Ming Zhong

Maelle Torterotot

Trevor A Branch

Kathleen M Stafford

Jean-Yves Royer

Rahul Dodhia

Juan Lavista Ferres

Detecting, classifying, and counting blue whale calls with Siamese neural networks

Keywords: Machine learning, bioacoustics, Convolutional Neural networks

published or not. The documents may come

I. Introduction

A. Background

The blue whale Balaenoptera musculus is the largest of the mysticete (baleen) whales, with lengths exceeding 30 meters [START_REF] Mcclain | Sizing ocean giants: patterns of intraspecific size variation in marine megafauna[END_REF]. They are endangered worldwide, although their population status differs from one location to another. The Indian Ocean, particularly its southern extent, is one of the oceans with the greatest blue whale acoustic diversity [START_REF] Stafford | Seasonal detection of three types of "pygmy" blue whale calls in the Indian Ocean[END_REF]. Blue whale subspecies present in the Indian Ocean include the Antarctic blue whale (Balaenoptera musculus intermedia) and the pygmy blue whale (B. m. brevicauda); and pygmy blue whales are further separated into multiple acoustic populations and possibly additional subspecies (e.g., B. m. indica). In the absence of extensive genetic data from Indian Ocean blue whales to determine speciation, the different song types of Indian Ocean blue whales, which are acoustically somewhat geographically distinct, are used to broadly define populations of blue whales. Prior to intensive commercial whaling beginning in the early 1900s, blue whales were once abundant in the Southern Hemisphere. This was particularly true in the Southern Ocean, where as many as 239,000 Antarctic blue whales congregated in summer to feed [START_REF] Branch | Evidence for increases in Antarctic blue whales based on Bayesian modelling[END_REF], primarily on Antarctic krill Euphausia superba.

Despite being the largest animal ever to exist on Earth, there is relatively little known about the distribution and migration of blue whales in the Indian Ocean. The Antarctic blue whale has been declared as "Critically Endangered" and pygmy blue whales are listed as "Data Deficient" by the International Union for the Conservation of Nature [START_REF] Cooke | Balaenoptera Musculus[END_REF] due to lack of sufficient data to assess their conservation status. Monitoring blue whales remains a challenge because of the relative scarcity of individuals as well as their pelagic distribution which largely encompasses remote and inaccessible regions of the ocean. Moreover, identifying pygmy from Antarctic blue whales by visual observation is difficult, as they look almost identical at sea, despite the smaller length of pygmy blue whales (Ichihara, 1966). Thus, most of the knowledge about blue whales in the Indian Ocean comes from whaling data (Branch et al., 2007(Branch et al., , 2009)), and from passive acoustic monitoring (Samaran et al., 2010a(Samaran et al., , 2013;;[START_REF] Stafford | Seasonal detection of three types of "pygmy" blue whale calls in the Indian Ocean[END_REF]Leroy et al., 2016;Dréo et al., 2018;[START_REF] Torterotot | Distribution of blue whale populations in the Southern Indian Ocean based on a decade of acoustic monitoring[END_REF]. Such monitoring efforts are widespread in the world's oceans and often result in many terabytes of digital data, which requires big data analysis efforts to analyze efficiently and robustly. Blue whale signals are particularly good candidates for this type of observation, because of their repetitive, long (more than 15 s), loud (more than 180 dB ref 1 μPa at 1 m) and low frequency (20-100 Hz) highly stereotyped calls (Cummings and Thompson, 1971). Blue whale song calls (hereafter calls) vary from one region to another and have been used to define acoustic populations which are geographically distinct [START_REF] Mcdonald | Biogeographic characterization of blue whale song worldwide: using song to identify populations[END_REF][START_REF] Stafford | Seasonal detection of three types of "pygmy" blue whale calls in the Indian Ocean[END_REF]. Taking advantage of the temporal and frequency differences among song units, we used machine learning methods to automatically detect, classify and count blue whale calls from a subset of acoustic recordings from the southern Indian Ocean. By developing a robust machine learning methodology to identify when and where each population occurs, this opens up a pathway to allocate historical catches and recent abundance estimates among the various populations, allowing us to assess the current status of each identified acoustic population. Such status assessments form the basis for appropriate management efforts to conserve these populations for the future.

B. Motivation for the work

Technological advances in the past two decades have allowed researchers to record and archive passive acoustic data from remote underwater ocean moorings. The mooring deployments can be from months to years with acoustic data archived on digital media in the instrument either continuously or on a duty cycle. The acoustic data is retrieved periodically resulting in up to many terabytes of data collected for each site. It is impractical to analyze all of the data manually or in real time. The way to efficiently process such a large volume of acoustic recordings has been the subject of many efforts in the past twenty years and has resulted in a rich body of literature on automated detection methods, particularly for blue whales (e.g., [START_REF] Stafford | Antarctic-type blue whale calls recorded at low latitudes in the Indian and eastern Pacific Oceans[END_REF], 2011[START_REF] Mouy | Automatic recognition of fin and blue whale calls for real-time monitoring in the St. Lawrence[END_REF][START_REF] Širović | Blue and fin whale acoustic presence around Antarctica during 2003 and 2004[END_REF][START_REF] Gavrilov | Acoustic detection and long-term monitoring of pygmy blue whales over the continental slope in southwest Australia[END_REF].

Detection methods based on bespoke detectors and conventional machine learning classifiers are the most prominent methods used during the last two decades [START_REF] Kowarski | A review of big data analysis methods for baleen whale passive acoustic monitoring[END_REF]. For example, a non-parametric classification tree analysis (CART) and a Random Forest analysis were implemented to provide robust results to classify 34 identifiable call types of beluga whale vocalizations from the eastern Beaufort Sea population [START_REF] Garland | Beluga whale (Delphinapterus leucas) vocalizations and call classification from the eastern Beaufort Sea population[END_REF].

To investigate the vocal repertoire of Southeast Alaskan humpback whales, three classification systems were used, including aural spectrogram analysis, statistical cluster analysis, and discriminant function analysis, to describe and classify vocalizations; and a hierarchical acoustic structure was identified to classify vocalizations into 16 individual call types nested within four vocal classes [START_REF] Fournet | Repertoire and classification of non-song calls in Southeast Alaskan humpback whales (Megaptera novaeangliae)[END_REF]. For blue whale signals in particular, most detection methods have been based on detection either in the time domain (e.g., matched filtering, [START_REF] Stafford | Long-range acoustic detection and localization of blue whale calls in the northeast Pacific Ocean[END_REF] or in the frequency domain (spectrogram correlation, e.g., Širović et al. 2009) although more recent efforts have involved more novel methods, including sparse representation of signals (e.g., [START_REF] Socheleau | Automated detection of Antarctic blue whale calls[END_REF][START_REF] Torterotot | Detection strategy for long-term acoustic monitoring of blue whale stereotyped and non-stereotyped calls in the Southern Indian Ocean[END_REF].

More recently, the rapid development of artificial intelligence and deep learning algorithms provide another approach for intelligent classification and prediction. In classifying animal sounds, deep neural networks (DNN) methods have progressed tremendously with accessibility to large training data and increasing computational power. Using spectrograms generated from raw audio recordings as input, researchers have applied Convolutional Neural Networks (CNN), either by training the model from scratch, or using transfer learning with pre-trained model weights, to classify calls from different species [START_REF] Bergler | ORCA-SPOT: An automatic killer whale sound detection toolkit using deep learning[END_REF][START_REF] Yang | Classification of odontocete echolocation clicks using convolutional neural network[END_REF][START_REF] Zhong | Beluga whale acoustic signal classification using deep learning neural network models[END_REF][START_REF] Kirsebom | Performance of a deep neural network at detecting North Atlantic right whale upcalls[END_REF]. Another approach is Recurrent Neural Networks (RNN), which utilizes temporal information of animal calls for classification tasks [START_REF] Ibrahim | Automatic classification of grouper species by their sounds using deep neural networks[END_REF][START_REF] Shiu | Deep neural networks for automated detection of marine mammal species[END_REF].

While the deep neural network models CNN and RNN have achieved great success in many classification tasks, they have limitations that typically these models rely on large size of datasets to train millions of parameters. For classification purposes of audio recording classifications, all we need from these models is good embedding representations for spectrograms. For same classes, we would expect the learned embeddings to be close to each other in the latent space; for different classes, the learned embeddings are far apart. In this paper, we proposed using Siamese Neural Networks (SNN) [START_REF] Koch | Siamese neural networks for one-shot image recognition[END_REF] as an alternative of widely used CNN to conduct classifications, especially when the size of training data is limited. Siamese Networks focuses on learning embeddings in the deeper layer that place the same classes close together. Hence, it can learn semantic similarity effectively.

II. Data

A. Data Sources and Data Annotation

The acoustic data used in this study was recorded by the OHASISBIO (Observatoire Hydro-Acoustique de la SISmicité et de la Biodiversité) hydrophone network [START_REF] Royer | OHASISBIO -Hydroacoustic Observatory for the Seismicity and Biodiversity in the Indian Ocean[END_REF], located in the Southwest Indian Ocean (see Fig. 1). The network was deployed in December 2009 and was still recording as of the date of this publication. To provide a testing and training dataset, we manually annotated signals from four populations of blue whales (Antarctic blue whale and three pygmy blue whale populations) using data from 5 of 11 available mooring sites (see Table I, Figure 2). Originally, song types were named based on the first location where calls were recorded. More recently, with the realization that the extent of each population is greater than originally understood, this naming convention has been updated (IWC 2020) to refer to broad geographical regions as follows (with abbreviation and first location): central Indian Ocean (CIO, Sri Lanka), southwest Indian Ocean (SWIO, Madagascar), southeast Indian Ocean (SEIO, Australia/Indonesia), and Antarctic blue whales. In addition, there are two additional song types of pygmy-type blue whales not yet reported on the OHASISBIO network: southwest Pacific Ocean (SWPO, New Zealand), and northwest Indian Ocean (NWIO, Oman, [START_REF] Cerchio | A new blue whale song-type described for the Arabian Sea and Western Indian Ocean[END_REF].

We follow this regional naming convention throughout the present study (Antarctic, SEIO, SWIO, CIO).

B. Data for modeling

For all four acoustic populations of blue whales, calls range from 6 to 40 seconds duration.

Using custom written scripts in Python 3.6, spectrograms were produced from audio files (with NFFT = 1024 and 75% overlap, Hanning window). Each spectrogram was generated from a 240-s audio segment that contained either one or multiple annotated blue whale calls and was resized as 224 pixels by 224 pixels with RGB channels (Fig. 3). During the annotation process, we only focused on the presence of one blue whale population in each acoustic file. However, as part of the temporal and geographical distributions overlap among these blue whale populations, their acoustic co-occurrence is common. As a result, for each extracted spectrogram, its corresponding label (the name of blue whale population, and the number of calls associated with the spectrogram) only represented the presence of that particular population but did not indicate absence of the other three populations.

FIG. 3. Example spectrograms from different acoustic populations of blue whale in the Indian

Ocean that illustrate the range of signal-to-noise ratios in the data from loudest to faintest from left to right. Row 1: Antarctic; row 2: SEIO; row 3: SWIO; row 4: CIO.

While the spectrograms extracted from annotated audio segments corresponded to positive labels (i.e., presence of a blue whale population with at least one call), we also extracted spectrograms that associated with negative labels (i.e., absence of a blue whale population with no call). For each of the four populations, we randomly selected audio clips that did not contain any annotated calls.

In total, we extracted 12,155 spectrograms (see Table II for breakdown by population), each representing a 240-second-long audio clip. These spectrograms, along with their associated labels, were used as input for building classification models.

III. Approaches

We assessed the performance of Convolutional Neural Networks (CNN) and a newer technique, Siamese Neural Networks (SNN), to determine which best identified and classified blue whale calls.

A. Classification Models using Convolutional Neural Network (CNN)

Convolutional Neural Networks (CNN) have been widely used for image classification tasks, and their success has also been proven in bioacoustic classification applications [START_REF] Bianco | Machine learning in acoustics: Theory and applications[END_REF]).

Here we used the DenseNet-201 architecture [START_REF] Huang | Densely connected convolutional networks[END_REF]) as a baseline to classify calls of the four blue whale populations, and to count the number of calls in each 240-s spectrogram.

DenseNet was developed specifically to improve the declined accuracy caused by the vanishing gradient in high-level neural networks and has the advantage of improving feature propagation both in forward as well as backward fashion. In a DenseNet architecture, each layer is connected to every other layer and obtains additional inputs from all preceding layers, and then passes its own feature-maps to all subsequent layers.

B. Classification Models using Siamese Neural Network (SNN)

Siamese Neural Networks (SNN) are a class of neural network architectures that contain two or more identical subnetworks. "Identical" here means that they have the same configuration with the same parameters and weights. Parameter updating is mirrored across both sub-networks.

SNN focuses on learning image embeddings in the deeper layers that place the same classes close together. Hence, it can be used to measure the similarity of the inputs by comparing their feature vectors and make decisions on whether the two images belong to the same category or different categories.

Since training of Siamese networks involves pairwise learning, cross entropy loss cannot be used in this case. Instead, we used another loss function called triplet loss [START_REF] Hoffer | Deep metric learning using triplet network[END_REF]. This is a loss function where an anchor (baseline) image is compared to a positive image (i.e., an image that is in the same category as the anchor image) and a negative image (i.e., an image that is in a different category as the anchor image). The distance (here we used squared Euclidean distance) from the anchor image to the positive image is minimized, and the distance from the anchor image to the negative image is maximized. As shown in formula (1), D(x, y)

represents the distance between the learned vector representation of spectrograms x and y, and α is a margin term used to stretch the distance differences between similar and dissimilar pairs in the triplet, and the remaining parameters represent the feature embeddings for the anchor (a), positive (p), and negative (n) images.

L(a, p, n) = max(0, D(a, p) -D(a, n) + α)

(1)

During the training process, an image triplet (anchor image, positive image, negative image) is fed into the model as a single sample (see Fig. 4). The distance between the anchor and positive images should be smaller than that between the anchor and negative images. For many deep learning models, a large training data set is needed to achieve good performance. While this may not be practical in many real applications, the architecture of Siamese Networks enables these networks to learn from very little data. When counting the number of blue whale calls, we only classified the spectrograms that had at least one annotated call, and the model was fit separately to each of the four blue whale acoustic populations, as the call densities varied from one population to another. Only 5% of the training dataset spectrograms had 5 or more annotated calls, and 1% had 6 or more, so we created categorical labels of "1", "2", "3", "4", and "5+" to correspond the number of calls in each spectrogram.

IV. Results

We have two classification tasks: the first is to detect and classify the presence or absence of calls from each of the four blue whale populations; and the second is to estimate the number of calls from each of these populations in the training dataset and eventually, novel acoustic datasets. For the two tasks, we compared the performance of the CNN and SNN methods. The annotated data was randomly split into training, validation, and testing sets (which account for 49%, 21% and 30% of the annotated data, respectively), and the model results were reported on the testing set.

A. Model performance for classifying the presence of blue whale calls

For Convolutional Neural Networks (CNN), the multi-class classification model outputs the predicted probability of blue whale call presence for each population, and can be assessed with commonly used metrics, including accuracy, sensitivity, specificity, and Area Under the Curve (AUC). For Siamese Neural Networks (SNN), the output is not probability based and there is no "threshold score", and thus no AUC which is measured at various threshold settings.

To have a fair comparison of the outputs of the two models, we will then use three metrics: accuracy, sensitivity, and specificity. To determine these, we denote annotated calls that were correctly identified as true positives (TP), spectrograms with no calls that were correctly classified as true negatives (TN), calls that were identified as blue whales but were not annotated as false positives (FP), and annotated calls that were not correctly identified as false negatives (FN). Accuracy is the fraction of predictions that model got right (i.e., (TP + TN)/(TP + FP + TN + FN)); sensitivity, or true positive rate, measures the percentage of presence that was correctly predicted (i.e., TP/(TP + FN)); and specificity, or true negative rate, measures the percentage of absence that was correctly predicted (i.e., TN/(TN + FP)). Since sensitivity and specificity in CNN model are dependent on the choice of threshold score, we used a default neutral threshold score of 0.5. For all three metrics, the Siamese Networks model outperforms CNN in overall metrics and almost for each individual population, although CNN is slightly better in Sensitivity for SEIO and Specificity for SWIO (Table III).

B. Model performance for counting the number of blue whale calls

Although treated as a classification task, using standard metrics alone (such as accuracy) that are commonly used to evaluate multi-class classification models may not be appropriate or comprehensive here, as the classes here actually have ordinal implications. Therefore, we used the prediction percentage error as the evaluation metric (see Table IV). The Siamese Networks provided a higher prediction accuracy (lower prediction error) than CNN. score. This makes it convenient for the users to have better understanding of how confident the model is when making classifications and under which circumstances the model may make mistakes. In practical implementations, it also allows users to choose appropriate threshold scores to have either less false positives or less false negatives depending on their specific needs (Fig. 5).

FIG. 5. Illustraiont of the results of call classification task by CNN. Top left and top right:

Histograms for predicted probabilities of positive and negative samples in the testing set.

Bottom left: receiver operating characteristic (ROC) curve. Bottom right: precision-recall curve.

In contrast, Siamese Networks, at the end of the common network in its architecture, output a vectored representation for each input image, thus providing an easy way to visualize in a 2dimension t-distributed stochastic neighbor embedding (t-SNE) (Maaten and Hinton, 2008) plot.

t-SNE is a nonlinear dimensionality reduction technique well-suited for embedding highdimensional data for visualization in a low-dimensional space of two or three dimensions.

Specifically, a Siamese Network models each high-dimensional object by a two-or threedimensional point in such a way that similar objects are modeled by nearby points and dissimilar objects are modeled by distant points with high probability. Fig. 6 shows the t-SNE plots of the testing set for the two classification tasks. From the plot, we can see that the classifications for each of the four blue whale calls are distinct from each other. The "Negative" class, which included "no call" samples for each population, sits in the middle of the four "Positive" classes and overlaps very little with any of them. In the second classification model the number of blue whale calls present in a spectrogram is estimated for each population (Fig 4b). Although we encoded the number of calls as categorical variables which ignored their ordinal implications (that is, category "1" should be closer to category "2" than category "3", and category "2" should be closer to category "3" than category "4" or "5+", etc.), the Siamese Networks clearly learned such ordinal relationships.

V. Discussion

We built classification models to detect, classify and count the number of calls by each of four blue whale acoustic populations in the Indian Ocean. In comparison to Convolutional Neural Networks (CNN) which have shown success in several prior research in classifying bioacoustics for multiple species [START_REF] Bianco | Machine learning in acoustics: Theory and applications[END_REF], Siamese Networks achieved better performance in this study.

While Siamese Networks are particularly suitable for scenarios where there are only a few samples in each class (i.e., few-shot learning), they can also be applied to larger datasets, like the one we used in this study. However, since Siamese Networks learns from quadratic pairs (to make use of all information available), the training is much slower than pointwise learning models such as CNN. Additionally, instead of outputting probabilities of the prediction, they output the distance from closest training samples in each class instead. In practice, CNN and SNN can be used together to complement each other. Given that the learning mechanism of SNN is somewhat different from CNN, their ensembled results are likely to perform even better.

While both models performed well in general on classifying calls from 4 populations of blue whales, their performance differed among different populations. Classification of Antarctic blue whale calls had the highest accuracy among 4 populations, while CIO had the lowest accuracy.

One possible reason is that Antarctic, SEIO and SWIO have larger sizes of training samples compared to CIO, but more likely is that Antarctic blue whale calls (Z-calls) have more frequency modulation on the spectrograms, compared to that of the CIO blue whale calls (which looks like a flat line). Another factor is the call loudness in the audio recordings. In general, CIO blue whale calls have lower signal-to-noise ratios in the annotated data, which increases the difficulty for the model to classify correctly with high confidence. The lower signal-to-noise ratios for CIO blue whale calls could be due to a number of factors among which we cannot currently distinguish. These include the CIO call having a lower source level than other calls; there are only a few source levels reported for blue whale signals globally, and none for CIO blue whale calls. It is also likely that the animals producing these signals are further from the hydrophones than the other populations, given what is known about their distributions, although since the hydrophones are omni-directional we cannot ascertain this for certain. This signal is the highest frequency signal we detected and as such would be subject to greater transmission loss than the other signals.

Compared to traditional methods which rely heavily on manual verification by a human user or template matching by software, the method presented here uses deep learning models and has the advantage of flexibility with regards to temporal and frequency variations in a dataset.

Notably for blue whale calls, the call frequency has been getting lower in all populations over time [START_REF] Mcdonald | Worldwide decline in tonal frequencies of blue whale songs[END_REF][START_REF] Leroy | Broad-scale study of the seasonal and geographic occurrence of blue and fin whales in the Southern Indian Ocean[END_REF], and one major advantage of this approach is that it looks for the shape of the call independent of the frequency of the call. Siamese methods for marine mammals. Density estimation is one of the key ways to determine trends in marine mammal populations using single instrument passive acoustic data and estimates of call counts [START_REF] Küsel | Cetacean population density estimation from single fixed sensors using passive acoustics[END_REF][START_REF] Marques | Estimating animal population density using passive acoustics[END_REF]. In this way, Siamese Networks are robust and shown here to be an effective way to automatically mine large acoustic data sets for the presence and number of blue whale calls.

FIG. 1 .

 1 FIG. 1. Map of the southern Indian Ocean. Black dots represent moorings of the OHASISIBIO

FIG. 2 .

 2 FIG. 2. Examples of annotated blue whale call with Raven Pro 1.5. a) SEIO pygmy blue whales, b)

FIG. 4 .

 4 FIG. 4. Architecture of Siamese Networks with triplet loss.

FIG. 6 .

 6 FIG. 6. (a) t-SNE plot for the model that classifies the presence or absence of blue whale calls

  Networks can easily classify and count multiple types of calls from several populations at the same time and have the ability to classify novel datasets that were collected from different mooring sites or different years. Even at the sites that have somewhat different underwater environments, the model still detected and classified the signals. An additional, and future advantage is that the model can easily scale up to include other species or call types with the addition of annotated data. Although we treated the call count estimation problem as a classification task and encoded the number of calls in each spectrogram as categorical variable, SNN surprisingly learned the ordinal relationship among them. Call counts, or cue rates (how often a signal occurs over a fixed time period, or number of individuals), are critical elements of density estimation

TABLE I :

 I Manually annotated acoustic data from 5 mooring sites for four populations of blue whales by hours and number of annotations per site.

	WKER	32.5 h,	13 h,	19.5 h,	_
		801 calls	109 calls	334 calls
	SWAMS	26 h,	26 h,	_	78 h,
		698 calls	572 calls		537 calls
	NEAMS	_	52 h,	19.5 h,
			769 calls	841 calls
	Mooring Site	Antarctic	SEIO	SWIO	CIO
	SSEIR	_	_	_	19.5 h,
					138 calls
	NCRO	_	_	71.5 h,	_
				1503 calls

_

Manual annotation was performed with Raven Pro 1.5 (Cornell Lab of Ornithology software) by a single bioacoustics expert. Given the distinct geographical distribution of the four blue whale acoustic populations, four datasets were annotated, one for each call type. The audio files composing each dataset were chosen among the OHASISBIO 2015 recordings, to cover a broad range of acoustic scenarios, from high to low SNR calls. Ten-minute spectrograms with fixed parameters (Hanning windows with 50% overlap and 512-point FFT) were screened for blue whale calls. For pygmy blue whales (CIO, SWIO, SEIO), only the strongest unit was annotated (see white boxes on Fig.

2

) whereas for Antarctic blue whales, the whole call was annotated.

TABLE II :

 II Number of labeled data for each population of blue whale. The number of true signals is shown in the left-hand column and the number of spectrograms with no calls used as negative training data is shown in the right-hand column.

	Population Name Annotated calls used for training		Null data used for training
	Antarctic	1,491	1,099
	SEIO	1,459	1,988
	SWIO	2,670	1,187
	CIO	659	1,602

TABLE III :

 III Model results for classifying the presence of blue whale calls for the CNN and SNN models. Highest performance for each measure and acoustic population is in bold type.

	SWIO	0.907	0.905	0.910	0.928	0.957	0.863
	CIO	0.838	0.779	0.899	0.908	0.787	0.963
			CNN			SNN
	Population	Accuracy	Sensitivity Specificity Accuracy	Sensitivity Specificity
	All 4 populations 0.901	0.893	0.909	0.922	0.921	0.922
	Antarctic	0.911	0.900	0.922	0.943	0.949	0.936
	SEIO	0.908	0.917	0.899	0.909	0.895	0.919

TABLE IV :

 IV Model results for predicting the number of calls by CNN and SNN.Even though Convolutional Neural Networks (CNN) did not perform as well as Siamese Neural Networks (SNN) in this dataset, CNN has its advantages of making predictions with probability

		Annotated	Predicted	Predicted	Prediction	Prediction
		number of	number of	number of	percentage	percentage
	Population	calls	calls by CNN	calls by SNN	error by CNN	error by SNN
	Antarctic	1478	1552	1504	5%	1.76%
	SEIO	889	957	878	7.65%	1.24%
	SWIO	2187	2087	2124	4.57%	2.88%
	CIO	316	305	311	3.48%	1.58%
	C. Further comparisons of two models

Acknowledgements

This work was supported by AI for Earth grants at Microsoft. Our appreciation to Dan Morris for connecting different parties for fruitful discussions and helpful online materials. Passive acoustic data collection was funded by the French Polar Institute and the French Oceanographic Fleet, with additional support from INSU-CNRS. M.T. acknowledges support from a PhD Fellowship of the University of Brest and a travel grant from the Isblue project (ANR-17-EURE-0015) to visit APL at the University of Washington.


Christian Iacovita

Roxana Dudric

Mircea Vomir

Ovidiu Ersen

Bertrand Donnio

Jean-Louis L Gallani

Mircea V Rastei

Imaging Large Iron-Oxide Nanoparticle Clusters by Field-Dependent Magnetic Force Microscopy

published or not. The documents may come   L'archive ouverte pluridisciplinaire

Introduction

Various applications in bio-nanomedicine, (1-3) bio-separation, (4,5) drug delivery, (6-8) magnetic hyperthermia, (9-11) magnetic resonance, [START_REF] Blanco-Andujar | Design of iron oxide-based nanoparticles for MRI and magnetic hyperthermia[END_REF][START_REF] Sun | PEG-Mediated Synthesis of Highly Dispersive Multifunctional Superparamagnetic Nanoparticles: Their Physicochemical Properties and Function In Vivo[END_REF][START_REF] Hu | Water-Soluble Magnetite Nanoparticles with High Relaxivity for Magnetic Resonance Imaging[END_REF] or magnetic resonance imaging, [START_REF] Lin | Magnetic nanoparticles applied in targeted therapy and magnetic resonance imaging: crucial preparation parameters, indispensable pre-treatments, updated research advancements and future perspectives[END_REF][START_REF] Wu | Magnetic Particle Spectroscopy: A Short Review of Applications Using Magnetic Nanoparticles[END_REF] rely on the magnetic state of iron-oxide nanoparticles (NPs). [START_REF] Fortin | Size-Sorted Anionic Iron Oxide Nanomagnets as Colloidal Mediators for Magnetic Hyperthermia[END_REF][START_REF] Demortière | Size-dependent properties of magnetic iron oxidenanocrystals[END_REF][START_REF] Smart | The Néel Theory of Ferrimagnetism[END_REF] Generally, iron-oxide NPs display small magnetic moments triggering weak magnetic responses in magnetic fields, a drawback for their potential use in applications. [START_REF] Levy | Correlating Magneto-Structural Properties to Hyperthermia Performance of Highly Monodisperse Iron Oxide Nanoparticles Prepared by a Seeded-Growth Route[END_REF][START_REF] Tong | Size-Dependent Heating of Magnetic Iron Oxide Nanoparticles[END_REF] A way to overcome this limitation is to enhance the magnetic moment by increasing the particle size. [START_REF] Huang | Investigation of the Growth Mechanism of Iron Oxide Nanoparticles via a Seed-Mediated Method and Its Cytotoxicity Studies[END_REF][START_REF] Guardia | Tuning the Size, the Shape, and the Magnetic Properties of Iron Oxide Nanoparticles[END_REF][START_REF] Nemati | Improving the Heating Efficiency of Iron Oxide Nanoparticles by Tuning Their Shape and Size[END_REF][START_REF] Cheah | Continuous growth phenomenon for direct synthesis of monodisperse water-soluble iron oxide nanoparticles with extraordinarily high relaxivity[END_REF][START_REF] Ma | Discovery of superparamagnetism in sub-millimeter-sized magnetite porous single crystals[END_REF] One particularly promising solution is the formation of porous or nanoparticulate clusters by self-assembling or self-aggregation. [START_REF] Ganesan | Size-controlled synthesis of superparamagnetic magnetite nanoclusters for heat generation in an alternating magnetic field[END_REF][START_REF] Bigall | Colloidal Ordered Assemblies in a Polymer Shell-A Novel Type of Magnetic Nanobeads for Theranostic Applications[END_REF][START_REF] Dallet | Micron-sized iron oxide particles for both MRI cell tracking and magnetic fluid hyperthermia treatment[END_REF][START_REF] Zahedi | Magnetic-Field-Induced Formation of Superparamagnetic Microwires in Suspension[END_REF][START_REF] Andreu | Nano-objects for Addressing the Control of Nanoparticle Arrangement and Performance in Magnetic Hyperthermia[END_REF] Hence, internanoparticle interactions within the clusters and the collective behavior can activate higher magnetic moments, [START_REF] Lartigue | Cooperative Organization in Iron Oxide Multi-Core Nanoparticles Potentiates Their Efficiency as Heating Mediators and MRI Contrast Agents[END_REF][START_REF] Blanco-Andujar | High performance multi-core iron oxide nanoparticles for magnetic hyperthermia: microwave synthesis, and the role of core-to-core interactions[END_REF][START_REF] Toulemon | Enhanced Collective Magnetic Properties Induced by the Controlled Assembly of Iron Oxide Nanoparticles in Chains[END_REF][START_REF] Bender | Supraferromagnetic correlations in clusters of magnetic nanoflowers[END_REF] enhancing the magnetic responses while providing larger surface areas for binding functional biomolecules, as needed for instance in drug delivery. [START_REF] Price | Magnetic Drug Delivery: Where the Field Is Going[END_REF] Clusters made of NPs can be formed in a two-step process, including first the synthesis of the NPs and then their assembling through colloidal (solvo-phobic) interactions mediated by ligands. [START_REF] Stolarczyk | Nanoparticle Clusters: Assembly and Control Over Internal Order, Current Capabilities, and Future Potential[END_REF] The method allows tuning the NP interactions with a good control. Alternatively, a clustering process can be produced through a direct aggregation of NPs during synthesis. [START_REF] Antone | Preparation and Application of Iron Oxide Nanoclusters[END_REF][START_REF] Gutiérrez | Synthesis methods to prepare single-and multi-core iron oxide nanoparticles for biomedical applications[END_REF] The polyol method has extensively been used in the last decade for this purpose. [START_REF] Caruntu | Synthesis of Variable-Sized Nanocrystals of Fe3O4 with High Surface Reactivity[END_REF][START_REF] Cheng | Facile synthesis and morphology evolution of magnetic iron oxide nanoparticles in different polyol processes[END_REF] Its versatility enables the formation of different types of aggregates with controlled morphologies ranging from nanoclusters to nanoflowers and hollow spheres. [START_REF] Deng | Monodisperse Magnetic Single-Crystal Ferrite Microspheres[END_REF][START_REF] Iacovita | Small versus Large Iron Oxide Magnetic Nanoparticles: Hyperthermia and Cell Uptake Properties[END_REF][START_REF] Sakellari | Ferrimagnetic nanocrystal assemblies as versatile magnetic particle hyperthermia mediators[END_REF][START_REF] Hugounenq | Iron Oxide Monocrystalline Nanoflowers for Highly Efficient Magnetic Hyperthermia[END_REF][START_REF] Gavilán | Formation Mechanism of Maghemite Nanoflowers Synthesized by a Polyol-Mediated Process[END_REF][START_REF] Reddy | Magnetic Nanoparticles: Design and Characterization, Toxicity and Biocompatibility, Pharmaceutical and Biomedical Applications[END_REF][START_REF] Hemery | Morphologies, and Magnetic Properties of Monocore Versus Multicore Iron Oxide Nanoparticles through the Controlled Addition of Water in the Polyol Synthesis[END_REF][START_REF] Iacovita | Cytotoxicity, and Cellular Uptake Properties of Manganese and Zinc Ferrite Magnetic Nanoparticles Synthesized by a Polyol-Mediated Process[END_REF] The major inconvenience of this method when applied for mass production is however the long reaction time (6 h to 24 h or even more). It has been shown that increasing the synthesis temperature above the boiling point of the polyols reduces the reaction time down to 1 h. [START_REF] Hachani | Polyol synthesis, functionalisation, and biocompatibility studies of superparamagnetic iron oxide nanoparticles as potential MRI contrast agents[END_REF] Such a temperature rise also triggers the development of ferromagnetic atomic crystal phases [START_REF] Iacovita | Vitro Intracellular Hyperthermia of Iron Oxide Magnetic Nanoparticles, Synthesized at High Temperature by a Polyol Process[END_REF] requiring systematic magnetic investigations.

A good understanding of the magnetic state of clusters made from NPs can be obtained by monitoring the collective magnetic response with superconducting quantum interference devices (SQUID) [START_REF] Saari | Characterization of the magnetic moment distribution in low-concentration solutions of iron oxide nanoparticles by a high-Tc superconducting quantum interference device magnetometer[END_REF] or vibrating sample (VSM) magnetometers. ( 52) Such magnetic measurements performed on dried powders or colloidal suspensions are necessary for optimizing the chemical synthesis, as well as for concluding about the magnetic state. MFM [START_REF] Kazakova | Frontiers of magnetic force microscopy[END_REF] on the other hand is a technique able to image individual clusters and to address their magnetization states at the nanometer scale. [START_REF] Rastei | Electrochemical growth of Co nanodots on patterned Si substrates[END_REF][START_REF] Rastei | Nanoscale hysteresis loop of individual Co dots by field-dependent magnetic force microscopy[END_REF][START_REF] Jaafar | Control of the chirality and polarity of magnetic vortices in triangular nanodots[END_REF][START_REF] Moya | Superparamagnetic versus blocked states in aggregates of Fe3-xO4 nanoparticles studied by MFM[END_REF] This technique has been used for revealing magnetic properties of individual NPs (58-61) and of magnetically correlated grain systems. [START_REF] Rastei | Growth control of homogeneous pulsed electrodeposited Co thin films on n-doped Si(111) substrates[END_REF] Nevertheless, MFM has also the potential to qualitatively investigate the magnetization state of clusters made of several NPs, in addition to their morphology, size characteristics, and separation. [START_REF] Iacovita | Magnetic force fields of isolated small nanoparticle clusters[END_REF][START_REF] Rastei | Defect-Driven Magnetization Configuration of Isolated Linear Assemblies of Iron Oxide Nanoparticles[END_REF] Moreover, in some cases, the magnetic interaction forces measured by the microscope can also be used for quantitative magnetization studies under magnetic fields. [START_REF] Angeloni | Single nanoparticles magnetization curves by controlled tip magnetization magnetic force microscopy[END_REF][START_REF] Jaafar | Hysteresis loops of individual Co nanostripes measured by magnetic force microscopy[END_REF] Here, we present a MFM study on individual large iron-oxide nanoparticle clusters by operating the microscope under a variable magnetic field. The aim is to develop a microscopy method capable to address the magnetization behavior of individual clusters, without the adverse impact of electrostatic fields. In particular, the goal of this study is to demonstrate that individual clusters display a SP behavior at room temperature with specific MFM imaging characteristics. The study is realized on individual clusters with diameters of the order of 300 nm. They are composed of crystalline nanoparticles of about 14 nm. The clusters were deposited on a non-magnetic carbon-membrane grid (as used in electron microscopy). This allows the investigation of the same clusters by TEM and MFM, using a nanometric correlative approach. In particular, it will be shown that field-dependent (FD) MFM signals acquired after an in situ removal of electrostatic contributions can inform about the magnetization state of individual clusters as large as several hundreds of nanometres. In the limit of the magnetic fields used in the present study, the hysteresis loops indicate a superparamagnetic (SP) state for all investigated clusters.

Experimental Details

Synthesis The synthesis of magnetic clusters has been performed with the following products: iron(III)chloride hexahydrate (FeCl3 6H2O) (Roth, ≥ 98%), ethylene glycol (EG) (Roth, ≥ 99%), polyethylene glycol 1000 (PEG1000) (Roth, ≥ 99%), and sodium acetate trihydrate (NaAc) (Roth, ≥ 99.5%). Iron oxide NPs have been synthesized according to the following procedure: FeCl3•6H2O (0.27 g), NaAc (1.2 g), and PEG1000 (0.5 g) were mixed and dissolved in 40 mL of EG. The solution was stirred at room temperature for 30 min and then transferred in a 60 mL round-bottom flask. Prior to sealing the reaction vessel, the solution was degassed with a flux of nitrogen gas for 5 min. The solution was heated to 300 °C with a constant heating rate of 5 °C/min and kept at this temperature for 1 h. A series of multiple ultrasonication/magnetic separation cycles, in order to remove the excess of ligands and unreacted precursors, were applied. The increase of the temperature at 300 °C during the synthesis, which is well above the boiling point of EG (200 °C), enables the formation of iron-oxide clusters in only 1 h, as compared to previous synthesis which require longer reaction times. [START_REF] Deng | Monodisperse Magnetic Single-Crystal Ferrite Microspheres[END_REF][START_REF] Iacovita | Small versus Large Iron Oxide Magnetic Nanoparticles: Hyperthermia and Cell Uptake Properties[END_REF] As shown in the inset of Figure 1 and Figure S1a, the resulting iron-oxide clusters exhibit on average a spherical-like shape with diameters in the range of hundreds of nanometers Methods TEM was carried out on a JEOL-2100F microscope working at 200 kV. A 5 μL drop of an aqueous solution containing clusters was deposited on a copper grid covered with a carbon membrane. XRD measurements (Figure 1a) were carried out on powder with a Bruker D8 ADVANCE diffractometer, using Cu Kα radiation. Magnetic measurements (Figure 1b) were performed on powder in magnetic fields up to 2 T, using a VSM. Atomic force microscopy (AFM) and MFM experiments have been performed in ambient conditions by means of a modified ICON-AFM apparatus controlled by Nanoscope V electronics from Bruker. Low magnetic moment probes (PPP-MFMR─NanoSensors) with a coercive field of 25 mT were used. Prior to the experiments, the analyzed clusters were not exposed to any magnetic field. The spring constant of the cantilevers was between 2 and 3 N m -1 , and their resonance quality factor ranged between 500 and 1000. The magnetic field, normal to the substrate plane, was given by a home-built electromagnet, calibrated with a Hall probe.

Results and Discussion

Figure 1c shows three clusters which have been individually analyzed by both TEM and MFM. The isolated upper cluster, labeled S, has a diameter of 350 nm, while the lower two clusters, labeled C1 and C2, have sizes of about 280 and 330 nm, respectively. The mean crystalline size inside the clusters (Figure 1d), calculated using Scherrer's equation and a fit of the (220), (311), and (440) diffraction peaks, is around 14 nm, indicating a multicore structure of the clusters. Moreover, the XRD pattern confirms the presence of a pure inverse spinel crystalline phase ascribed to magnetite─Fe3O4 (PDF no: 88-0315 (67)), as can also be seen in Figure 1e. In addition, the corresponding atomic lattice parameter is very close to that of bulk magnetite (a = 0.8375 nm). As shown by EDX analysis [Figure 1f-h], the iron (Fe) and oxygen (O) elements within the clusters are homogenously distributed. At 4 K, the VSM hysteresis loop [Figure 1b] measured on powders shows a ferromagnetic character with a saturation magnetization of 78 emu/g, a remnant magnetization of 20 emu/g, and a coercive field of 28 mT. At 300 K, the saturation magnetization decreases considerably to 62 emu/g, owing to spin-canting effects. [START_REF] Baaziz | Magnetic Iron Oxide Nanoparticles: Reproducible Tuning of the Size and Nanosized-Dependent Composition, Defects, and Spin Canting[END_REF] The remnant magnetization is of about 6 emu/g with a coercive field of 10 mT, which suggests the presence of ferromagnetic phases.

The collective magnetic properties of the clusters have been further addressed by temperature dependences of magnetization (Figure S1c). From zero-field cooling (ZFC) curve, the onset of the ferromagnetic to SP transition (blocking temperature TB) can be identified around 175 K. However, the peak in the ZFC curve is very broad extending above 300 K. This indicates a gradual transition to the SP state which is not completed at 300 K as the ZFC curve displays a magnetization value of 35 emu/g. The field cooled (FC) curve coincides with ZFC curve in a very short temperature range from 300 to 280 K, which represents the so-called irreversibility temperature (TIRR), while below 280 K, it reaches a plateau. It is worth noticing that for monodispersed and non-interacting single-domain magnetic nanoparticles, the TIRR coincides with TB forming a narrow ZFC peak, with both ZFC and FC curves decreasing progressively toward zero, becoming even negative in the vicinity of room temperature.

(69) For our samples, the behavior of ZFC and FC curves is therefore typical for an assembly of nanoparticles with a non-negligible distribution in the magnetic properties. This might be induced by interparticle interactions and/or residual ferromagnetic phases. For accurate magnetic state measurements by MFM, it is essential to first evaluate the electrostatic interactions between the microscope probe and the sample. Indeed, the phase signal measured by MFM may contain a significant contribution from non-magnetic electrostatic forces, which arise from topographic effects and from inhomogeneous surface potentials between the clusters and substrate surface. (70-73) Therefore, we began our study by precisely evaluating the electrostatic contributions to the phase signal acquired in MFM. Our approach is to monitor the phase shift of the cantilever oscillation at specific tip-sample distances as a function of a bias voltage applied between the tip of the microscope and the sample substrate (Figure 2a).

In the absence of electrostatic interactions, the phase signal as a function of the applied tip bias is expected to have a quadratic shape (parabola) centered at 0 V. As shown in Figure 2a (also see Figure S2), the parabolas are not symmetric with respect to 0 V, as their maxima are shifted to about +1 V. This suggests the presence of an important intrinsic electrostatic contribution to the phase signal. It is also seen that, although the parabolas progressively flatten with tip-sample distance, they persist to at least 900 nm. The parabolas recorded in the presence of an external out-of-plane magnetic field ranging from 15 to 55 mT are shown in Figures 2b andS2. As seen, the parabolas do not change with respect to the magnetic field, which confirms the possibility of compensating the electrostatic interactions by applying a bias voltage of +1 V to the tip. Insights into the magnetic state of the clusters were gained by recording FD MFM images. Two sets of MFM images have been recorded: one without electrostatic compensation (0 V, presented in Figure S3) and another with an electrostatic compensation (+1 V), as shown in Figures 3 andS4. Figure 3 shows FD-MFM images of the isolated cluster labeled S in Figure 1c, taken at +1 V (full series of images in Figure S3). Starting at a magnetic field of +55 mT, a dark (negative) contrast is observed over the cluster, which corresponds to an attractive interaction due to the parallel alignment of tip and cluster magnetization.

Decreasing the external magnetic field toward zero results in a decrease of the phase contrast. This indicates a decrease of the cluster magnetization along the z-axis (normal to the substrate plane), since the tip magnetization is constant in the respective magnetic field range (see below). A more complex situation is present in the field interval between 0 mT and the coercive field of the tip at -25 mT. In this interval, one could expect that the cluster magnetization aligns with the external field, that is, in the opposite direction with respect to the tip magnetization. The absence of bright contrast over the cluster nevertheless shows that this is not the case. In turn, the dark contrast reveals an attractive interaction, indicating that the magnetization components of the cluster and the tip are still oriented in the same direction (as depicted by arrows in Figure 3). This shows that the magnetization of the cluster is influenced by the stray field of the magnetic tip that overcomes the external magnetic field in this interval. In fact, variations of the sample magnetization induced by MFM magnetic tips have been observed in MFM experiments, (74-78) while the axial stray field of the tips have indeed been found in the mT range. [START_REF] Van Schendel | A method for the calibration of magnetic force microscopy tips[END_REF][START_REF] Streblechenko | Quantitative magnetometry using electron holography: field profiles near magnetic force microscope tips[END_REF] Based on the above observations, the magnetization of the cluster is schematized in Figure 3 by arrows (brown) of various thicknesses, while the magnetization of the tip is represented by black and blue arrows. It can thus be deduced that at 0 mT, the dark MFM contrast (attractive interaction) is caused by the tip magnetic stray-field that induces a residual magnetization in the cluster. At -25 mT (red arrow in Figure 3), the tip coercive field is reached, and the tip magnetization suddenly reverses. The stray field of the tip points then along the same direction as the external field, explaining the larger MFM signal below the position indicated by the red arrow. It is then followed by a progressive increase of the dark contrast as the external magnetic field is swept toward more negative values.

The contrast dynamics over the cluster follows the same trend when sweeping back the external magnetic field from -55 to +55 mT. The tip coercive field is reached at +25 mT (red arrow in Figure 3). Along the entire magnetic cycle, a dominant dark contrast is therefore observed in FD-MFM images corresponding to a single magnetic domain configuration. This is consistent with a coherent rotation of the spins of multiple parts forming the cluster, indicating that the cluster behaves as a macro-spin. In addition, the observed uniform dark contrast induced by tip stray-field, that is, in the absence of an external magnetic field reflects a low value of the overall anisotropy energy of the cluster.

By a one-by-one analysis of the FD-MFM images, an average phase signal over the cluster can be obtained. Figure 4a,b shows the variation of the average phase signal as a function of the external magnetic field, for a tip bias of 0 and +1 V, respectively. A butterfly-like loop is observed in both cases. At +1 V, the compensation of electrostatic effects induces a downward shift of the butterfly loop, providing a clearer magnetic signal of the sample. It might be expected that the magnetic field of the tip adds to the external field, resulting in a shift of the magnetic field axis and in a deformation of the MFM hysteresis curves. However, the magnetization of the clusters follows the magnetization direction of the tip (interaction always attractive). This means that the resulting MFM hysteresis loop reflects the tip magnetization behavior rather than of the cluster. As a consequence, in the limit of our field resolution, a shift of the MFM hysteresis loop is not observed.

The electrostatic contribution interferes with the "magnetic" phase signal. As seen in Figure 4a in the interval -10 and -25 mT, the values of the magnetic phase shift are positive, which in principle must denote a repulsive interaction experienced by the tip. This is a clear visualization of the effect of the electrostatic field. Without the electrostatic interaction as shown in Figure 4b, the butterfly loop is fully located at negative values as the tip stray-field and the external magnetic field maintains an attractive magnetic interaction. The second effect induced by electrostatic interactions and observed in Figure 4a is the asymmetry of the butterfly loop, when the external field is reversed. Indeed, when the electrostatic contribution is compensated [Figure 4b], the butterfly loop is far more symmetric, also being less distorted. These effects brought by the electrostatic fields can be seen even better when looking at the hysteresis loops obtained after considering the sharp reversal of the tip magnetization at ± 25 mT [Figure 4c,d]. The resulting hysteresis loop from Figure 4d has a shape given mainly by the magnetization reversal of the microscope tip. Before further strengthening this conclusion by presenting results on highly anisotropic CoPt nanoislands (coercive fields > 300 mT and close to 100% of remnant magnetization), we hereafter present the findings on the two clusters seen in the lower part of Figure 1c. As above, the FD-MFM images were recorded with and without electrostatic contributions. FD-MFM images taken at +1 V are shown in Figure 5, full series of images at +1 V and 0 V can be seen in Figures S6 andSI5, respectively. The dark contrast in Figure 5 shows a similar trend as before for the isolated cluster. The tip magnetization reversal for both, back and forth magnetic sweeping directions, takes place at -25 and +30 mT, respectively. The dark contrast is uniform over both clusters along the entire magnetic cycle, which speaks in favor of a SP behavior of both clusters as in the case of the isolated cluster.

Phase butterfly-like loops are observed for both clusters [labeled C1 (largest) and C2 (smallest), Figure 6a,b]. No positive value of the magnetic phase shift is detected over the entire cycle, suggesting weaker electrostatic effects [Figure 6a]. Indeed, they are expected to be reduced by the proximity of the two clusters, which decreases the topographic variations. [START_REF] Angeloni | Removal of electrostatic artifacts in magnetic force microscopy by controlled magnetization of the tip: application to superparamagnetic nanoparticles[END_REF] Consequently, the downward shifts of the butterfly-like loops [Figure 6a,b] and of the resulting hysteresis loops [Figure 6c,d] are not so pronounced when compensating the electrostatic contribution, as compared to the isolated cluster. The fact that the phase signal delivered by the two clusters is higher with respect to the isolated cluster can also play a role. Above adjacent clusters, the tip collects a higher magnetic signal, the phase values being greater as compared to an isolated cluster. This observation strengthens the above conclusions that the hysteresis loops reflect the magnetic reversal of the tip. To support these findings, we conducted experiments on hard magnetic samples consisting on CoPt islands of 45 nm in height and 200 nm in diameter [Figure 7a]. Due to the strong spin-orbit coupling, these islands have a large out-of-plane anisotropy, and a large coercive field in the range 350-550 mT, which is more than ten times larger than the tip coercive field. (81) As a consequence, the magnetization of the CoPt islands is blocked at room temperature and constant in the field interval explored in the present MFM study. [START_REF] Abes | Magnetic switching field distribution of patterned CoPt dots[END_REF] Changes in phase contrast with the external magnetic field can then be exclusively ascribed to the variation/reversal of the tip magnetization. With a zero external magnetic field, the magnetic contrast is either dark (negative) or bright (positive) [Figure 7b], denoting a parallel or antiparallel orientation of the magnetization, respectively. The quartz substrate supporting the CoPt islands has a different dielectric constant as compared to the carbon TEM grid and a far larger thickness of about 1 mm. For this reason, the maxima of the parabolas are shifted to approximately─5.0 V, which is significant [Figure 7c]. The electrostatic contribution to the magnetic signal is therefore expected high. An MFM image of four CoPt islands acquired with -5.0 V applied to the tip is shown in Figure 7b. At the maximum applied magnetic field of +55 mT, a dark contrast is observed over the island 1 and 4, while islands 2 and 3 exhibit a bright (positive) contrast [Figure 7d]. The phase signal recorded over all islands reverses when reaching a magnetic field of -28 mT [Figure 7d]. Sweeping backward the external magnetic field, the initial phase signal is recovered when applying +30 mT (Figure S7).

The average phase signal as a function of the external magnetic field for both types of magnetized islands therefore results in typical hysteresis loops of the tip (Figure 7e). This is because the CoPt magnetization is strictly constant in this field interval. In addition, the sudden switch of the signal over CoPt islands reveals that the reversal of tip magnetization occurs in a reduced field range, suggesting a strong bimodal state of the tip magnetization, that is, up and down. These conclusions, along with the fact that the phase contrast reverses above the clusters at the same fields (-28 and +30 mT) again demonstrate that the hysteresis loops acquired on the clusters can be directly associated to the tip magnetization behavior.

Reconstruction of Magnetization Behavior of the Clusters

To retrace the FD magnetization behavior of the clusters, one possibility is to subtract the magnetization switching of the tip. The simplest way is to consider a square hysteresis loop for the tip. Similar to the case of diamagnetic corrections realized for weak magnetic samples, it is first required to establish a correction factor. The slope of the phase signal before the coercive fields (either flip direction) is rather constant [see black or red arrows in Figures 4d and6d]. Under these conditions, a correction factor can be the phase signal at zero magnetic field, which is slightly different for different clusters and sweeping directions. 8 show a progressive increase of the cluster magnetization with the magnetic field, and the absence of a hysteresis, indicating a SP behavior of these individual clusters supported by a substrate. Our magnetic field (<55 mT), as well as the impossibility to perform low-temperature experiments with our microscope does not allow us to further support this conclusion. The SP behavior revealed in the present MFM study is in agreement with the small dimension of crystallites (14 nm) composing the clusters, as the critical size of magnetite to enter ferromagnetic regime at room temperature is about 30 nm. In dried powders, as generally measured by VSM or SQUID magnetometry, the clusters are most likely assembled in large aggregates which, upon a first exposure to an external magnetic field, are prone to develop magnetic dipolar interactions and finally ferromagnetic characteristics, as observed in our VSM curves. Dipolar interactions in highly concentrated suspensions of 13 nm maghemite NPs were in fact observed by temperature-dependent magnetization measurements. (82) However, to date, a clear picture on inter-particle interactions mediated by ligands is not yet available. In this regard, our approach based on FD-MFM investigation of individual clusters can be extended to other systems such as nanoflowers or hollow spheres. [START_REF] Gavilán | Formation Mechanism of Maghemite Nanoflowers Synthesized by a Polyol-Mediated Process[END_REF][START_REF] Iacovita | Cytotoxicity, and Cellular Uptake Properties of Manganese and Zinc Ferrite Magnetic Nanoparticles Synthesized by a Polyol-Mediated Process[END_REF]

Conclusions

In conclusion, large iron-oxide nanoparticulate clusters have been synthesized in a quick (1 h) and one step process. According to TEM-EDX and XRD analysis, the clusters display a poly-crystalline multicore structure and an overall spherical shape with sizes of the order of few hundreds of nanometers. The magnetization state of individual clusters has been probed at room temperature by FD magnetic force microcopy. It was found that after an in situ removing of electrostatic interactions, which are known to affect the phase signals in magnetic force microscopy, it is possible to retrace the FD magnetization behavior of individual clusters in interaction with the microscope tip. The resulting magnetization loops display a typical butterfly-like shape resulting mainly from the magnetization reversal of the tip, as separately demonstrated on hard magnetic CoPt islands. After taking into account the magnetization reversals of the tip, the MFM phase signals reveal a gradual increase of the magnetization of the clusters with the magnetic field, and the absence of a hysteresis in the limit of magnetic fields below 55 mT, suggesting a SP behavior. Overall, these results show an effective experimental procedure for probing large magnetic clusters with variable sizes and shapes, as needed for many biomedical applications. Our approach can readily be used for temperature-dependent experiments or to confirm the SP character of clusters composed of crystallites of various sizes.

Figure 1 .

 1 Figure 1. (a) X-ray diffraction (XRD) diffraction pattern of a powder of Fe3O4 particle-based clusters. (b) Magnetization curves of the powder at 4 and 300 K. Inset is a transmission electron microscopy (TEM) image (3 × 5 μm 2 ) of a cluster assembly on a carbon substrate. (c) TEM image of three clusters studied by magnetic force microscopy (MFM), labeled S, C1, and C2. (d) TEM image of a border crystallite with the corresponding fast Fourier transform pattern (e) showing the presence of well-defined crystallographic structure. (f,g) Chemical maps obtained by EDX spectroscopy of iron and oxygen distribution within the Fe3O4 cluster, respectively. (h) Relative chemical map with the distributions of both iron and oxygen within the clusters.

Figure 2 .

 2 Figure 2. (a) Phase signal acquired above the cluster S from Figure 1c as a function of tip bias for different tip lifts. (b) Phase signal taken above the cluster C2 as a function of tip bias and external magnetic field (lift 200 nm). The dashed lines indicate the center of the parabolas located in each case at about +1 V tip bias.

Figure 3 .

 3 Figure 3. MFM images acquired on the isolated cluster from Figure 1c at different values of external magnetic field ranging between +55 and -55 mT (tip lift = 200 nm and +1 V tip bias). Black and blue arrows represent the magnetization of the tip, while the brown arrows denote the cluster magnetization. The red arrows indicate the reversal of the tip magnetization.

Figure 4 .

 4 Figure 4. (a,b) FD-MFM average phase signals at 0 and +1 V, respectively, acquired over the isolated cluster from Figure 1c. Tip lift: 200 nm. (c,d) Corresponding hysteresis loops obtained by considering the tip magnetization reversal. Arrows indicate back and forth sweeping of the external magnetic field.

Figure 5 .

 5 Figure 5. FD-MFM images acquired on the two adjacent clusters from Figure 1c at different values of external magnetic field ranging between +55 and -55 mT (tip lift = 200 nm and +1 V tip bias). Black and blue arrows represent the magnetization of the tip, while the brown arrows denote the cluster magnetization The red arrows indicate the reversal of the tip magnetization.

Figure 6 .

 6 Figure 6. (a,b) FD-MFM average phase signals at 0 and +1 V acquired on C1 [right-hand cluster in the "dimer" from Figure 1c] and on C2 [left-hand cluster in the "dimer" from Figure 1c], as a function of the external magnetic field. Tip lift: 200 nm. (c,d) Corresponding hysteresis loops considering the tip magnetization reversal. Arrows indicate the back and forth sweeping of the external magnetic field.

Figure 7 .

 7 Figure 7. (a) Topographic AFM image (6 × 1 μm 2 ) and its corresponding MFM phase image (b) taken at 200 nm lift and a tip bias of -5.0 V. The numbers label the CoPt islands. (c) Phase signal as a function of applied tip bias, recorded at lifts ranging from 100 to 1200 nm. The black dashed lines indicate that the center of the parabolas are located at -5.0 V tip bias. (d) Phase profiles along the four CoPt islands displayed in (a) as a function of external magnetic field (lift of 200 nm and tip bias of -5.0 V). (e) MFM hysteresis loops obtained by measuring the CoPt islands (island 1 and island 2) at a lift of 200 nm and a tip bias of -5.0 V. Insets show the phase images of the islands recorded at +55 mT and -55 mT, highlighting the magnetic contrast inversion owing to tip magnetization reversal.

Figure 8 .

 8 Figure 8. FD phase signal for the three clusters from Figure 1c, obtained by considering the magnetic reversal of the microscope tip. Data highlighted in (a) is for the isolated S cluster, in (b) is for C2 cluster, and in (c) is for C1 cluster. Solid lines are linear fits. Error bars are below 15% of the phase signal (not included for clarity).

Figure 8

 8 Figure8shows the phase signal of the three investigated clusters, obtained by subtracting, for each cluster, the phase shift measured at zero field. The data for all clusters drop on rather imperfect linear variations [Figure8aisolated S cluster, (b) C2, and (c) C1]. Nonetheless, the curves from Figure8show a progressive increase of the cluster magnetization with the magnetic field, and the absence of a hysteresis, indicating a SP behavior of these individual clusters supported by a substrate. Our magnetic field (<55 mT), as well as the impossibility to perform low-temperature experiments with our microscope does not allow us to further support this conclusion. The SP behavior revealed in the present MFM study is in agreement with the small dimension of crystallites (14 nm) composing the clusters, as the critical size of magnetite to enter ferromagnetic regime at room temperature is about 30 nm. In dried powders, as generally measured by VSM or SQUID magnetometry, the clusters are

Acknowledgement

This work is supported by ANR OH-RISQUE SMARAGD (14 OHRI 0008 01), ANR-11-LABX-0058-NIE within the Investissement d'Avenir program ANR-10-IDEX-0002-02, and Romanian Ministry of Education and Research, CNCS─UEFISCDI, project number PN-III-P2-2.1-PED-2019-3283, within PNCDI III.

Supporting Information

The Supporting Information is available free of charge at https://pubs-acs-org.scd-rproxy.ustrasbg.fr/doi/10.1021/acs.jpcc.1c05426. Magnetic characterization of the clusters (in powder) as a function of magnetic field and temperature and complete series of field-dependent MFM images, as well as a characterization of the magnetic-field in the AFM microscope (PDF)

Author Information

Corresponding Author: M. V. Rastei -Institut de Physique et Chimie des Matériaux de Strasbourg, UMR 7504 CNRS, Université de Strasbourg, Strasbourg F-67034, France; https://orcid.org/0000-0003-3927-1409; Email: rastei@ipcms.unistra.fr

Authors:

Notes

The authors declare no competing financial interest.