) the maximum residuals are calculated with the whole supertrace while in (b) only the transient pulses are used. 500 keV ). This choice is taken mainly to ease the readability of the results, represented in figure 9.

The data series relative to the 344 keV database (green markers) follow the same trend of the high end energy ones until Z C = 46 mm. From there on, the 344 keV database and the high end energy databases start to diverge. For Z C > 56 mm the average maximum residuals reach values around 7% , which is more than a factor 2 larger than the ones for the high end energy data series. Similarly, the 245 keV database is comparable with all the databases with E γ > 245 keV (light blue markers) in the first two slices of the detector (Z C < 24 mm) then it starts to diverge reaching values for the average maximum residuals which are over 10% towards the back of the detector.

Finally, the blue markers in figure 9 show the comparison between the 122 keV and the other energy databases. These data series don't follow any of the trends previously discussed as the total average maximum residuals have values of ∼ 5% in the front slice of the database and then increase to over 10% toward slice 2 of the detector.

Discussion

The analysis presented above indicates that, for gammaray energies larger than 500 keV , the pulse shapes are independent of the energy of the interaction. The corresponding databases match, in average, within 3% with maximum values around 5%. For energies lower than 500 keV discrepancies appear. The 344 keV and 245 keV databases follow the same trend of high energy databases until Z C = 46 mm and Z C = 24 mm respectively, while the 122 keV database presents overall large inconsistencies.

The observed discrepancies for energies below 500 keV are not a prove that the pulse shapes differ with the energy of the interacting gamma-ray. They may rather be related to limitations of the pulse-shape selection and to the PSCS implementation, especially when approaching segmentation or segment slice borders.

As a first hypothesis one can think that the discrepancies are due to the gamma-ray absorption that lowers the statistics of vertical datasets. However, this alone can't be the cause of the phenomenon. In fact, by analyzing the events distribution per slice of the vertical datasets it can be seen that the available statistics for the back slices of the detector is larger for the 344 keV scan with respect to the 779 keV and 1408 keV ones [START_REF] Canditiis | 3D characterization of multisegmented HPGe detectors[END_REF]. This is due to the higher branching ratio of the 344 keV gamma ray which compensates for the absorption in the germanium.

Another factor that can cause discrepancies is the noise level of the pulses used for the χ 2 selection. For example, the noise amplitude of the 122 keV pulses is measured [START_REF] Canditiis | 3D characterization of multisegmented HPGe detectors[END_REF] to be in average ∼ 5 keV and ∼ 2 keV for the core and segment contacts, respectively, which correspond to ∼ 6% and 2% of their maximum amplitude. The transient signals amplitudes are generally small with respect to the amplitude of the net charge signal. In fact, apart from the segment border where they may rise up to ∼ 30%, they are generally limited to ∼ 15 -10%, or less, of the net charge signal amplitude. These amplitudes are to be compared with the 2% noise amplitude, which therefore certainly affect the pulse shape as shown in figure 10. Such distortion can influence the χ 2 selection. High levels of noise can also lead the time alignment to fail as the T 50 value can be incorrectly shifted due to the noise and the χ 2 selection would then be performed on events that are not properly aligned.

The analysis evidenced that the larger discrepancies between signals are found on the net charge signals rather than on the transient signals (figure 9). It has to be noticed, however, that the transient signals are normalized to the maximum amplitude of the net charge signals. Since the amplitude of transient signals is in relation smaller, it may mean that actually the difference between the transient pulses has a more relevant impact on the database comparison.

Finally, it should be mentioned that effects due to the energy of the detected interaction were observed and analyzed in [START_REF] Soederstroem | Interaction position resolution simulations and in-beam measurements of the AGATA HPGe detectors[END_REF]. In that work, in-beam events acquired with the first AGATA triple cluster (3 crystals) were reconstructed with the PSA and tracking algorithms used for on-line data treatment and off-line data analysis. The results showed that the spatial resolution improves linearly in the range ∼ 250 keV to 1500 keV as the energy increases and remains constant at higher energies. Although a simple, direct comparison among the results shown in the present work and the ones in [START_REF] Soederstroem | Interaction position resolution simulations and in-beam measurements of the AGATA HPGe detectors[END_REF] is not possible, it is worth to note that the AGATA PSA algorithm uses a similar factor of merit formula to the one used for the PSCS technique (see equation 1) with an optimized exponent. Thus, it is expected that the PSA algorithm may present problems similar to the PSCS ones for events which energies are below 500 keV and may be less and less precise with decreasing energy. This phenomenon may partially explain the spatial resolution deterioration at low gamma-ray energy evidenced in [START_REF] Soederstroem | Interaction position resolution simulations and in-beam measurements of the AGATA HPGe detectors[END_REF].

Conclusions and Perspectives

Three-dimensional scans were performed with the 137 Cs and 152 Eu sources. Due to the low activity of the source and the branching ratio of the transitions of interests, the 152 Eu scan was restricted to one sector of the detector (sector B). The 152 Eu scan allowed to construct five different databases of pulses, with different energies and at exactly the same points, in a single measurement. Databases were built for the energies 122 keV , 245 keV , 344 keV , 779 keV , 1408 keV ( 152 Eu) and 662 keV ( 137 Cs). A comparison was made within each database, and the maximum residual value between two signals associated to the same point of the database was chosen as comparison parameter. The databases with an energy > 500 keV proved to be comparable within each other, with differences varying along the crystal length from around 3% (front) up to 5% (back), in average. Some discrepancies were instead found with the 344 keV database for Z C values larger than 46 mm. The residual values appeared to diverge by a factor 2 towards the back of the detector. The same behavior was found for the 245 keV database which starts to diverge for Z C values larger than 24 mm. Finally, the 122 keV database proved to be strongly divergent from the other databases.

It was supposed that the reasons behind these divergent behaviors are to be attributed to the PSCS technique that somehow is not efficient at low energies. It is hypothesized that the major cause for this lays in the noise level which, for low amplitude signals, can have an impact on the pulse alignment and can distort the transient signals, leading to a loss of information. The impact of the gamma ray absorption on the input statistics was also considered among the discrepancies causes, although this may have only a minor impact.

It was remarked that the parameters shown in the present work share a similar trend to the spatial resolution of the AGATA detectors which deteriorates as the energy of the interacting gamma ray decreases, as observed in [START_REF] Soederstroem | Interaction position resolution simulations and in-beam measurements of the AGATA HPGe detectors[END_REF]. This points out that the PSA algorithms may be less precise for interaction energies below 500 keV . Further investigations on the phenomenon are necessary. Some improvements on the PSCS technique could lead to better performances. For example, the use of noise filtering techniques, such as the Fourier transform noise canceling, could be evaluated, especially for low energy pulses where the PSCS appears to be more affected. In order to enable measurements with low en- ergies in a full sector volume using the 152 Eu source, a horizontal-horizontal scanning configuration could be tested. It would reduce statistics issues for low energy gamma-ray beams towards the back of the detector.

Finally, as a further analysis, it would be interesting to compare the databases obtained by the IPHC scanning table with the ones measured with other scanning tables exploited by the AGATA collaboration by using the same capsule mounted in a given test cryostat moved from one place to the others. Such measurement is foreseen in the near future.

Fig. 2 :

 2 Fig. 2: Example of signals coming from a S-type AGATA detector plotted as a supertrace (see text for details).

  Figure 2 shows an example of a signal generated by a 662 keV gamma ray ( 137 Cs source) interacting in segment E3. The signal is presented as a supertrace, defined as a compact, continuous representation of all the leading edges coming from the 36 segments and the core of the detector. The signal is normalized with respect to the core signal amplitude. The signals corresponding to the core segment and the hit segment are defined as net charge signals, while the signals corresponding to other segments are called transient signals. The shapes of the net charge and transient signals depend on the position where the interactions take place, thus giving the detector position-sensitive properties.

Fig. 3 :

 3 Fig. 3: Schematic view of the scanning table.

Fig. 4 :Fig. 5 :

 45 Fig. 4: Distributions of the T 90 10 in three slices of (a) the 662 keV database (the histograms include part of sector A and sector B) and (b) the 779 keV database (sector B). The color scale is in nanoseconds. The 2 mm-thick (1 grid unit) slices considered are at Z C = 6 mm, 48 mm and 80 mm.

Fig. 6 :

 6 Fig. 6: Comparison of two pulse shapes taken at the same point (X C = 10 mm , Y C = 20 mm, Z C = 30mm) of the 662 keV database (black line, upper panels) and 779 keV database (red line, upper panels). The green lines in the lower panels show the residual value between the two pulses. The blue arrow shows the residual with the maximum value.

Fig. 7 :

 7 Fig. 7: Distributions of the maximum residuals (see text and figure 6), in percent of the full energies, between the pulses of the 662 keV and 779 keV databases. The data are relative to the 2 mm-thick (1 grid unit) database slices at Z C = 6 mm, 48 mm and 80 mm.

Fig. 8 :

 8 Fig. 8: Average maximum residual, as a function of Z C .Each point represents the mean value of the maximum residual distribution corresponding to a given database slice (see figure7). The graphs refers to the comparison, pair by pair, between the 662 keV , 779 keV and 1408 keV databases. In (a) the maximum residuals are calculated with the whole supertrace while in (b) only the transient pulses are used.

Fig. 9 :

 9 Fig. 9: Average maximum residual, as a function of Z C .Each point represents the mean value of the maximum residual distribution corresponding to a given database slice (see figure7). The graphs refers to the comparison, pair by pair, between the 122 keV , 245 keV , 344 keV 662 keV , 779 keV and 1408 keV databases. The data series corresponding to the comparison between a database of a given energy and all the databases of higher energy are regrouped and averaged (see text for detail). In (a) the maximum residuals are calculated with the whole supertrace while in (b) only the transient pulses are used.

Fig. 10 :

 10 Fig. 10: Examples of pulses used for the χ 2 selection interacting in segment B1. The pulse shown in (a) and (b) are relative to the 122 keV and 1408 keV datasets, respectively. The noise in the 122 keV signal is non-negligible, especially for the transient signals, while it has no influence on the 1408 keV signal.

Table 1 :

 1 Characteristics of the sources used by the IPHC scanning table. Branching ratios (BR) are found at[START_REF]The Lund/LBNL table of isotopes[END_REF]. The BR of the 152 Eu source are normalized with respect the total BR of the branches with the highest decay probability. The three sources have cylindrical shape. The dimension of the 241 Am and 137 Cs sources are 3 mm for both the diameter and height. The 152 Eu has a 3.2 mm diameter and a 2.3 mm height.

	Source Activity(GBq) E γ (keV)	BR
	241 Am	1.5	60	0.36
	137 Cs	1.85	662	0.851
	152 Eu	0.74	122	0.286
			245	0.076
			344	0.265
			779	0.129
			1408	0.210

Acknowledgements This work has been supported by the MIRION Technologies Canberra Company under the contract No 152521 and the OASIS ANR grant under the contract ANR-17-CE31-0026-02.


Raul Diaz-Salmeron

Balthazar Toussaint

Catherine Cailleau

Gilles Ponchel

Kawthar Bouchemal
email: kawthar.bouchemal@universite-paris-saclay.fr

D-S Author

B T Author

C C Author

G P Author

K B Author

Morphology-Dependent Bioadhesion and Bioelimination of Hyaluronan Particles Administered in the Bladder

Keywords: hyaluronan, nanoparticles, nanoplatelets, bioadhesion, bioelimination, bladder, glycosaminoglycans

Intravesical instillation allows for direct exposure of the urothelium to a drug. However, the therapeutic efficacy is often limited by rapid elimination of the drug from the bladder. In this investigation, we hypothesize that the morphology of nanoparticulate drug delivery systems could significantly impact bioadhesion and bioelimination from the bladder. Bioadhesion kinetics evaluated ex vivo on rat bladder mucosa demonstrate that particles with a flattened morphology, denoted as nanoplatelets, are rapidly attached to the mucosa at a lower concentration than nanospheres. The two particles have comparable surface potentials and equivalent volumes and are composed of hyaluronan, a nonsulfated polysaccharide that plays a significant role in restoring the bladder glycosaminoglycan layer. The bioaccumulation and bioelimination studied in vivo reveal that the nanoplatelets are eliminated from the rat bladders less rapidly than nanospheres. This investigation suggests that the bioadhesion of HA could be improved by optimizing particle morphology, opening new opportunities for the treatment of local urothelial diseases, such as interstitial cystitis/painful bladder syndrome, by restoring the loss of the glycosaminoglycan layer.

Introduction

Interstitial cystitis/painful bladder syndrome (IC/PBS) is a chronic and disabling disease responsible for pelvic pain and secondary infections. [1] This disease is generally undiagnosed, and patients often have symptoms for years before a firm diagnosis is made. [2] Although the etiology is unknown, theories have associated IC/PBS with inflammatory responses, autoimmune disorders, microbial infections, and bladder wall defects characterized by a loss of the glycosaminoglycan (GAG) layer on the urothelial cell surface. Due to GAG bladder defects, the permeability of the urothelium to toxins found in the urine becomes excessive, resulting from diffuse transmural inflammation. [3] Among the different GAGs present on the cell surface, hyaluronan (HA) is a nonsulfated polysaccharide that plays a significant role in restoring the bladder GAG layer. The instillation of HA solution (also known as Cystistat ® ) directly in the bladder through a urethral catheter is often used as a therapeutic strategy for IC/PBS to promote restoring the defective GAG layer at the urothelium surface. Although Cystistat ® was shown to have fewer side effects than other therapeutic strategies, [4] it has a long-term beneficial effect in only two-thirds of patients. [5] The reason for low efficacy could be due to several factors, including the dilution of the drug in the urine and drug elimination by spontaneous voiding. Indeed, the drug as a solution is held inside the bladder no more than two hours after intravesical administration. Therefore, the drug concentrations in the urothelium are expected to be low and transient. The drug concentration and the period of exposure of the drug to the urothelium are the two main parameters that showed importance for the efficacy of the drugs administered inside the bladder. In this context, efforts have been devoted to increasing the residence time of the active drug and maintaining an effective drug concentration close to the urothelium. [6] Different strategies have been envisioned by increasing either the viscosity or the bioadhesion of the drug delivery systems. [7] Thermosensitive hydrogels alone or containing adhesive polymers such as chitosan (CS), polycarbophil, [8] and hydroxypropylcellulose [9] have been used as a drug reservoir to extend the residence time in the bladder. However, high viscosity could increase the risk of urinary obstruction. Another strategy to increase the drug concentration and the residence time on the mucosa consists of encapsulating a drug inside bioadhesive particles that have a spherical morphology. [10] Bioadhesion was shown to improve by manipulating the surface properties or the size of the particles. [11] For the treatment of IC/BPS, as the active drug is HA itself, [12] designing carriers that can increase the polysaccharide concentrations close to the cell wall for restoring the GAG layer represents an unmet challenge for the scientific community. Indeed, HA shows low bioadhesive behaviors. [13] In particular, HA had a low adhesion time on the mucosa and a low value of the work of adhesion. [START_REF] Grabovac | Bernkop-Schnürch[END_REF] In this context, the strategy envisioned in this study was to consider particle morphology as a relevant parameter to improve bioadhesion. Particle morphology could affect the bioadhesion mechanism, [15] including the diffusion kinetics of the particle in the lumen (transport phase) and the ability of the particles to be immobilized by the glycome at the urothelium surface (attachment phase). [16] Herein, the bioadhesive behaviors of nonspherical HA particles, denoted as nanoplatelets (NPs), were investigated. NPs have flat surfaces and a high aspect ratio (AR), as characterized by atomic force microscopy (AFM) under liquid conditions (AR up to 20). [17] The NPs were formed by supramolecular and hierarchical selfassembly in water of HA hydrophobically modified with palmitic acid (HA-PA) and αcyclodextrin. [18] In this investigation, the bioadhesion behaviors of NPs were evaluated ex vivo and in vivo in rats after intravesical instillation. The results were compared to nanospheres (NSs) with a comparable surface composition and surface potential.

Results and discussion

Physicochemical characterizations

Two types of particles were investigated in this study, HA-NSs, and HA-NPs, the latter of which have a typical flattened morphology, hexagonal shape, and sharp edges. [17, 18c, 19] Physicochemical characterization of the HA-NPs and HA-NSs was previously performed in our research group by transmission electron microscopy (TEM), cryo-TEM, scanning electron microscopy (SEM), AFM imaging, and analysis of the surface potentials. [17, 18c, 19] HA-NPs dimensions determined by AFM under liquid conditions were in length and in height. [17] HA-NSs mean diameter evaluated from TEM and cryo-TEM was ~160 nm, while the height ranged from 130 to 150 nm, characterized by AFM. [20] The mean hydrodynamic diameter of CS spherical particles (CS-NSs), used as a positive control of bioadhesion, [21] was ~187 nm. [19] The diameter was characterized by quasi-elastic light scattering and AFM. [19] Herein, TEM observations used as a rapid characterization method confirm that the fluorescent labeling of the particles did not alter their morphology and size (Figure 1). The surface potentials characterizations revealed that after fluorescent labeling of HA-NPs and HA-NSs, the zeta potentials remained negative, and on the same order of magnitude (Table 1). Concerning CS-NSs, the labeling with rhodamine B decreased the zeta potential from to . Although the decrease in the zeta potential, previous investigations showed that the CS-NSs kept their bioadhesive behaviors after rhodamine B labeling. [21] In this investigation, suspension of HA-PA in water, which serves for the preparation of the HA-NPs by assembly with α-cyclodextrin, was used as a control of bioadhesion. We hypothesized that the amphiphilic nature of the compound could improve the interactions with the urothelium. It was previously demonstrated that HA hydrophobically modified with alkyl chains forms macromolecular micelles in water above the critical micelle concentration (37.3 μg mL -1 for a degree of substitution of 12.7%). [22] TEM observations of HA-PA suspension in water revealed the existence of ~200 nm round-shaped particles in which the hydrophobic alkyl chains could be inside the micelles surrounded by the hydrophilic HA backbone. [18c]

Ex vivo bioadhesion studies

Bioadhesion kinetics

Bioadhesion kinetics were studied to evaluate the impact of the particle morphology on the diffusion phase since this phase contributes to determining the time necessary to reach adhesion equilibrium. The kinetics of bioadhesion of HA-NPs and HA-NSs were investigated on freshly excised bladder mucosa of healthy rats. The bioadhesive behaviors of the NSs and the NPs were analyzed within the framework of adsorption theories. [16a, 16b] For this purpose, the weights of the particles remaining attached to the mucosal surface after three cycles of washing were determined after different contact times of the suspensions with the urothelium.

For this kinetic study, desorption was neglected. [16b] It was considered that the particles resisting the three washing cycles were irreversibly adsorbed. The bioadhesion kinetics were determined by keeping the other parameters influencing the bioadhesion constant (e.g., the surface of the mucosa, the temperature of the adhesion experiment, and particle concentration). The bioadhesion kinetics in Figure 2.a and 2.b showed that the HA-NPs adhered more quickly to the bladder mucosa than HA-NSs. The NSs rapidly reached adhesion saturation after 30 min. For the NPs, 60 min was necessary to reach saturation (Figure 2.a).

The initial phase of the adsorption kinetics up to 30 min of contact (Figure 2.b) was theoretically approached according to the diffusion model proposed by Durrer and coworkers. [16a, 16b] The rate constant (k) was calculated from the first phase of the adsorption kinetics following Equation 1. The results in Figures 2.c and 2.d show that the constant k was twofold higher for the HA-NPs than for the NSs ( and ). The constant k depicts the diffusion of the particles in the suspension medium, suggesting that the NPs diffused faster than the NSs to the urothelium.

The number of NS or NP layers attached to the rat bladder mucosa was calculated considering their dimensions and the theoretical number of NSs or NPs enabling them to cover the entire mucosal surface, which was equal to 0.87 cm 2 (Equation S1-S9 in the Supporting Information). This method of expressing the results suggested that the particles behave very differently and accumulate as multiple layers at the urothelial surface. The calculated number of particle layers was 26 times higher for HA-NPs than for HA-NSs (315 layers of HA-NPs vs. 12 layers of HA-NSs). The number of adhered NP layers was 11 times higher than that of CS-NSs (28 layers) used as a control bioadhesive particles (Table 2).

These results could be explained by the ability to form multiple layers of particles at the urothelial surface. After the formation of the first layers involving NP-cell surface interactions, additional layers of NPs could be progressively deposited over time until saturation.

Theoretical studies can be found in the literature showing that oblate and disc particles exhibited significantly higher adhesion probabilities than spherical particles. [23] The adhesion probability of rod-shaped particles with an AR of 5 was estimated to be approximately 20 times higher than that of spherical particles. Similarly, the oblate-shaped particles exhibited an almost ten times higher adhesion probability than the spherical particles. [23] Figure for HA-NSs. The surface of bioadhesion was 0.87 cm², and the bulk concentration of HA in NP and NS suspensions was maintained constant at 10 mg mL -1 . The time necessary to reach near-equilibrium bioadhesion was estimated at 120 min from the plateau observed in (a). The temperature was maintained at 25°C. n = 6 rats for each group.

Table 2. The number of attached particles (HA-NPs, HA-NSs, and CS-NSs) labeled with rhodamine B and the theoretical number of particle layers formed on 0.87 cm 2 of the bladder mucosa.

Formulation Adhesion contact time (min)

Number of attached particles ( )

Number of particle layers

HA-NPs

Effect of temperature on bioadhesion

In this set of experiments, the near-equilibrium time determined in previous kinetic studies was maintained at 120 min, and the bioadhesion of HA-NPs was studied at 25°C and 37°C.

The adhesive behaviors of HA-NPs were compared to other HA formulations (HA, HA-PA, and HA-NSs). The concentration of the polysaccharides in each formulation was kept constant at 10 mg mL -1 . According to the results in Figure 3.a (at 25°C) and 3.a' (at 37°C), the HA solution exhibited a low bioadhesion (0.41 g m -2 ), in agreement with previous findings. [13,[START_REF] Grabovac | Bernkop-Schnürch[END_REF] The amphiphilic HA-PA did not show any significant improvement in bioadhesion, compared to HA solution, regardless of the temperature of the experiment ( increased, the hydrogen bond stability decreased, but the stability of the hydrophobic interactions remained essentially constant. [24] A temperature increase results from different events, including the partial breaking of hydrogen bonds, an increase in hydrophobic interactions, and a decrease in the viscosity of the solution in which the particles are suspended.

Furthermore, increasing the temperature of the experiment results in an increase in the particle diffusivity, as previously reported for HA-NPs and HA-NSs. [20] However, at 37°C, HA-NPs moved faster in water than HA-NSs and the surface explored by the HA-NPs was 33 times higher than that explored by HA-NSs, explaining the high bioadhesion of the NPs. [20] Another explanation of the increase in bioadhesion with increasing temperature from 25°C to 37°C could be the partial dehydration of the thin glycome layer on the urothelial cells. Previous investigations highlighted the role of water movement in bioadhesion since the adhesive and cohesive nature of the mucus gel increased when the water content decreased. [25] for all the formulations was kept constant at 10 mg mL -1 . The temperature of the experiment was varied as 25°C and 37°C. Two asterisks and ns (no significant differences) denote p-values < 0.01 and > 0.05, respectively, after one-way ANOVA followed by Tukey's multiple comparison post-test comparing the mean between different groups. n = 6 rats for each group.

In vivo evaluation of the residence time of HA formulations after intravesical administration to rats

In this set of experiments, the residence time of HA-NSs and HA-NPs was evaluated in living healthy rats after intravesical administration. An HA-PA suspension and a solution of Cy5.5

were used as controls. The fluorescence of Cy5.5 in the bladders was monitored up to 7 days from the abdomen side of anesthetized rats using a real-time in vivo imager. Representative images of rat abdomen fluorescence of HA-NPs, HA-NSs, and HA-PA suspension at 24 h, 48 h, and 72 h are given in Figure 4. The fluorescence observed from the in vivo images (as presented in Figure 4) was then quantified by calculating the average radiant efficiency using the same threshold and the maximum and the minimum color scales for each image. Typical biphasic curves were obtained when the average radiant efficiency was plotted vs. time (Figure 5). Regardless of the formulation, the fluorescence signal first increased, followed by a second phase where the signal decreased. The increase in the fluorescence signal corresponds to the diffusion of the formulations in the bladder and their attachment to the urothelium surface. The decrease in the fluorescence signal corresponds to the bioelimination of the formulations by the natural voiding of the bladder. For NSs, the fluorescence increased until one-hour postadministration. Then, the signal continuously decreased until 3 h. The fluorescence was near zero after this period (Figure 5). Those results agreed with ex vivo experiments revealing that HA-NSs had low bioadhesion to the urothelium (Figure 3.c and 3').

For the HA-PA suspension, the fluorescence signal was higher than that for HA-NSs.

Contrarily to HA-NSs, the fluorescence signal of HA-PA was still present after 72 h postadministration (Figure 4.b). The higher signal of HA-PA observed in vivo could be due to its higher flexibility and amphiphilicity compared to HA-NSs, which could be favorable for their diffusion in the urine and interaction with the urothelium surface. However, the high signal of HA-PA in vivo was unexpected regarding the ex vivo results, which showed that a small amount of HA-PA remained attached to the urothelium. The discrepancies in the ex vivo and in vivo results could be explained by the ex vivo experimental conditions of bioadhesion, which do not mimic what happens in vivo. We hypothesize is that HA-PA suspension could attach to the urothelium, but the three cycles of washing performed for ex vivo experiments may be too drastic compared to what happens in vivo, resulting from HA-PA removal.

Concerning HA-NPs, ex vivo bioadhesion experiments agreed well with in vivo data.

Those results could be explained by the fast diffusion of HA-NPs through aqueous media, [20] and firm attachment to the urothelial cells through receptor-mediated mechanisms. [17] Several studies revealed that micro-and nanoparticles that have a high AR were taken up by cells faster and in large amounts than spheres. For a review article on shape-dependent endocytosis, see reference. [26] Although the mechanism of fast and higher cell uptake of particles with high AR is not well elucidated, it is believed that the high particle surface area could lead to strong interaction with the cell membrane. [27] The flat surface of the particles could result from strong multivalent interactions characterized by simultaneous binding of multiple ligands to multiple receptors. [28] Both experimental and theoretical works unambiguously indicate that ligand/receptor interaction strength and specificity could be improved by multivalency. [29] Besides the high AR of HA-NPs, the sharpness of the edges could be favorable for their internalization by cells. Previous works revealed that particles with sharp edges or irregular surfaces were faster internalized by cells than spheres or discs with a smooth surface. [30] Altogether, it could be concluded that due to their specific physicochemical properties, including the morphology, and biophysical behaviors in the bladder, the HA-NPs were able to counteract micturition and dilution after in vivo administration. ) or a solution of free Cy5.5 (white). One, two, three, and four asterisks denote p-values < 0.05, < 0.01, < 0.001, and < 0.0001, respectively, after the two-way Kruskal-Wallis test followed by Tukey's multiple comparison post-test. n = 6 rats for each group.

Conclusion

In summary, in this investigation, we quantitatively studied the effect of particle morphology on bioadhesion and bioelimination from rat bladder after intravesical administration. Herein, we demonstrated that a flat particle morphology was favorable for both the diffusion phase and the attachment phase of bioadhesion. Furthermore, the improvement in the bioadhesive behaviors of HA particles by optimizing their morphology resulted in lower bioelimination from the mucosal epithelium of live rats. However, we also highlighted discrepancies between ex vivo and in vivo results for HA hydrophobically modified with palmitic acid, which could be due to the three cycles of washing performed ex vivo. Those conditions could not mimic in vivo situations leading to material removal over washing cycles. Future investigations should be performed ex vivo by quantifying the polysaccharide remaining attached to the urothelium by varying the number of washing cycles. This optimization will lead to identifying the experimental conditions for a better correlation of ex vivo bioadhesion experiments with in vivo data. Finally, future investigations will be focused on the evaluation of the ability of HA formulations to cure bladder diseases such as IC/PBS.

Experimental section

Materials

HA ( according to the manufacturer) was purchased from LifeCore Biomedical (Minnesota, USA). Cyanine 5.5-NH 2 (Cy5.5-NH 2 , λ ex 679 nm, λ em 705 nm) was purchased from Lumiprobe (Montluçon, France). Cy5.5-labeled-O-palmitoyl hyaluronan (HA-PA) was synthesized and characterized as published elsewhere. [18c] CS ( according to the manufacturer) was purchased from Amicogen (Seoul, Korea).

Isobutyl cyanoacrylate monomer was obtained from WPI (Friedberg, Germany). PolyFluor 

(methacryloxyethyl thiocarbamoyl rhodamine B) was purchased from Biovalley

Polyscience Inc ® (Warrington, USA). Acetonitrile was obtained from Carlo Erba ® (Val de Reuil, France). NaCl, α-cyclodextrin, phosphate buffer saline (PBS, ), NaOH (1 N), sodium dodecyl sulfate, and nitric acid were purchased from Sigma Aldrich (St.

Quentin Fallavier, France). A dialysis membrane with a molecular weight cutoff was purchased from Spectra/Por (Biovalley, Marne la Vallée, France).

Milli-Q ® water was used for all the experiments (resistivity 18.2 MΩ.cm at 21°C, Millipore purification system, Millex, SLAP 0225, Millipore, France).

Methods

Preparation and characterization of NSs, NPs, and controls

NSs preparation: NSs were prepared by emulsion polymerization by adapting previous works on polysaccharide NSs. [19,31] Non-labeled poly(isobutyl cyanoacrylate) NSs coated with HA (denoted HA-NS) or CS (CS-NS) were prepared by dissolving of HA or CS (0.069 g) in dilute nitric acid (5 mL) at a concentration of 0.2 mol L -1 under magnetic stirring in a glass tube at 40°C for 30 min. After HA dissolution, isobutyl cyanoacrylate monomer (0.250 mL) was added under vigorous magnetic stirring. The reaction was allowed to continue at 40°C under stirring for 24 h. The sample was cooled to room temperature. Then, NSs were purified first by filtration through a 1 µm filter to remove polymer aggregates and then by dialysis twice for 30 min, twice for 60 min, and once overnight against water (1 L). Finally, the pH of each NS suspension was adjusted to 6.5 with NaOH (1 N). HA NSs labeled with Cy5.5 were prepared according to the same method. In this case, native HA was previously coupled to Cy5.5 fluorescent dye, [18c] and polymerized with IBCA under vigorous magnetic stirring. HA or CS NSs labeled with rhodamine were prepared similarly, except that 5 min after the addition of isobutyl cyanoacrylate, a solution of PolyFluor ® 570 (1 mL) (a fluorescent competitive monomer at a 2 mg mL -1 concentration in acetonitrile) was added. The reaction was allowed to continue at 40°C under stirring for 24 h. Then, the NSs were purified by filtration followed by dialysis according to the process described above for non-labeled NSs.

NPs preparation: HA NPs labeled with Cy5.5 were prepared by adding α-cyclodextrin solution (500 mg) in water to a small vial containing Cy5.5-labeled-O-palmitoyl hyaluronan (10 mg). α-cyclodextrin solution was prepared in water at a 100 mg mL -1 concentration. The weight was then brought up to 1 g with the addition of water and mixed at room temperature under magnetic stirring for 72 h, as previously optimized. [18c] Preparation of controls: Hydrophobically modified HA suspensions in water (HA-PA)

were prepared according to the same protocol used for NPs, without adding α-cyclodextrin.

Solutions containing native HA were freshly prepared by mixing HA (10 mg) in water (1 mL).

Noteworthy, the stability of NPs and NSs over storage at variable temperatures and in biological media was already demonstrated. [32]

Physicochemical characterizations

The zeta potentials of NSs and NPs were calculated at 25°C by measuring the electrophoretic mobility using a Zetasizer (Nanoseries Nano-ZS, Malvern Instruments, France). [18c, 33] The scattered angle was 173°. The suspensions were diluted to (1:33 v/v) in NaCl (1 mM).

Measurements were performed on three independent formulations. The morphology of NSs and NPs labeled or not with fluorescent dyes was characterized by TEM according to previous works. [18c, 33] The images were obtained at 80 kV transmission using a transmission electron microscope (JEOL 1400) coupled to TEM Domain Center software. The suspensions were diluted in water (1/30 and 1/100 for NPs and NSs, respectively). The dilutions were optimized to allow the observation of particles with good contrast. Then, dilute suspensions (5 µL) were placed on a carbon grid. After 3 min of drying at room temperature, NPs were directly visualized under the microscope, while NSs were stained with a phosphotungstic acid solution before observation.

Bioadhesion ex vivo assays

Preparation of the bladder mucosa: Fresh bladders were removed from euthanized female Wistar rats (250-300 g, Janvier Labs, Saint Berthevin, France) as described in the Supporting Information. Then, the bladders were excised and opened lengthwise and longitudinally. The bladders were then spread on an aluminum plate. A tight rubber seal with a diameter of 0.87 cm 2 was positioned on the mucosa. Then, a second aluminum plate with a cylindrical slit in the center, able to accommodate 700 µL, was coupled to the tight seal and the first plate (Figure S1 in the Supporting Information).

Bioadhesion kinetics: Suspensions of fluorescently labeled HA-NPs (labeled with Cy5.5)

and spherical particles (labeled with rhodamine B) (600 µL at a 10 mg mL -1 concentration)

were put in contact with a 0.87 cm 2 delimited bladder mucosa at different time intervals (2.5, 5, 10, 30, 45, 60 and 120 min) to determine the time necessary to reach an equilibrium adhesion plateau. The temperature was maintained at 37°C.

Investigation of the impact of particle morphology on Bioadhesion:

Once the attachment equilibrium was determined, the impact of particle morphology on the bioadhesion was determined after two hours of incubation. The tested formulations were HA-NPs and HA-NSs.

The temperature was varied from 25°C to 37°C. The bioadhesion results were compared to different controls: a native HA solution, hydrophobically modified HA suspension (HA-PA), and CS-NSs suspension (used as a positive control of bioadhesion). The HA concentration was maintained at 10 mg mL -1 regardless of the formulation.

Quantification of attached materials:

At the end of bioadhesion experiments, the excess fluorescently labeled formulations were aspirated using a pipette and rinsed three times with a PBS solution (600 µL) for 1 min each rinsing cycle to eliminate unattached materials (HA-NPs, HA-PA, HA, HA-NSs or CS-NSs). Subsequently, the bladder tissue, including the attached materials, was harvested, and solubilized in 2.5 mL of a solution of NaOH (1 wt%) and sodium dodecyl sulfate (2 wt%). Samples were ultrasonicated for two hours and left overnight at room temperature under magnetic stirring. The attached materials were quantified from the absorbance signal and UV spectra of extracted samples containing the polymers (Lambda 25 UV/VIS Spectrometer, PerkinElmer, Waltham, MA, USA) (Supporting Information, Figures S2 to S7). The assay of HA associated with urothelial tissues was based on the determination of the concentrations of the fluorescent probes (rhodamine and Cy5.5).

Since the medium was complex, the second derivatives of the UV spectra were used to isolate the spectral variations related to these molecules from the signal originating from the media.

The spectra were analyzed by the Savitzky-Golay algorithm, and convolution coefficients were those needed to perform a quadratic/cubic polynomial adjustment with a window size of 15 values. The wavelengths ranged from 400 nm to 700 nm for rhodamine B and from 500 nm to 800 nm for Cy5.5. The amplitude between the minima and the maxima observed on second-derivative spectra was used as a signal proportional to the concentration. For rhodamine, the minimum signal was observed at 545 nm, and the maximum was observed at 566 nm. For Cy5.5, the minimum and the maximum signals were observed at 679 nm and 705 nm, respectively. The experimental data were compared to the corresponding calibration curves generated under the same experimental conditions (Supporting Information Figures S3, S5, and S7). The results were expressed as the amount of the attached polymer per apparent surface of the bladder mucosa (g.m -2 ). For mechanistic analysis, the number of attached NSs and NPs was calculated and expressed as the number of theoretical layers covering the exposed surface of the mucosa. The rate constants k characterizing the adsorption kinetics were estimated according to Durrer et al., [16a, 16b] from the plot of vs. time t following Equation 1:

(

where C0, Ct, and Cf are the particle concentrations (mg mL -1 ) at time 0, at time t, and at equilibrium, respectively. The concentrations Ct and Cf were calculated as the difference between the initial material concentration in the suspension medium (10 mg mL -1 ) and the adsorbed material amount at time t and at equilibrium, respectively. The dimensions of the bioadhesion system are as follows:

In vivo evaluation of the residence time of the formulations in rat bladders

-The thickness of each plate rubber: 7 mm -The width of the plate rubbers: 3.9 cm -The length of the plate rubbers: 8 cm -The diameter of the seal = 10.5 mm = 1.05 cm -The surface of adhesion:

Calibration curves

Calibration curve of hyaluronan poly(isobutyl cyanoacrylate) nanospheres labeled with rhodamine B

Step 1: Preparation of samples containing NS at different concentrations: a) First, a sample of 10 mL of hyaluronan nanospheres (HA-NS) labeled with rhodamine B or Cynaine5.5 (Cy5.5) was prepared as described in the main text. The final concentration of nanospheres (NSs) was as determined by gravimetric analysis. b) The stock sample was first diluted to 10 mg mL -1 with water, then diluted again with water to reach the following concentrations: 0.75, 0.50, 0.25, 0.10, 0.05, and 0.01 mg mL -1 .

Step 2: NSs were then degraded to release the fluorescent dye contained in the particle core as described below:

a) The samples were ultrasonicated for 2 h and magnetically stirred overnight. b) Samples were diluted at 1/4 with a solution of NaOH and sodium dodecyl sulfate (SDS) (250 µL NS sample mixed to 750 µL of NaOH/SDS solution). c) Samples were then diluted at 1/4 by adding the extraction solvents (250 µL NS sample mixed to 250 µL of ethanol and 500 µL of acetonitrile). Briefly, a suspension composed of bladder tissue digested in 1% NaOH/ 2% SDS (250 µL) was added to small vials. Then, protein precipitation was performed with the addition of 250 µL of ethanol followed by 30 s of vortex mixing and 500 µL of acetonitrile followed by 30 s of vortex mixing. [1] After being left 5 min at room temperature to optimize and allow total protein precipitation, samples were centrifuged at 2200 g for 10 min at room temperature, and 800 µL of the supernatant were collected into 1.5 mL-polypropylene tubes. d) The concentrations of rhodamine B and Cy5.5 were determined by a UV/VIS spectrophotometer (Perkin Elmer, Waltham, MA, USA) at 552 nm and 680 nm, respectively. e) Data were analyzed by transforming the absorbance spectra into second derivatives.

Typical spectra were reported in Figure S2 and the calibration curve in Figure S3.

Calibration curve of chitosan poly(isobutyl cyanoacrylate) NSs labeled with rhodamine B

The calibration curve was obtained following the same steps (from step (a) to step (f)) as described in the previous section 2.1.

Figure S4.

Second-order derivative spectra of NS suspensions composed of NS core coated with CS and labeled with rhodamine B (CS-NS). NS concentration ranged from 0.0025 to 0.1875 mg mL -1 . Step 2: The particles were then degraded to release the fluorescent dye:

a) The samples were ultrasonicated for 2 h and magnetically stirred overnight. b) Samples were diluted at 1/4 by adding the extraction solvents (250 µL of particle sample mixed with 250 µL of ethanol and 500 µL of acetonitrile). Briefly, a suspension composed of bladder tissue digested in a mixture of NaOH/SDS (1/2%) (250 µL) was added to small vials. Then, protein precipitation was performed with the addition of 250 µL of ethanol followed by 30 s of vortex mixing and 500 µL of acetonitrile followed by 30 s of vortex mixing. [2] After being left 5 min at room temperature to optimize and allow total protein precipitation, samples were centrifuged at 2200 g for 10 min at room temperature, and 800 µL of the supernatant were collected into 1.5 mL-polypropylene tubes. c) The concentrations of Cy5.5 were determined by a UV/VIS spectrophotometer at 552 nm and 680 nm, respectively. d) Data were analyzed by transforming the absorbance spectra into second derivatives.

Typical spectra were reported in Figure S6 and the calibration curve in Figure S7.

Figure 1 :

 1 Figure 1: TEM images of non-labeled particles (a, d, g), Cy5.5-labeled particles (b, e, h), and rhodamine B-labeled particles (c, f). The formulations were: HA-NPs (a, b, c), HA-NSs (d, e, f), or CS-NSs (g, h). Scale bars: 500 nm for (a, b, c, f, and h), 200 nm for (e), and 100 nm for (d and g).

Table 1 :

 1 Zeta potentials () of NPs and NSs composed of hyaluronan (HA) or chitosan (CS). The particles were labeled with Cyanine5.5* (Cy5.5) or rhodamine B**.

2 .

 2 Figure 2. (a) Bioadhesion kinetics of HA-NPs () and HA-NSs (). The contact time of NPs or NSs on the mucosa varied from 2.5 min to 120 min for (a) and from 2.5 min to 30 min for (b). Dash points in panels (c) and (d) represent theoretical plots for the experimental data given in (a) for HA-NPs () and for HA-NSs () following Equation 1. The rate constants were for HA-NPs andfor HA-NSs. The surface of bioadhesion was 0.87 cm², and the bulk concentration of HA in NP and NS suspensions was maintained constant at 10 mg mL -1 . The time necessary to reach near-equilibrium bioadhesion was estimated at 120 min from the plateau observed in (a). The temperature was maintained at 25°C. n = 6 rats for each group.

Figure 3 .

 3 b at 25°C and 3.b' at 37°C). In contrast, the formulation of HA-PA as HA-NPs by self-assembly with cyclodextrin resulted in the adhesion of a high amount of HA at the urothelium surface at both temperatures. The attached HA was 4.93 g m -2 at 25°C (Figure3.d) and 6.21 g m -2 at 37°C (Figure3.d'). Interestingly, at 37°C, the bioadhesion of HA-NPs was 2.38-times higher than that of the bioadhesive control CS-NSs (Figure3.c'). Concerning HA-NSs, the adhesion at 25°C was as low as HA solution and HA-PA suspension (Figure3.c). Increasing the temperature from 25°C to 37°C induced an increase in the bioadhesion of HA-NSs from 0.34 g m -2 to 1.23 g m -2 . However, the attached HA remained five-fold lower for HA-NSs than that of HA-NPs at 37°C (Figure3.c'). Therefore, it could be concluded that the HA-NPs exhibited the highest bioadhesion compared to the other formulations. The adhesion of particles studied in this investigation on the urothelium involves several types of noncovalent bonding, such as hydrogen, hydrophobic, and electrostatic interactions. When the temperature

Figure 3 .

 3 Figure 3. Bioadhesion of HA formulations (HA (a, a'), HA-PA (b, b'), HA-NSs (c, c'), and HA-NPs (d, d')) was determined after 120 min of contact with the rat bladder mucosa. CS-NSs in (e, e') were used as a positive control for bioadhesion. The initial concentration of the polysaccharides (HA or CS) for all the formulations was kept constant at 10 mg mL -1 . The temperature of the experiment was varied as 25°C and 37°C. Two asterisks and ns (no significant differences) denote p-values < 0.01 and > 0.05, respectively, after one-way ANOVA followed by Tukey's multiple comparison post-test comparing the mean between different groups. n = 6 rats for each group.

Figure 4 .

 4 Figure 4. IVIS Lumina scans of rat abdomens after intravesical administration of Cy5.5-labeled HA formulations (HA-NSs (a), HA-PA (b), and HA-NPs (c)). Views were obtained at 24 h, 48 h, and 72 h after intravesical instillation. The concentration of the HA in the formulations was maintained at 10 mg mL -1 .

Figure 5 .

 5 Figure 5. (a) Average radiant efficiency (RE) of rat bladder abdomens after the administration of Cy5.5-labeled HA formulations: HA-NPs (), HA-PA (), or HA-NSs (). A solution of free Cy5.5 () was used as a negative control. Quantitative analysis of the bladders was performed with the same threshold and the same region of interest (ROI) from 1 h to 168 h postadministration. (b) A magnification of panel (a) by focusing on 1 to 24 h postadministration. The four columns in (c) represent the average RE of the rat's abdominal regions after 24, 48, and 72 h postadministration of the formulations: HA-NPs (black), HA-PA (dark grey), or HA-NSs (light grey) or a solution of free Cy5.5 (white). The two columns in (d) represent a magnification of the average RE of HA-NSs (light

Animals:Figure S1 .

 S1 Figure S1. (a) Picture of the lower side of the metallic plate with a rubber (10.5 mm in diameter). (b) picture of the Bioadhesion system with the seals.

Figure S2 .

 S2 Figure S2.Second-order derivative spectra of NS suspensions composed of poly(isobutyl cyanoacrylate) core coated with HA and labeled with rhodamine B (HA-NS). NS concentration ranged from 0.0025 to 0.1875 mg mL -1 .

Figure S3 .

 S3 Figure S3. Calibration curve of NS suspensions composed of HA-NS labeled with rhodamine B.

Figure S5 . 2 . 3 . 5 Step 1 :

 S52351 Figure S5. Calibration curve of NS suspensions composed of poly(isobutyl cyanoacrylate) core coated with CS and labeled with rhodamine B.

Figure S6 .

 S6 Figure S6. Second-order derivative spectra of HA-NP suspensions labeled with Cy5.5 at different concentrations.

  Concentration of HA in the nanoplatelets (mg.mL -1 ) Amplitude d''(A)/d( l ) y = 0.008027 x + 6.533e-005

Acknowledgements (S9)

Supporting Information

Supporting Information is available from the Wiley Online Library or from the author.

Conflict of Interest

The authors have no conflicts of interest.

The table of contents entry

Intravesical instillation is limited by rapid drug elimination. Herein, we demonstrate that the flat morphology of hyaluronan particles significantly improves bioadhesion and decreases bioelimination from the bladder. Bioadhesion kinetics are evaluated ex vivo on rat bladder mucosa, while particle bioelimination was studied in vivo using a real-time imager system.

This investigation could open opportunities for efficient drug delivery

Data analysis and details of calculations

From bioadhesion experiments, the mass of attached N particles per m² of bladder mucosa (m T , g.m -²) was calculated from the calibration curves.

NSs

The total number of attached NSs (N NS ) was calculated following the Equation S1-S3, where ρ is the bulk density of preparations ( 1.2 g.cm -3 determined by a pycnometer device at 20°C), is the total volume occupied by N NSs and , and are the mean hydrodynamic radius and diameter of NSs.

It is possible to calculate the number of layers that theoretically cover the 0.87 cm 2 of the bladder surface: (S4) (S5)

NPs

The same calculations were used for HA-NPs suspensions except that the surface of the hexagons was calculated from the equation:

, where is the hexagon side determined from our previous work. [3] (S1')

corresponds to the width of the NPs determined from our previous work. [3] It is also possible to calculate the number of layers that theoretically cover the 0.87 cm 2 of the bladder surface: (S8)