Frédérique De Vignemont

Andrea Serino

Yu Hong

Alessandro Wong

Farnè

Peripersonal space: A special way of representing space

It is easy to believe that our representation of the world is structured under a binary mode: there is the self and then there is the rest of the world. However, one can question this conception in light of recent evidence on the existence of what is known as peripersonal space, which we may think of something like a buffer zone between the self and the world. As a provisional definition, we can say that peripersonal space corresponds to the immediate surroundings of one's body. Nonetheless, it should be noted even at this early stage that the notion of peripersonal space does not refer to a well-delineated region of the external world with sharp and stable boundaries. Instead it refers to a special way of representing objects and events located in relative proximity of what one takes to be one's body. Indeed, as we shall see at length in this volume, peripersonal processing displays highly specific multisensory and motor features, distinct from those that characterize the processing of bodily space and the processing of far space. Depending on the context, the same area of physical space can be processed as peripersonal or not. This does not entail that any location in space can be perceived as peripersonal, but only that there is some degree of flexibility. Now, when some area of space is perceived as peripersonal, it is endowed with an immediate significance for the subject.

For example, objects in one's surroundings are directly relevant for the body, because of their potential for contact, yet they still are in external space. With spatial proximity comes temporal proximity, which gives rise to specific constraints on the relationship between perception and action. There is no time for deliberation when a snake appears next to you. Missing it can directly endanger you. You just need to act.

It may then seem tempting to reduce the notion of peripersonal space to the notion of behavioural space, a space of actions, but this definition is both too wide and too narrow. It is too wide because the space of action goes beyond peripersonal space. Actions can unfold at a relatively long distance: for instance, I can reach for the the book at the top of the shelf despite it being relatively distant from my current location at the writing desk. On the other hand, the definition is too narrow because the space that surrounds us is represented in a specific way no matter whether we plan to act on it or not. There is thus something quite unique about peripersonal space, which requires us to explore it in depth.

Despite the intuitive importance of peripersonal space, it is only recently that the significance of the immediate surroudings of one's body has been recognised by cognitive science. The initial discovery that parietal neurons respond to stimuli 'close to the body' was made by Leinonen and Hyvarinen (1979), but it was Rizzolatti and his colleagues who described the properties of premotor neurons specifically tuned to this region of space in 1981 and named it "peripersonal space".

Detailed neurophysiological exploration of this still unknown territory had to wait until the late nineties, in monkeys (e.g. [START_REF] Graziano | A bimodal map of space: somatosensory receptive fields in the macaque putamen with corresponding visual receptive fields[END_REF] and in human patients (e.g. di Pellegrino, Ladavas & Farnè, 1997). Since then, there has been a blooming of research on peripersonal space in healthy human participants with the help of new experimental paradigms (such as the crossmodal congruency effect, hereafter CCE, [START_REF] Spence | Spatial constraints on visual-tactile cross-modal distractor congruency effects[END_REF] and new tools (such as virtual reality, [START_REF] Maselli | Sliding perspectives: dissociating ownership from selflocation during full body illusions in virtual reality[END_REF]. For the first time, leading experts on peripersonal space in cognitive psychology, neuropsychology, neuroscience and ethology are gathered in this volume to describe the vast number of fascinating discoveries about this special way of representing closeness to one's body along with their on-going research. For the first time too, these empirical results and approaches are brought into dialog with philosophy.

Our aim in this introduction is not to summarize the seventeen chapters that this volume includes.

Instead we offer an overview of the key notions in the field and the way they have been operationalized. We then consider some of the implications of peripersonal space for fundamental issues in the philosophy of perception and for self-awareness.

Theoretical and methological challenges

What is peripersonal space? Surprisingly, perhaps, this is one of the most difficult questions that the field has had to face these last thirty years. To better understand the notion of peripersonal space, it is helpful to contrast it with notions that may be more familiar in the literature and that are closely related. In particular, we shall target three notions: personal space, reaching space, and egocentric space. We shall then turn to the different experimental paradigms and the questions they leave open.

Personal space

Historically, the idea that space around the subject is not represented uniformly was first described in ethology and in social psychology. On this approach, the boundaries are defined exclusively in social terms and vary depending on the type of social interactions, whether negative or positive.

The Swiss biologist Heini Hediger (1955), director of the Zurich zoo, first described how animals react in specific ways depending on the proximity of approaching predators. Each of the distances between the prey and the predator (flight, defence and critical) is defined in terms of the specific range of actions that it induces. Animals also react differently to conspecifics depending on their proximity. Hediger, for instance, distinguishes between personal distance (the distance at which the presence of other animals is tolerable) and social distance (the distance at which one needs to be to belong to the group). Later, the social psychologist [START_REF] Hall | The hidden dimension[END_REF] introduces more distinctions: intimate space, in which we can feel the warmth of another person's body (up to 45 cm); personal space, in which we can directly interact with the other (up to 1.2 m); social space, in which we can work or meet together (up to 3.6 m); and public space, in which we have no involvement with other people. He concludes: "Each animal is surrounded by a series of bubbles or irregularly shaped balloons that serve to maintain proper spacing between individuals." (Hall, 1966 , p. 10). Hall's work linked these distinctions to key features of human societies and described how they shape social interactions, how they vary between cultures (e.g., European vs. Asian), and how they influence the design of architectural spaces (e.g., houses, work places).

Although of great interest, the analysis of most of these bubbles remains purely descriptive.

There is both the risk of unwarranted proliferation and that of undue conflation across concepts and terminology. Moreover, space is only considered in its social dimension. The definition of peripersonal space, by contrast, is more liberal and includes not only the proximity of individuals but of objects too. It would thus be a category mistake to confuse personal space and peripersonal space. Indeed, some studies point to dissociations between the two [START_REF] Patané | Cooperative tool-use reveals peripersonal and interpersonal spaces are dissociable[END_REF]. Clearly marking the distinction between the two notions, however, should not prevent us from exploring their relationship.

Reaching space

Another notion that is often discussed together with peripersonal space is the notion of reaching space. It is functionally defined as the distance at which an object can be reached by the subject's hand without moving her trunk. The two notions are sometimes reduced one to the other, but we believe that they should be carefully distinguished. To start with, reaching space is typically larger than peripersonal space although they can spatially overlap. A second difference between the two notions is that reaching space refers to a unique representation that is shoulder-centred. By contrast, there are several different representations of peripersonal space, which are centred respectively on the hand, the head, the torso, and on the feet. A third difference is at the neural level. The representations of reaching space and peripersonal space both involve fronto-parietal circuits but they include different brain areas. Reaching representation is associated with dorsal premotor cortex, primary motor cortex, SMA, and parietal areas 5 and 7. On the other hand, peripersonal representation is associated with ventral and dorsal premotor cortex, the ventral intraparietal area, IPS, and the putamen.

The final major difference concerns the functional role of these two representations. Most research in cognitive neuroscience has restricted their investigation to bodily movements such as reaching, grasping or pointing. These movements allow us to act on the world, to explore it, and to manipulate objects. From an evolutionary point of view, these are the movements whose ultimate function is to find food and eat it. Reaching space is exclusively concerned by this type of movements. But there is another class of movements, possibly even more important, whose function concerns a different dimension of survival, namely, self-defence. These movements are sometimes summarized with the famous 3 Fs: freeze, fight, flight. One should not think, however, that we engage in protective behaviour only when there are predators. In everyday life, we avoid obstacles on our path, we retract our hand when coming too close to the fire, we tilt our shoulder when walking through a door, and so forth. Unlike the representation of reaching space, the representation of peripersonal space plays this dual role: to engage with the world but also to protect oneself from the world (Vignemont, 2018).

Egocentric space

Standard accounts of perception acknowledge the importance of the spatial relation between the subject and the perceived object: objects are seen on the left or right, as up or down, and even as close or far from the subject's body. These egocentric coordinates are especially important for action planning. One might then wonder what is so special about peripersonal perception. For some, there is actually no difference between egocentric space and peripersonal space. They are both body-centred and they both have tight links to action [START_REF] Briscoe | Egocentric Spatial Representation in Action and Perception[END_REF][START_REF] Ferretti | Visual Feeling of Presence[END_REF]. However, it is important to clearly distinguish the frame of reference that is exploited by peripersonal perception from the egocentric frame of perception in general.

Egocentric location of objects is encoded as occurring in external space, and not in bodily space.

Although body parts, such as the eyes, the head and the torso, are used to anchor the axes on which the egocentric location is computed, the egocentric location is not on those body parts themselves. By contrast, the perceptual system anticipates objects seen or heard in peripersonal space to be in contact with one part or another of the body. Hence, though the objects are still located in external space, they are also anticipated to be in bodily space. The reference frame of peripersonal perception is thus similar to the somatotopic frame used by touch and pain (also called skin-based or bodily frame). To illustrate the difference with the egocentric frame, consider the following example. There is a rock next to my right foot (somatotopic coordinates) on my right (egocentric coordinates). I then cross my legs. In environmental space, the egocentric coordinates do not change (I still see the rock on my right) but the rock is now close to my left foot, and the somatotopic coordinates thus change.

An interesting hypothesis is that the multisensory-motor mechanism underlying peripersonal processing contributes to egocentric processing, by linking multisensory processing about the relationship between bodily cues and environmental cues (computed by posterior parietal and premotor areas) with visual and vestibular information about the orientation of oneself in the environment (computed in the temporo-parietal junction). This proposal might provide insight into how egocentric space processing is computed, which is less studied and less well understood as compared to allocentric space processing, which is computed by place and grid cells in mediotemporal regions [START_REF] Moser | Place cells, grid cells, and the brain's spatial representation system[END_REF]

Probing peripersonal space

We saw that one of the main challenges in the field is to offer a satisfactory definition of peripersonal space that is specific enough to account for its peculiar spatial, multisensory and motor properties. There have been a multitude of proposals but they remain largely controversial and they have given rise to much confusion. Another source of confusion can be found in the multiplicity of methods used to experimentally investigate peripersonal space. Using diverse experimental tasks, different studies have highlighted specific measures and functions of peripersonal space (see Table P1). Visual & Tactile (V-T)

V task irrelevant T target modality

Visual object far from hand Touch on index or thumb of the grasping hand.

Tactile discrimination (index or thumb)

Slower reaction times when planning to move or moving toward object

Emphasis can be put either on perception or on action, but also on impact prediction or on defence preparation. A prominent category of studies employed tactile detection or discrimination tasks, using multisensory stimulation to probe perceptual processes. One of these approaches (the CCE and its variants) consists in the presentation of looming audio-visual stimuli combined with tactile ones. It has consistently revealed the fairly limited extension of the proximal space for which approaching stimuli can facilitate tactile detection. It has also been used to show the plasticity of the representation of peripersonal space after manipulations such as the rubber hand illusion, fullbody illusion, and tool use. Other paradigms focus on bodily capacities. For instance, the reachability judgment task probes the effect of perceived distance on the participants' estimate of their potential for action. The extent of peripersonal space and its dynamic features have also been tackled by joining perceptual multisensory and action tasks concurrently. Finally, in probing the defensive function of peripersonal space, top-down modulation of nociceptive or startling tactile stimuli have been shown to modulate physiological responses (e.g. the blink reflex).

As can be seen from inspection of the (non-exhaustive) Table P1, a variety of methods have been proposed to study the representation of peripersonal space, which likely taps into diverse physiological and psychological mechanisms. There is then the risk of losing the homogeneity of the notion of peripersonal space within this multiplicity of methods. Ideally, one should design experimental paradigms for them to best investigate the notion to study. However, in the absence of a robust definition of the notion, it may happen that the tasks come first and that the notion is constantly redefined on the basis of their results. If there is more than one method, this may give rise to a multiplicity of notions. We believe that it will be beneficial to use multiple tasks to address the same question, especially for the purposes of determining whether there are different notions of peripersonal representation. Beyond the methodological value of such an effort, this route holds the potential to provide more operationally defined and testable definitions of peripersonal space, with the promise of strengthening the theoretical foundations of the notion.

In addition to the central issue of 'which test for which peripersonal space?', we want to introduce some of the many questions for future research to consider from both an empirical and a theoretical perspective.

Outstanding questions:

1. Can we distinguish purely peripersonal space processes from attentional ones? Do we even need the concept "attention" for near stimuli, if we characterize peripersonal space as a peculiar way of processing stimuli occurring near the body? And conversely, do we need the concept "peripersonal space" if it is only a matter of attention?

2. Is peripersonal space a matter of temporal immediacy in addition to spatial immediacy? How do the spatial and temporal factors interact? 6. Did peripersonal space evolve as a tool for survival? What was peripersonal space for?

What is it for today? What will it be for tomorrow? Do we still need peripersonal space in a future in which brain-machine-interfaces feature heavily?

7. Are there selective deficits of peripersonal space or are they always associated with other spatial deficits? What impact do they have on other abilities?

8. What are the effects of environmental and social factors, known to be relevant for personal space, on peripersonal space?

9. What are the effects of peripersonal space on social interactions? For instance, is emotional contagion or joint action facilitated when the other is within one's peripersonal space?

10. How are fear and pain related to the defensive function of peripersonal perception?

Philosophical implications

The computational specificities of the processing of peripersonal space are such that one can legitimately wonder whether peripersonal processing constitutes a sui generis psychological kind of perception. Whether it is the case or not, one can ask whether the general laws of perception, as characterised by our philosophical theories, apply to the special case of the perception of peripersonal space. More specifically, one needs to reassess the relationship between perception, action, emotion, and self-awareness in the highly special context of the immediate surroundings of one's body. Here we briefly describe the overall directions that some of these discussions might take.

Self-location and body ownership

Where do we locate ourselves? Intuitively, we locate ourselves where our bodies are, and this is also the place that anchors our egocentric space and our peripersonal space. Discussions on egocentric experiences has emphasized the importance of self-location for perspectival experiences.

One might even argue that one cannot be aware of an object as being on the left or on the right if one is not aware of one's own location. Being aware of the object in egocentric terms involves as much information about the perceived object as about the subject that perceives it. It has thus been suggested that the two terms of the relation (the perceived object and the self) are both experienced:

when I see the door on the right, my visual content represents that it is on my right [START_REF] Schwenkler | Vision, Self-Location, and the Phenomenology of the 'Point of View[END_REF]Peaocke, 2000). Many, however, have rejected this view [START_REF] Evans | The varieties of reference[END_REF][START_REF] Campbell | Reference and Consciousness[END_REF][START_REF] Perry | Thought without representation[END_REF]Schellenberg, 2007). They argue that there is no need to represent the self, which can remain implicit or unarticulated. A similar question can be raised concerning peripersonal space.

Spatial proximity, which plays a key role in the definition of peripersonal space, is a relational property. One may then ask questions about the terms of the relation: are objects perceived close to the self, or only to the body? And is the self, or the body, represented in our peripersonal experiences? Another way to think about this is to ask: does peripersonal perception require some components of self-awareness? Or is it rather the other way around: can peripersonal perception ground some components of self-awareness?

Interestingly, it has been proposed that multisensory integration within peripersonal space is at the basis of body ownership and that this can be altered by manipulating specific features of sensory inputs [START_REF] Makin | On the other hand: dummy hands and peripersonal space[END_REF][START_REF] Serino | Peripersonal space (PPS) as a multisensory interface between the individual and the environment, defining the space of the self[END_REF]. For instance, in the rubber hand illusion, tactile stimulation on the participant's hand synchronously coupled with stimulation of a rubber hand induces an illusionary feeling that the rubber hand is one's own (see the enfacement illusion, the full body illusion or the body swap illusion for analougous manipulations related to the face or the whole body). The hypothesis here is that the body that feels as being one's own is the body that is surrounded by the space processed as being peripersonal. On this view, peripersonal space representation plays a key role in self-consciousness. A crucial question is how such low-level multisensory-motor mechanisms relate to the subjective experience of body owership. A further question is how self-location, body location and the 'zero point' from which we perceive the world normally converge. A fruitful path to investigate their mutual relationship is to examine 'autoscopic phenomena', such as out-of-body experiences and heautoscopic experiences in patients [START_REF] Blanke | The out-of-body experience: disturbed self-processing at the temporo-parietal junction[END_REF] and in full body illusions (Leggenhager et al., 2007;[START_REF] Ehrsson | The experimental induction of out-of-body experiences[END_REF]. In these cases, subjects tend to locate themselves toward the location of the body that they experience as owning.

Likewise, the extent and shape of peripersonal space is altered along with the induction of illusory ownership toward the location of the illusory body [START_REF] Serino | Body part-centered and full body-centered peripersonal space representations[END_REF]. This suggests that peripersonal space is tied to self-location and body ownershipthat peripersonal space goes with self-location, and that the latter goes with body ownership. This raises interesting questions about cases of heautoscopic patients who have illusions of a second body which they own and who report feeling located in both bodies. What would their peripersonal space look like? Measuring peripersonal space appears to provide us with a further empirical and conceptual tool for thinking about self-location.

Sensorimotor theories of perception

In what manner does perceptual experience contribute to action? And conversely, in what manner does action contribute to perceptual experience? The relationship between perceptual experience and action has given rise to many philosophical and empirical debates, which have become more complex since the discovery of dissociations between perception and action [START_REF] Milner | The visual brain in action[END_REF]. For instance, in the Hollow Face illusion, a concave (or hollow) mask of a face appears as a normal convex (or protruding) face, but if experimental subjects are asked to quickly flick a magnet off the nose (as if it were a small insect), they direct their finger movements to the actual location of the nose in the hollow face. In other words, the content of the illusory visual experience of the face did not correspond to the visually guided movements directed toward the face (Króliczak et al., 2006). Although not without controversy, results such as this have been taken as evidence against sensorimotor theories of perception insofar as they claim that action is constitutive of perceptual experience [START_REF] Noë | Action in Perception[END_REF][START_REF] O'regan | Why red doesn't sound like a bell: Understanding the feel of consciousness[END_REF]. It can also be taken as an argument against what [START_REF] Clark | Visual experience and motor action: Are the bonds too tight?[END_REF] calls the assumption of experience-based control, according to which perceptual experience is what guide action. Here we claim that empirical findings on peripersonal space can shed a new light on this debate, and more specifically, that they can challenge a strict functional distinction between perception and action in one's immediate surroundings.

No matter how complex actions can be and how many sub-goals they can have, all bodily movements unfold in peripersonal space. Typically, while walking, the step that I make is made in the peripersonal space of my foot and while I move forward, my peripersonal space follows -or better, it anticipates my body's future position. Action guidance thus depends on the constant fine-grained monitoring and remapping of peripersonal space while the movement is planned and performed. It is no surprise, then, that peripersonal space is mainly represented in brain regions that are dedicated to action guidance. In addition, the practical knowledge of one's motor abilities determines whether objects and events are processed as being peripersonal or not. Consider first the case of tool use. One can act on farther objects with a tool than without.

This increased motor ability leads to a modification of perceptual processing of the objects that are next to the tool. After tool use these objects are processed as being peripersonal (e.g. [START_REF] Iriki | Coding of modified body schema during tool use by macaque postcentral neurones[END_REF]Farnè and Ladavas, 2000). Consider now cases in which the motor abilities are reduced. It has been shown that after ten hours of right arm immobilization there is a contraction of peripersonal space such that the distance at which an auditory stimulus was able to affect the processing of a tactile stimulus is closer to the body than before [START_REF] Bassolino | Dissociating effect of upper limb non-use and overuse on space and body representations[END_REF]. It may thus seem that the relationship between perception and action goes both ways in peripersonal space: peripersonal perception is needed for motor control whereas motor capacities influence peripersonal perception. Can one then defend a peripersonal version of the sensorimotor theory of perception? And if so, what shape should it take? Although all researchers working on peripersonal space agree on the central role of action, little has been done to precisely articulate this role (for discussion, see [START_REF] De Vignemont | Peripersonal perception in action[END_REF].

A first version is to simply describe peripersonal perception exclusively in terms of unconscious sensorimotor processing subserved by the dorsal visual stream. Interestingly, in the Hollow face illusion, the face was displayed at less than 30 cm from the participants. Hence, even in the space close to us, the content of visual experiences (e.g., convex face) does not guide fine-grained control of action-oriented vision (e.g., hollow face). On this view, there is nothing really special about peripersonal space. We know that there is visuomotor processing, and this is true whether what one sees is close or far. This does not challenge the standard functional distinction between perception and action.

However, this account does not seem to cover the purely perceptual dimension of peripersonal processing. As described earlier, most multisensory tasks, which have become classic measures of peripersonal space, do not involve action at all. Participants are simply asked to either judge or detect a tactile stimulus, while seeing or hearing a stimulus closer or farther from the body. In addition, visual shape recognition is improved in peripersonal space [START_REF] Blini | Mind the Depth: visual perception of shapes is better in peripersonal space[END_REF]. This seems to indicate that there is something special about perceptual experiences in peripersonal space. And since motor abilities influence these perceptual effects, as in the case of tool use, these peripersonal experiences must bear a close relationship to action.

One may then propose to apply the classic perception/action model of perception to the perception of peripersonal space. At the unconscious sensorimotor level, peripersonal processing provides the exact parameters about the immediate surroundings required for performing the planned movements. But at the perceptual level, peripersonal experiences also play a role. One may propose, for instance, that peripersonal experiences directly contribute in selecting at the motor level the type of movement to perform, such as arm withdrawal when an obstacle appears next to us.

Here one may reply that there is still nothing unique to peripersonal space. Since [START_REF] Gibson | The ecological approach to visual perception[END_REF], many have defended the view that we can perceive what have been called affordances, that is, dispositions or invitations to act, and we can perceive them everywhere (e.g., [START_REF] Chemero | An outline of a theory of affordances[END_REF]. The notion of affordance, however, is famously ambiguous. One may then use instead [START_REF] Koffka | Principles of Gestalt Psychology[END_REF] notion of demand character, which actually inspired Gibson. For instance, Koffka describes that you feel that you have to insert the letter in the letterbox when you encounter one. One may assume that you do not experience the pull of such an attractive force when the letterbox is far. In brief, the perception of close space normally presents the subject with actions that she needs to answer to. But again, is it true only of peripersonal space? When I see my friend entering the bar, I can feel that I have to go to greet her. One might then propose that the difference between peripersonal space and far space in their respective relation to action is just a matter of degree. One may claim, for instance, that one is more likely to experience demand characters in peripersonal space than in far space. This gradient hypothesis has probably some truth in it, but the direction of the gradient remains to be understood: why is the force more powerful when the object is seen as close? [START_REF] Bufacchi | An Action Field Theory of Peripersonal Space[END_REF] have recently proposed an interesting conceptualization of peripersonal space as an "action field" or more properly as a "value field", i.e. as a map of valence for potential actions. Why and how such a field is referenced and develops around the body and how it is related to attention remains to be explained.

Affective perception

If there is a clear case in which the objects of one's experiences have demand characters, then the case when one sees a snake next to one's foot is such a case. One feels that one has to withdraw one's foot. But then it raises the question of the relationship between peripersonal perception and evaluation and about the admissible contents of perception. Can danger be perceptually represented? More specifically, does one visually experience the snake close to one's foot as being dangerous?

A conservative approach to perception would most probably reply that danger is represented only at a post-perceptual stage. On this view, one visually experiences the shape and the colour of the snake, on the basis of which one forms the belief that there is a snake, which then induces fear, which then motivates protective behaviour. Danger awareness is manifested only later, in cognitive and conative attitudes that are formed on the basis of visual experiences. However, a recent more liberal approach to perception has recently challenged the conservative approach and argued that the content of visual experiences can also include higher-level properties [START_REF] Siegel | The Contents of Visual Experience[END_REF]. On this view, for example, one can visually experience causation. Interestingly, discussions on the admissible contents of perception have been even more recently generalized to evaluative properties, but so far they have focused on aesthetic and moral properties (Bergqvist and Cowan, 2018): Can one see the gracefulness of a ballet? Can one see the wrongness of a murder? The difficulty in these examples is that assessing such aesthetic and moral properties requires sophisticated evaluative capacities that are grounded on other more or less complex abilities and knowledge. Danger, on the other hand, a more basic evaluative property, possibly the most basic one from an evolutionary standpoint. To be able to detect danger is the firstalthough not the onlyobjective to meet for an organism to survive.

Because of the primacy of danger detection, one can easily conceive that it needs to occur very early on, that it does not require much conceptual resources and that it needs to be in direct connection with action. This provides some intuitive plausibility to the hypothesis that danger can be visually experienced.

One may then suggest that this hypothesis finds empirical support in the existence of peripersonal perception. For many indeed, peripersonal space is conceived of as a margin of safety, which is encoded in a specific way to elicit protective behaviours as quickly as possible if necessary. [START_REF] Graziano | The intelligent movement machine: An ethological perspective on the primate motor system[END_REF], for instance, claims that prolonged stimulation of regions containing peripersonal neurons in monkeys triggers a range of defensive responses such as eye closure, facial grimacing, head withdrawal, elevation of the shoulder, and movements of the hand to the space beside the head or shoulder. Disinhibition of these same regions (by bicuculline) leads the monkeys to react vividly even for non-threatening stimuli (when seeing a finger gently moving toward the face, for instance), whereas their temporary inhibition (by muscimol) has the opposite effect: monkeys no longer blink or flinch when their body is under real threat. In humans, just presenting stimuli close by (as compared to far; [START_REF] Makin | Coding of visual space during motor preparation: approaching objects rapidly modulate corticospinal excitability in hand-centered coordinates[END_REF][START_REF] Serino | Motor properties of peripersonal space in humans[END_REF] or as approaching [START_REF] Finisguerra | Moving sounds within the peripersonal space modulate the motor system[END_REF] results in the hand modulating the excitability of the cortico-spinal tract so as to implicitly prepare a potential reaction. When fake left or right hands that subjects have an illusory sense of ownership are similarly approached, the corticospinal modulation occurs as rapidly as within 70 milliseconds from vision of the approaching stimulus. What is more interesting is that this time delay is sufficient for the peripersonal processing to distinguish between right and left hands [START_REF] Makin | Left or right? Rapid visuomotor coding of hand laterality during motor decisions[END_REF].

is precisely the kind of fast processing one would need to decide which hand of one's body is in danger and needs to be withdrawn. In short, having a dedicated sensory mechanism that is specifically tuned to the immediate surroundings of the body and is directly related to the motor system is a good solution for detecting close threats and for self-defence.

There are, however, many ways that one can interpret how one perceives danger in peripersonal space. We shall here briefly sketch three possible accounts:

(i) Sensory account: the sensory content includes the property of danger;

(ii) Attentional account: attentional priority is given to some low-level properties of the sensory content;

(iii) Affective account: sensory content that is combined with affective content;

The first interpretation is simply that the perceptual content includes the property of danger along properties of shape, colour, movement and so forth. One way to make sense of this liberal hypothesis is that danger is conceived here as a natural kind. On this view, when you see a snake next to your foot, you visually experience not only the snake but also the danger. The difficulty with such an account, however, is that too many things can be dangerous. Although there are many different kinds of snakes, there is still something like a prototypical sensory look of snake. By contrast, there is no obvious sensory look of danger. In brief, there is a multidimensional combination of perceptual features (e.g., shapes, colours, sounds…) characterizing threats and it is impossible to find a prototype. Furthermore, what is perceived as dangerous depends on one's appraisal of the context. Unlike natural kinds, danger is an evaluative property.

One may then be tempted by a more deflationary interpretation in attentional terms. It has been suggested that we have an attentional control system that is similar on many respects to bottom-up attention but that is specifically sensitive to stimuli with affective valence [START_REF] Vuilleumier | Affective and motivational control of vision[END_REF]. It occurs at an early sensory stage, as early as 50-80 milliseconds after stimulus presentation. Consequently, it can shape perceptual content by highlighting specific information. This attentional account is fully compatible with a conservative approach to perception. Indeed, giving attentional priority to some elements of the perceptual content is not the same as having perceptual content representing danger as such. However, one may wonder whether this deflationary account suffices to account for our phenomenology. There is a sense indeed in which we can experience danger. In addition, attention can possibly help in prioritizing tasks, but would attention help in interpreting the affective valence within such a short delay?

A possibility then is to propose that we experience danger not at the level of the perceptual content, but at the level of the associated affective content. On this last interpretation, perception can have a dual content, both sensory and affective. One may then talk of affective colouring of perception (Fulkerson, forthcoming) but this remains relatively metaphorical. A better way to approach this hypothesis might be by considering the case of pain. It is generally accepted that pain has two components, a sensory component that represents the location and the intensity of the noxious event and an affective component that expresses its unpleasantness.

One way to account for this duality in representational terms is known as evaluativism [START_REF] Bain | What makes pains unpleasant?[END_REF]: pain consists in a somatosensory content that represents bodily damage and that content also represents the damage as being bad. One may then propose a similar model of understanding for the visual experience of the snake next to one's foot: danger perception consists in visual content that represents a snake and that content also represents the snake as being bad.

We believe that a careful analysis of the results on peripersonal space and its link to selfdefence should shed light on this debate, and, more generally, to raise new questions about the admissible contents of perception.

The series of chapters included in the present volume directly or indirectly tap into these (and other) open questions about the nature of our interactions with the environment, for which peripersonal space is a key interface. Without aiming to be exhaustive, we hope that the contributions collected here will provide an updated sketch of the state of the art and provide new insights for future research.

3.

  Can there be cognitive penetration of peripersonal perception? At what stage does threat evaluation intervene? 4. Can there be a global whole-body peripersonal representation in addition to local body part-based peripersonal representations? If so, how is it built? What is its relation to egocentric space? 5. To what extent does peripersonal space representation, as a minimal form of the representation of the self in space for interaction, relate to allocentric representation of space for navigation?

TABLE P1 .

 P1 Experimental measures of peripersonal space

	Stimulus Modality	Stimulus Position	Response	Peripersonal Effect
	Visual & Tactile (V-	Visual and auditory at	Tactile detection	Better accuracy and/or
	T)	various distances or looming.		faster reaction times with
	Auditory & Tactile			V closer to the body
	(A-T)	Touch on the hand, head, or
		trunk
	V & A task irrelevant
	T target modality
	Visual & Tactile (V-	Visual at various distances.	Tactile discrimination	Slower reaction times with
	T)		(index or thumb)	V closer to the body
		Touch on index or thumb.
	V task irrelevant
	T target modality
	Visual	Visual at various distances.	Visual object detection or	Faster reaction times with
			discrimination	V closer to the body
	V target modality
	Visual	Visual at various distances.	Judgement if reachable by	Slower reaction times and
			the hand or not	accuracy at chance level
	V target modality		(without moving)	(50% i.e., threshold) when
				V presented at around arm
				length
	Strong tactile or	Touch at the hand, kept at	Hand Blink Reflex (HBR)	HBR increases at shorter
	nociceptive	various distances from the	(motor evoked response	hand-face distances
		face	recorded from the face)
	No task


Thomas Beck

Frédéric Boniol

Jérôme Ermont

Luc Maillet

Franck Wartel

An automata-based method for interference analysis in multi-core processors

INTRODUCTION

Multi-core processors (MCPs) provide huge gains that allows replacing several embedded single-core processors with a smaller number of computing platforms. However, such processors face important challenges to their integration in safety-critical systems. Due to resource sharing, unwanted interferences may exist between the tasks hosted by the different cores that can cause unexpected delays.

Certification authorities have published a set of recommendations [2] for designers who want to certify a MCP hosting a set of safety-critical tasks. Basically, this entails a two steps process: First, the designer must identify all the interferences that can occur between the tasks. Second, he must show that the severity of the interferences is compliant with the real-time requirements of the tasks. Many works have faced this interference challenge in MCPs. The reader can refer to [START_REF] Maiza | A survey of timing verification techniques for multi-core real-time systems[END_REF] for a large and complete overview of the scientific work on timing verification for MCPs. Among this work, a recent one [START_REF] Carle | Reducing timing interferences in real-time applications running on multicore architectures[END_REF] has shown that it is possible to characterise tasks running on MCPs by a timed sequence of Time Interest Points (TIPS), i.e., a sequence of instructions on which the tasks can suffer from interference.

We focus on the issue of the identification of interferences. We refine the notion of interference with two finer definitions, related to the type of the components causing the interference. And, considering that tasks can be abstracted by timed sequences of TIPS, we develop an the approach automata-based method, inspired from [START_REF] Gustavsson | Towards WCET analysis of multicore architectures using UPPAAL[END_REF], to count the interferences that can occur between the tasks hosted by the processor.

METHOD OVERVIEW

The landscape in which the method is involved is shown in Figure 1. Within this landscape, the scope of the paper is highlighted by the gray box. Let us consider a set of tasks τi hosted by a MCP (on the left of the Figure). The objective of the method is to compute an upper bound of the extra timing penalty associated with each τi due to interference occurring in the architecture (on the right part of the Fig- ure). For that purpose, we compute an upper bound of the maximal interference number (noted INi) from which each τi can suffer. The approach relies on the analysis method proposed by Carle et al. in [START_REF] Carle | Reducing timing interferences in real-time applications running on multicore architectures[END_REF] (on the left part of Figure 1). This method is based on the TIPS notion, which are load and store instructions that generate and suffer from interference. By means of static analysis, Carle et al. show that it is possible to extract a temporal segment sequence, as the one shown Figure 2, from the binary code of a task . Each segment is characterised by a duration (noted di,j for task τi and segment j), and the maximal number of memory requests leaving the core and sent to the bus (noted µi,j). These requests correspond to load and store operations that are not always hit in the L1 cache of the core hosting the task. For instance in Figure 2, τ1 makes zero memory request in segment one, and at most three requests in segment two. Such a sequence defines a bus access profile of the task under consideration. It characterises its worst-case memory activity that can lead to interference. As shown in Figure 1 we take these profiles as inputs, and we study an automatabased method to count, by model-checking, the interference that can occur for each τi .

DEFINITIONS

Let us consider an archetypal MCP architecture depicted Figure 3. This processor is composed of (1) two cores (C0 and C1) owning their private cache L1, (2) a shared cache L2, (3) a DDR memory composed of one bank B, and (4) a shared bus allowing the two cores to address the L2 cache and the DDR. Each core Ci hosts a task called τi.

Let us suppose that τ0 and τ1 are characterised by the profiles shown in Figure 4. They are divided into three segments. τ0 and τ1 can compete to access the memory in the first segment (from 0 to 10). In this segment, each task sends at most 2 memory requests. In the next two segments, either τ0 or τ1 does not send any request, leaving the memory path available for the other task.

Requests from τ0 and τ1 could collide in the bus. If they arrive at the same time, only one of them can pass, the second one must wait. The effect of the interference is a delay caused by a simultaneous collision.

According to the request path of τ0 and τ1, a second interference could arrive in L2. However, the intrinsic nature of this interference is different. Let us imagine for instance that τ0 reads a data from the bank B and put it in the L2 cache. As long as the data remains in the cache, each time τ0 accesses it, its request path ends with L2. Let us imagine now that τ1 reads another data from the bank B. According to the cache policy, data of τ1 could evict data of τ0 from L2. Consequently, the next time τ0 would try to access its data, it should have to lengthen its request path to the memory. The effect is similar to the bus interference: τ0 would suffer from a longer delay. However, the scenario of interference is different. There is no simultaneous collision. The two requests can occur at different time. In other words, τ1 provokes a delayed interference on τ0. As shown in this example, in order to analyze the interferences that can occur, it is necessary to identify the types of HW components. We consider two types of components: transport and storage component. As explained below instantaneous interferences can occur in AHB Bus Figure 3. And a delayed interference can occur in L2. In the same way, a second delayed interference can occur in B. Indeed, a memory bank is composed of a row buffer acting as a local cache. It contains the last block of accessed data. Hence, requests to data in the row buffer (one talks about "row hit" requests) are faster than request to data not in the row ("row miss" requests). When a "row miss" occurs, the requested data has to be fetched in the row buffer, making the request time longer. As in cache, the content of the row buffer depends on previous requests. Hence, banks memory are storage components that can cause delayed interferences.

MODELING

To model the interferences, we define two class of automata: automata for HW components, and automata for SW tasks.

HW components

The role of the HW component automata is to model the answers of the component to requests sent by the tasks. It determines if a request creates an interference in the component and notify the software automaton of this result. As said in section 3, we consider two types of HW components: transport and storage components.

Transport component

According to definition 1 a transport component is modeled by the automaton Figure 5. It is composed of three locations (Free, Occupied and Check ), and three internal data (waiting queue, nb elmt, and bus state):

• waiting queue is an internal list containing the identifiers of the tasks waiting for the component.

• nb elmt is the number of tasks currently waiting for the component (including the task currently using it).

The three locations of this automaton are:

• Free: This location is the initial one. The component is free and waits for a request. When a request arrives the location changes to Occupied, and the function update bus is called. This function updates the internal variables of the automaton to let it know which task is calling it.

• Occupied : The component is already occupied by a request. Once the request is completed the next location is Check if the internal waiting queue is not empty (nb elmt = 0) and Free if the queue is empty (nb elmt = 0). All outgoing transitions of the Occupied location are waiting for the synchronization event end req. This event is sent by a task automaton when it releases the component after its request has been completed by the memory.

• Check : This location's purpose is to switch the task whose request is handled by the component. It conversely within an unfavorable one (the interference case), when receiving a request. This notion of favorable delay is related to the task asking for the component. The automaton is composed of four locations:

• IDLE models the state where the component does not handle any request. It is waiting for a memory access request. When receiving it, it reaches Check delay and it calls the function check delay which determines whether there is an interference or not (i.e., whether the component is in an internal favorable state or not). check delay compares the state of the component with the state of the component if the task is alone. Further explanations on this function are presented in the section 5.

• Check delay is a transient location. Its role is to reach Normal delay or Interference delay according to the value of delay.

• Normal delay models the normal response delay of request. Once the request is completed the component returns to IDLE and waits for another request. Before returning to the IDLE, the automaton is waiting for an occurrence of end req sent by the task automaton processing the current request. When taking the transition to IDLE, the update st comp function is called to update the internal state variables of the component.

• Interferences delay: When an interference occurs the response delay is extended. This location represents the added delay induced by the interference. Thereby the next location has to be Normal delay because an interference is represented by an extra time added to the response delay.

Task automaton

A task automaton models the bus access profile of the task and the path of the requests through the HW components of the processor. Figure 7 gives the automaton of the task τ0. It is composed of three parts. The first (locations Request bus, Result bus, and Wainting bus) models the answer of the bus to the request. The second part (locations Request L2, Result L2, and Wainting L2 ) models the answer of the L2 cache. And and the last part models the answer of the DDR memory.

The initial location of the automaton is an Idle location. It waits for a start (or a end) event from the scheduler (that is, the automaton implementing the sequence of the segment of the task profile). When receiving a start event, the local counter nb req is set to zero. And the automaton reaches Request bus. The three parts follow then the same pattern composed of three locations:

• Request cmp (where cmp is bus, L2, or DDR) models the state in which the task has sent the request to the component cmp and is waiting to know if the request generates an interference. If there is an interference the next location is Waiting cmp. If there isn't an interference the next location is Results cmp.

• Waiting cmp models the extra delay the task encounters due to the interference.

• Results cmp models the case in which the request is normaly completed (with or without interference). The

Scheduler automaton

The last automaton models the sequences of segments of the task profiles. For instance, the profiles of τ0 and τ1 shown in Figure 4 are modeled by the automaton in Figure 8. From the initial location, the Scheduler starts the first segment of τ0 and τ1. Then it waits for an occurence of synchro sent by τ0 and τ1 at the end of their segment. When receiving them, the scheduler notifies the end of the first segment, and starts the second one. And so on until the last segment (when num segt == nb segt).

INTERFERENCE ANALYSIS

To determine when an interference occurs we instrument each component automaton with an algorithm to decide whether or not the task accessing the component is suffering from an interference. These algorithms dependent on the type of the component under consideration.

Transport component. To count interferences in transport components is very simple: there is an interference iff the component is occupied and another request is waiting for it.

Storage component. For storage components, the decision algorithm is more complex. To determine if an access from a task X is affected by an access from a task Y, we not only maintain the "actual" state of the component but also the "virtual" state of the component for each task as if the task were alone. Such a "virtual" view of the component is only paying attention to accesses from the task it is associated to. It means that an access from task X to the storage component affects both the "actual" view and its "virtual" view of the component, but not the "virtual" view associated to task Y. Thereby, for each access to a storage component we are able to determine if there is interference or not by comparing the virtual view of the task with the actual view of the storage component. If there is a difference between these two views it means that an interference occurred. This algorithm is implemented by the function check delay() of each storage automaton. In the example of a L2 direct mapped cache with 16.384 lines of 32 bytes, our algorithm works on:

NEXT WORK

The next step is to conduct a set of experiments to validate the method. We plan to explore the GR740 processor: a quad-core processor based on SPARC V8 architecture. After this validation, the second work will consider finer placement and replacement policies in our cache model in order to capture more realistic configurations.

Figure 1 :

 1 Figure 1: Approach overview (the gray box highlights the scope of the paper, while dashed lines are out of the scope)

Figure 3 :Figure 4 :

 34 Figure 3: A simplified multi-core platform

Definition 1 .

 1 (Transport component) A transport component is a component whose the internal state only depends on the presence or absence of a request using it. If a request is using the component, then it is "occupied". Otherwise, it is "free". Definition 2. (Storage component) A storage component is a component whose the internal state depends on previous requests (including the current one if any). AHB Bus of Figure 3 is a transport component. Examples of "storage components" include caches and memory banks. The content of a cache depends on the previous memory requests that used it. It has a direct effect on the length of next memory request paths. Following the distinction, we refine the notion of interference. Definition 3. An instantaneous interference occurs whenever at least two requests sent by two different tasks collide on a same transport component. Definition 4. A delayed interference occurs whenever a request r sent by a task τ uses a storage component whose internal state has been made non-compliant with τ by another task τ .

  is a transient location reached when the current task occupying the component is releasing it and when another one is waiting for the component. The role of location is to trigger the transition returning to Occupied to process the next request. Once again the update bus function is called when returning to Occupied to change the internal variables of the component.

Figure 5 :

 5 Figure 5: Automaton of a transport component

Figure 7 :

 7 Figure 7: Automaton for task τ0 of example Figure 3

Figure 8 :

 8 Figure 8: Scheduler automaton

( 1 )

 1 an array cache array containing 16 384 lists of 32 bytes representing the current state of the L2 cache ; (2) one similar array task i array for each task i representing the virtual state of the cache if the task were alone. Let us consider two tasks. Imagine that task 1 requests a data stored in line 250. cache array[250] and task 1 array[250] are updated. Then task 2 accesses the same line, cache array[250] and task 2 array[250] are modified. When task 1 accesses again line 250, task 1 array[250] and cache array[250] are different meaning that task 1 suffers from an interference. Interference analysis by model-checking. To compute an upper bound of the number of interferences experienced by each task in each component for each segment, we use the UPPAAL model-checker. For instance, let us consider nb interf L2[0,τ0] the number of interference in the first segment of τ0 in cache L2 . UPPAAL shows that ∀[](nb interf L2[0,τ0] ≤ 3) that is, 3 is an upper bound of nb interf L2[0,τ0]. This computation takes less than one second (UPPAAL 4.1.24 running on a 3,2 GHz Apple M1 Pro with 32 Go DDR5).


Honorato Diógenes

Piva

Roger Honorato Piva

Sajjad Ghojavand

Alain Pautrat

Maxime Debost

Ernesto A Urquieta-González

Svetlana Mintova
email: svetlana.mintova@ensicaen.fr

D H Piva

Synthesis of core-shell magnetic nanoparticles containing ultra-small domains of silicalite-1

Keywords: crystallization, zeolite, core-shell nanostructure, adsorption, magnetic properties

Synthesis of core-shell nanostructures with magnetic core and zeolitic shell is an ongoing challenge. Herein, we present a strategy for preparation of -Fe2O3@mesoporous silica (mSiO2) core-shell nanoparticles containing ultra-small domains of silicalite-1 in the shell (-Fe2O3@mSiO2/Silicalite-1). The strategy consists in a solid-state reorganization of the precursor amorphous mSiO2 shell into silicalite-1 using a tetrapropylammonium hydroxide (TPAOH) as an organic structure-directing agent (OSDA) under mild hydrothermal (HT) conditions. The formation of sililicalite-1 crystalline domains has been investigated through the detailed characterization of products obtained at different times of HT treatment by XRD, Raman spectroscopy, FTIR, SEM with EDS, TEM and nitrogen physisorption. By careful tuning the time of HT treatment, the -Fe2O3@mSiO2/Silicalite-1 nanostructures are prepared.

Benefitting from the unique mesopores/microporous structure formed, the -Fe2O3@mSiO2/Silicalite-1 core-shell nanostructure shows superior adsorption capacity to remove aniline from aqueous solutions than the -Fe2O3@mSiO2. Moreover, the -Fe2O3@mSiO2/Silicalite-1 nanostructure is easily separated from aqueous solutions using magnetic separation technique within 1 minute.

Introduction

Magnetic core-shell nanostructures with porous shell hold a great promise for many applications, such as catalysis, separation, drug delivery, and sensing. The main advantage of using these structures is the facile separation from the reaction mixture through the use of an external magnet, which eliminates the necessity for cumbersome filtration and centrifugation procedures. [1] In addition, magnetically induced heating has been demonstrated to initiate chemical catalysis on demand. [2,3] In this respect, significant progress has been made in controlling the growth of porous shell on magnetic cores, particularly the mesoporous silica. [4,5] In contrast, the development of magnetic core with microporous shell, such as zeolite, remains a largely unexplored field. Zeolites, a series of microporous crystalline aluminosilicates, are widely applied in the fields of adsorption and catalysis, [6] and combined with magnetic nanoparticles appears to be a very promising prospect. For example, core@shell structures consisting of magnetite cores and zeolite silicalite-1 shells have been synthesized by Deng et al. [7] Due the high affinity for trypsin and excellent microwave-absorption ability of the magnetite cores, the author demonstrated that the core@shell structure is highly efficient for digestion of proteins. Magnetic zeolite -Fe2O3/TS-1 with core@shell structure has been prepared successfully and used as catalyst in photocatalytic degradation of phenol or 4chlorophenol and thiophene oxidation. [8] A more detailed description of each magnetic zeolite composite type and their application is presented in the recent review. [9] To date, three different synthesis methods have been used to synthesize core-zeolite shell structures; (i) seeded growth; (ii) direct synthesis and (iii) physical coating. [10] Cheng et al., prepared magnetic zeolite -Fe2O3/TS-1 with core-shell structure by using seeded growth method, and tested this nanostructure in the photocatalytic degradation of organic contaminants.

It is important to emphasize that achieving a core-shell material with a shell thickness below 100 nm is still a challenge by using seeded growth approach, since the minimum shell thickness is limited to the size of the zeolite seeds (40-50 nm). [10] Such a drawback can be overcome using in situ method, wherein a coating process is favored by the simultaneous nucleation, adsorption, and growth of seeds on the surface of the core particles, all performed in a one-pot synthesis. Using in situ method, some authors have reported the use of these magnetic zeolites in the removal of different contaminants. For example, García-Aguilar et al., [11] prepared ferrite core with a well-defined shell of zeolite ZSM-5 by submitting a suspension of TiO2-coated nickel ferrite nanoparticles to hydrothermal treatment in an aqueous precursor solution. Yu et al., [12] used magnetic particles impregnated with zeolite A to remove ammonium from water, whereas Liu et al., [13] added magnetite particles to a zeolite A precursor solution in order to produce magnetic zeolites for posteriorly application to remove heavy metal ions from aqueous solutions. Despite that the previous studies have demonstrated the formation zeolite shell on magnetic particles, the competition between nucleation centers in the bulk gel and on the surface of the core particle resulting in abundant formation core-free zeolite particles has been also reported. [10] An alternative, and more practical approach is the physical coating where the microsized-core particles are coated by zeolite crystals using a binder. [14] This approach allows circumventing some of the main disadvantages of the previously mentioned techniques, including core dissolution and core-free zeolite formation. Nonetheless, the use of binder can decrease the concentration of active sites and their accessibility. Additionally, this technique is mainly applicable to recover microsized particles as a core. [10] While recent works on core-shell nanostructures are becoming increasingly in quantitative and insightful, synthesis techniques to prepare core-zeolitic shell nanostructures (< 100 nm) with discrete, uniform, and finely tunable shell thickness without the generation of core-free zeolite particles are still challenging.

Herein, we present a procedure to obtain magnetic -Fe2O3@mSiO2 core-shell nanoparticles containing ultra-small domains of silicalite-1 (-Fe2O3@mSiO2/Silicalite-1). It was found that a mixture comprising Fe3O4@mSiO2 nanostructures and TPAOH as an OSDA under hydrothermal conditions enables a reorganization of amorphous TPA + -silicate composites in the shell and further formation of ultra-small domains containing the secondary building units (SBU) of silicalite-1. In an effort to elucidate the formation of intermediates during the recrystallization of mSiO2 into silicalite-1 zeolite, the structural, microstructural, and textural evolution of the products were characterized by X-ray diffraction (XRD), Raman spectroscopy, Fourier-transform infrared spectroscopy (FTIR), scanning electron microscope (SEM) with Energy dispersive X-ray (EDS), Transmission electron micrograph (TEM) and nitrogen physisorption. We also demonstrated that the -Fe2O3@mSiO2/Silicalite-1 products show promising functional properties such as superparamagnetism confirmed by the superconducting quantum interference device (SQUID) magnetometer measurements, and adsorption capacity for removing aniline from aqueous solution. These findings provide more options and insight into the prospective synthesis of nanosized core-shell structures containing zeolitic shell.

Results and discussion

Structural and textural characteristic of calcined samples

The main text of the article should appear here with headings as appropriate. The recrystallization of amorphous silica nanoparticles into silicalite-1 using tetrapropylammonium (TPA + cations as structure directing agent) in the presence of limited amounts of water has been previously reported. [15] We have used similar approach to recrystallize the mSiO2 shell initially growth on the Fe3O4 nanoparticles into Silicalite-1. Figure 1a shows powder XRD patterns of the calcined products collected after hydrothermal treatment for different periods of time. No new Bragg reflections have been observed for the products prepared up to 12 h of hydrothermal treatment, and the solid product shows mainly a broad band at 20-25° (2θ), characteristic of amorphous silica. Typical Bragg diffraction peaks corresponding to the MFI framework structure started to appear in the XRD patterns of the sample Fe@Si-24, and the intensities of XRD peaks sharply increased for the sample Fe@Si-24. These peaks corresponding to the crystallographic planes of silicalite-1. [16] The XRD pattern of the sample hydrothermally treated up to 24 h also shows an additional peak at around 35.34° (2θ) as indicated by an arrow in Figure 1. This peak can be observed in the as-prepared products (Figure S4). The XRD pattern of precursor magnetic core (Figure S5) contains a peak that can be indexed to the (311) plane of the cubic structure of magnetite (JCPDS card No. 79-0416). Similar peak is observed for the as-prepared and calcined Fe3O4@mSiO2 nanostructured samples (Figure S5). Noteworthy, magnetite and maghemite (-Fe2O3) have inverse-spinel structure with similar lattice spacing, and distinguishing between these two phases with diffraction is difficult for nanoparticles smaller than 20 nm. [17] Therefore, we further investigated the structure of the calcined product by Raman spectroscopy (Figure 1b), which allows distinguishing between different iron oxide compounds based on characteristic vibrational modes. [18] Three broad bands at 710, 483 and 350 cm -1 , which are assigned to A1g, Eg and Tg, phonon modes of maghemite, respectively, [19] are observed for the series of samples obtained up to 24 of HT. Therefore, the Raman data indicate the presence of γ-Fe2O3 phase in the calcined samples and support that the diffraction peak observed at 35.34° 2θ in the XRD patterns correspond to the (311) plane of the -Fe2O3 phase (JCPDS card No. 39-1346). These observations are in a good agreement with the phase transition of Fe3O4 occuring under thermal treatment. The importance of the SiO2 shell to hinder the phase transformation of -Fe2O3 to hematite (-Fe2O3) as a non-magnetic phase, during the calcination was shown. [20] Raman spectra also contain the characteristic bands of silicalite-1 at 825, 470, 373 and 292 cm -1 , [21] emerging for the sample Fe@Si-24 and consequently increases in the intensity for the sample Fe@Si-48. This result indicates that the crystallinity of silicalite-1 increased with increasing the hydrothermal treatment, which is in a good agreement with the XRD data. Structural evolution of calcined products was further investigated by FTIR spectroscopy (Figure 2a). Compared to FTIR spectrum of precursor Fe@Si (Figure S6), the FTIR bands at 474, 802, and 1100 -1300 cm -1 observed in the sample Fe@Si-3 can be assigned to the bending mode of Si-O, and symmetric stretch and asymmetric stretch of Si-O-Si, respectively. [22] A weak broad band at 592 cm -1 appears in the sample Fe@Si-6, which is assigned to the asymmetric stretching mode of the five-member rings (5MR) for MFI structure. [23] Noteworthy, this region overlaps with the bands at 636 and 577 cm -1 (Figure S6), assigned to the Fe-O stretching vibration of γ-Fe2O3 phase. [18] Therefore, a possible role of these bands cannot be excluded. With further hydrothermal treatment (sample Fe@Si-12), the band at 592 cm -1 is shifted to ca. 570 cm -1 , suggesting that the 5MRs are become more rigid, i.e. a shrinkage of 5MRs takes place. [23] This band has been assigned to the presence ultra-small domains containing SBU of silicalite-1. [24] In addition, the bending mode of Si-O shifts to 470 cm -1 , indicating a gradual increase in periodic structures. The band of 5MRs shifts to ca. 554 cm -1 and two bands at 1109 and 1235 cm -1 can be observed for the sample Fe@Si-24. The latter bands are assigned to the external Si-O asymmetric stretching band of silicalite-1 phase. With further prolonging the HT, the intensity of these absorption increased, which is indicating the long-range crystalline order and growth of silicalite-1 crystals. [23] Further in situ adsorption of isobutanol was carried out to identify the formation of short range crystalline order in the silicate structure containing 5MRs after short time of HT. [25] Figure 2b shows FTIR spectra of isobutanol adsorbed in the calcined samples, where the characteristic CH3 and CH2 stretching vibration bands between 3000 and 2800 cm -1 are evident. [26] For the Fe@Si sample, isobutanol is only adsorbed on silanol groups as indicated by the presence of a band at 3745 cm -1 . The intensity of this band decreases with increasing the time of hydrothermal treatment, and completely disappears for the sample Fe@Si-48. Furthermore, an increase in the intensity of the bands in the region 3000-2800 cm -1 is observed. Interestingly; a dramatic increase in the normalized peak area was observed for the samples Fe@Si-12 and Fe@Si-24 (Figure 2b, inset), indicating that most of the accessible micropores are formed simultaneously with the formation of ultra-small domains containing SBU of silicalite-1. The textural properties of the calcined samples were studied by N2 physisorption measurements (Figure 3). N2 physisorption isotherms of samples Fe@Si-3 and Fe@Si-6 exhibited hysteresis loops corresponding to the mesopores with a pore size of 17 nm (Figure 3 inset). These samples show very similar N2 physisorption isotherms, and a slight increase in the specific surface and mesopore volume (Table 1), when compared to the Fe@Si sample (Figure S7). As can be seen, the surface area of the samples increased with extending the HT treatment time from 6 h to 12 h. This result is attributed to the partial transformation of silica into zeolite and the generation of micropores in the silica shell (Table 1). The FTIR data are in a good agreement with this observation. The hysteresis loops due to mesopores and the sharp uptakes at low relative pressure, characteristic of micropores, simultaneously increased for the sample Fe@Si-24 (Figure 3). From the shape of the isotherm, we infer that a complex material containing micropores and mesopores are obtained after 12 and 24 h HT. We postulate that mesopores are further developed by partial dissolution of core-shell nanostructure as reflected in the broadening of the mesopore size distribution (Figure 3, inset). N2 physisorption Type IV isotherm with a very small hysteresis loop for the sample Fe@Si-48 is measured, indicating the presence of micropores predominantly (Figure 2).

Sample

Surface area a) (m 2 g -1 )

Micr. area b) (m 2 g -1 )

Vmicro b) (cm 3 g -1 )

Vtotal c) (cm 3 g -1 ) Fe@Si-3 132 -0.00 0.34 Fe@Si-6 143 -0.00 0.35 Fe@Si-12 226 30 0.03 0.43 Fe@Si-24 377 140 0.10 0.52 Fe@Si-48 478 295 0.13 0.29 a) Calculated by Brunauer-Emmett-Teller (BET) method from the adsorption data obtained at P/P0 between 0.05 and 0.25.

b) Micropore area and volume determined by t-plot method. c) Total pore volume determined via the single-point method.

Microstructural evolution of samples during the hydrothermal treatment.

The morphological change of the samples during the hydrothermal treatment was initially observed by SEM. Figure 4 shows selected SEM images of the microstructure of the precursor Fe3O4@mSiO2 and as-synthesized products after 12h, 24h and 48 h HT treatment. The Fe3O4@mSiO2 nanostructure (Figure 4a) consists of irregular aggregates formed by nanoparticles with spherical morphology (see TEM image in Figure S3a). No significant change in morphology was observed in the as-synthesized Fe@Si-12 samples (Figure 2a and Figure S8). Results from the XRD and Raman spectroscopy suggested the presence of silicalite-1 crystals domains for the sample Fe@Si-24 (Figure 1), and the SEM revealed the formation of crystals with a size of 100 -200 nm (Figure 4c). The silicalite-1 crystal are embedded in the solid aggregates. However, large aggregates formed by micron-sized particles are also observed (Figure S8). According to the EDS elemental mapping, the Si-rich region is present in the assynthesized sample Fe@Si-24, while Fe and Si are homogeneously distributed in the assynthesized sample Fe@Si-12 (Figure S9). This result suggests a phase transition proceeds between 12 and 24 h HT treatment. Prolonged hydrothermal treatment to 48 h resulted in the formation of crystals with larger sizes of 250 -460 nm with crystal-like facets, characteristic of silicalite-1 solely prepared in at 90 °C. Particle size of the samples along the hydrothermal treatment were investigated by DLS (Figure 5). The hydrodynamic diameter of the as-synthesized sample Fe@Si-3 with an average diameter of 91 nm is almost identical to the initial material prior hydrothermal treatment; the sample has nearly monodisperse particle size distribution (Figure S2c andS2d). A gradual shift to lower average sizes from 88 to 68 nm is observed for as-synthesized samples Fe@Si-6 and Fe@Si-12, respectively. Two distinctive peaks corresponding to particles a hydrodynamic diameter of 62 and 262 nm are observed for the as-synthesized sample Fe@Si-24. These results are consistent with the SEM observation (Figure 4) showing the formation of micron-sized particles after 24h HT. Finally, a monomodal size distribution of crystals with an average hydrodynamic size of 360 nm is observed for the as-synthesized sample Fe@Si-48. In addition to DLS size distributions presented as a function of scattering intensity (Figure 5a), the size distributions as a function of number of particles is also shown. A decrease in the average hydrodynamic diameter from 51 nm to 39 nm for the as-synthesized samples Fe@Si-3 and Fe@Si-24, respectively, is observed (Figure 5b). Note that a monodisperse particle size distribution curve is measured for the sample after 24 h HT which is different from the DLS intensity representation curve (Figure 5b). This is due to the fact that, in the DLS size distribution by numbers, the presence of a few large particles is masked by the large number of small particles. This result suggests that the product prepared after 24 h HT is mainly formed by nanoparticles with the average hydrodynamic size of 42 nm. For the as-synthesized sample Fe@Si-48, particles with a diameter between 136 and 482 nm are formed (Figure 6b), which is in a good agreement with the particles size observed by SEM (Figure 4). DLS results show a decrease in the size of particles during the period 3 -24h HT that clearly suggest a gradual dissociation of the silica shell. We monitored the rate of shell leaching of the Fe3O4@mSiO2 particles under hydrothermal treatment for various times using DLS. The leaching rate is approximately 0.416 nm h -1 thus suggesting that the mSiO2 shell has been leached completely after 37 h HT. The combination of microscopy and DLS analysis suggests that silicalite-1 crystals are formed after 24 h HT. To further observe the dissolution of the mSiO2 shell and the microstructural evolution during the hydrothermal treatment, the as-synthesized samples were characterized by TEM (Figure 6).

The core-shell nanostructure was well preserved after the hydrothermal treatment as shown in the TEM pictures. The as-synthesized samples Fe@Si-6, Fe@Si-12 and Fe@Si-24 contain monodisperse particles with mean diameters of 40.6, 36.4, and 32.4 nm, respectively. These results are consistent with the DLS analysis (Figure 5b). With increasing the HT time, not only the particles size decreases, but the silica shell surface became significantly rough. The rough surface would reflect on the larger surface area than the smooth one. Indeed, a slight increase in the specific surface area is observed for the samples Fe@Si-6 and Fe@Si-12 (Table1). In addition, the mesoporosity of the silica shell becomes higher as the HT increases, which is in a good agreement with the N2 adsorption-desorption isotherms o the calcined products (Figure 3). Shell-free magnetic core is observed in the as-synthesized sample Fe@Si-24 thus confirming the dissolution of the mSiO2 shell (Figure 6c, inset). As shown by HR-TEM (Figure 6b, d andf), the hydrothermal treatment leads to morphological changes as well. Interestingly, local periodic structure is clearly visible for the as-synthesized sample Fe@Si-12 (dotted circles in Figure 6d), while in the case of as-synthesized sample Fe@Si-24, ordered arrays of crystalline microporous framework can be observed (Figure 6f). Small crystalline domains have been observed within the amorphous phase during the initial stage of crystallization of zeolites. [27] These results strongly demonstrate that ultra-small domains containing SBU of silicalite-1 in the sample obtained for 12 h HT treatment are formed within the mSiO2 shell, and further HT treatment improve the crystallinity of the particles, which is in accordance with FTIR analysis (Figure 2a). At 48 h HT treatment, the sample is composed by core-free silicalite-1 crystals and small aggregated nanoparticle (Figure S10). The crystalline structure of the small nanoparticles was confirmed by selected area electron diffraction (SAED) anaysis. The dot pattern of SAED corresponds to Fe3O4 (JCPDS card No. 19-0629) thus demonstrating that the hydrothermal treatment maintains the single crystal integrity of the core. On the basis of our observations herein, we propose a scheme representing the formation of ultra-small domains of silicalite-1 in the shell of the composites during the hydrothermal treatment (Scheme 1). Initially, most TPA + species are present in the liquid phase, while a portion penetrate into the mesoporous silica shell (stage i). Dissolution of the mSiO2 shell by etching occur during the hydrothermal treatment, yielding rough surface of the Fe3O4@mSiO2 and release of Si species (stage ii). Upon further hydrothermal treatment, reorganization of the TPA + -silicate species and formation of ultra-small crystalline domains containing the SBU of MFI type framework structure gradually proceeds (stage iii). After that, the TPA + -silicate shell grows via solid-state reorganization. As the mSiO2 dissolution proceeds, the nucleation of silicalite-1 nanocrystals is driven by supersaturation of silicate species in the solution (stage iv), which is in line with previous reports. [28] Finally, larger silicalite-1 crystals are formed leading to a complete deletion of mSiO2 shell and absence of core-shell nanostructure (stage v). It has been claimed recently that silicalite-1 crystal grows via a combination of classical and nonclassical mechanisms by the addition of both silicate species and nanoparticle precursors, respectively. [29] We do not discard that ultra-small domain had been detached from the shell and then aggregates to silicalite-1 crystal at longer hydrothermal treatment times. The solid rearrangement of the amorphous SiO2 shell into zeolite crystals may provide some guidance for further optimization of the synthesis process where other heteroatoms can be introduced in the structure of zeolite catalysts such as Al, B, Sn, etc. Scheme 1. Proposed scheme for the formation of -Fe2O3@mSiO2/Silicalite-1 composite during hydrothermal treatment. Water molecules and hydroxyl anions are omitted for clarity.

Evaluation of adsorption performance

This type of nanostructure containing micro or mesopores can serve as potential adsorbent owing to the unique shape selectivity adsorption and easy separation by using external magnetic field. A preliminary adsorption study was performed using SiO2/Silicalite-1 adsorbent for aniline removal from water. [30] The calcined samples were employed to adsorb aniline from an aqueous solution at pH 6.72 and room temperature. As shown in Figure 7a, the qt increases sharply with an increasing the contact time, indicating a very rapid adsorption reaction between aniline and the solid surface. Indeed, more than 95% of the maximum adsorption capacity was achieved within the first 5 min. The adsorption capacity of sample Fe@Si-12 reaches 13.17 mg g -1 , while similar values were achieved for the samples Fe@Si Fe@Si-3 and Fe@Si-6 (5.01, 5.42 and 5.98 mg g -1 , respectively). Further, the samples Fe@Si-24 and Fe@Si-48 show increased adsorption capacity to 27.4 and 58.2 mg g -1 , respectively. As previously reported, the molecular size of aniline is 0.59 nm which is similar to the pores size of silicalite-1 (0.56 nm). [31] Zeolites showed distinct adsorption affinity to molecules with molecular size similar to their pores due the so-called close-fit theory. [32,33] It is suggested that, the increase of qt is related to the presence of mesopores in sample Fe@Si-12 that facilitate the diffusion of aniline from the solution.

Further the magnetic properties of the samples were evaluated. The room-temperature magnetic characterization using a SQUID magnetometer indicates that the sample Fe@Si-12 possess a magnetization saturation value of 3.9 emug -1 (Figure 7b). The coercivity and the remanence were found to be 11.6 Oe and 0.06 emu g -1 , respectively (Figure 8b, inset), which revealed that the superparamagnetic behavior of the magnetic nanoparticle core remains after the hydrothermal treatment and calcination steps. However, the saturation magnetization is considerably lower than the bear magnetic Fe3O4 nanoparticle core (43.6 emu g -1 ), which is due to presence of non-magnetic shell. [22] Low saturation magnetization is also observed for the precursor Fe3O4@mSiO2 (see Figure S11). Although saturation magnetization is relatively low, the product prepared after 12h could still be easily collected using an external magnetic field (Figure 7c).

Conclusion

In summary, we have reported an approach to synthesize nanosized (< 100 nm) and narrowly size-dispersed -Fe2O3@mSiO2 nanoparticles containing ultra-small domains of silicalite-1 (-Fe2O3@mSiO2/Silicalite-1). The strategy is based on the solid-state reorganization of the amorphous SiO2 shell formed onto Fe2O3 into silicalite-1 using TPA + as an OSDA under hydrothermal conditions. The mechanism of formation of silicalite-1 shell during the hydrothermal treatment of the silica on -Fe2O3 is proposed. We also demonstrated that the calcined samples showed promising superparamagnetism, and the adsorption capacity towards aniline from aqueous solution is improved.

Experimental Section

Synthesis of Fe3O4@mSiO2 nanostructures: All chemicals were of analytical grade and used without further purification. Fe3O4 nanoparticles (NPs) with a uniform diameter of 13.8 nm (Figure S1) were obtained by thermal decomposition. [34] In a typical preparation, 4 g of iron oleate complex were added to a solution containing 0.32 mL of oleic acid (Synth, P.A.) and 12.5 mL of 1-octadecene (Aldrich, 90 %). The resulting brown solution was stirred for 30 min and transferred to a three-necked round-bottom flask and heated to 320 °C for 60 min under a N2 atmosphere. The solution was cool to room temperature, and the as-prepared solid was separated, washed several times with a mixture of hexane : acetone solution in a ratio of 1 : 4 (v/v), and finally dispersed in cyclohexane (5 mg mL -1 ) for further use.

Fe3O4@mSiO2 core-shell nanostructures with an average size of 50.7 nm and silica shell thickness close to 15.6 nm (see Figure S2 in the Supporting Information for additional details) were prepared by the reverse microemulsion method. [22] In a typical synthesis, 10 mL of Igepal CO-520 (Sigma-Aldrich) and 20 mL of the abovementioned Fe3O4 NPs dispersion were mixed with 160 mL of cyclohexane (Sigma-Aldrich, 99%) under ultrasonication for 10 min and then kept for 20 min under vigorous stirring. Subsequently, 2 mL of ammonium hydroxide (Synth, 25%-28%) was added dropwise under magnetic stirring. Finally, 4 mL of tetraethyl orthosilicate (Sigma-Aldrich, ≥99%) was added sequentially dropwise into the above suspension at a precisely controlled rate of 0.5 mL h -1 by using a Syringe Pump (KDS 100).

Four times fractionated drop was adopted. After stirring the resulting mixture for 36 h, the Fe3O4@mSiO2 nanostructures was precipitated using an excess of ethanol, washed several times with deionized water, and then stored in deionized water (5 mg mL -1 ) for further use.

Recrystallization of mSiO2 shell into Silicalite-1:

The recrystallization of mSiO2 shell into Silicalite-1 were performed under hydrothermal synthesis. In brief, 100 g the as-synthesized Fe3O4@mSiO2 nanostructures were separated by centrifugation and then dispersed in TPAOH (1M), to which deionized water was added in order to prepare a well-dispersed suspension. The molar composition of this precursor slurry is 25SiO2:4.5TPAOH:260H2O (where SiO2 precursor is mSiO2 shell). Adequate amount of Fe3O4@mSiO2 was used based on the ICP and TG results (Table S1). The resulting mixture was sonicated for 20 min and stirred for 30 min at room temperature. Then, the mixture was transferred to a 5 mL Teflon-lined autoclave and hydrothermally treated at 90 °C for different time (3, 6, 12, 24 and 48 h). After the prescribed synthesis time, the products were separated by centrifugation and subsequently washed with distilled water until the filtrate had an approximately neutral pH. The as-prepared products were dry at 80 °C overnight and calcined in air at 550 °C for 5 h. Calcined samples were named Fe@Si-h, where h is the time of hydrothermal treatment. The Fe3O4@mSiO2 precursor was calcined at same condition and named Fe@Si.

Materials characterization: X-ray diffraction patterns were recorded using a PANalytical X'Pert Pro diffractometer equipped with a Cu Kα X-ray source (40 kV, 40 mA, λ = 1.5418 Å).

Raman spectra were recorded on a confocal microprobe Raman system (Horiba Jobin Yvon Labram 300) at a fixed laser excitation of 532 nm. Dynamic light scattering (DLS) experiments were conducted on a Malvern Zetasizer Nano instrument equipped with a backscattering geometry (scattering angle of 173°, He-Ne laser with a wavelength of 632.8 nm). Fourier transform infrared (FTIR) spectra were recorded on a Nexus IR spectrophotometer using KBr.

The morphology of the solid products was observed using a Tescan Mira I LMH scanning electron microscope (SEM) under an accelerating voltage of 20 kV. Energy dispersive X-ray (EDX) analysis was performed using two Bruker XFLASH 6/30 EDX cameras. Transmission electron micrograph (TEM) images were obtained by a Philips FEI Tecnai G2 microscopy operating at 300 kV. Nitrogen physisorption analysis was carried out on a Micromeritics ASAP 2020 gas adsorption analyzer. Prior to analysis, the samples were degassed at 200 °C for 12 h.

In situ FTIR adsorption of butanol were recorded with a Nicolet 6700. Inductively coupled plasma mass spectrometry (ICP-MS) measurements were recorded using a 7900 ICP-MS from Agilent Technologies. TGA was performed with a thermogravimetry analyzer (SETARAM SETSYS 1750 CS) under oxygen atmosphere from 25 to 800 °C at a heating rate of 5 °C min - 1 . For the measurement of isobutanol adsorption IR spectra, the products were pressed into selfsupported disks (2 cm 2 area, 20 mg cm -2 ) and placed in an IR cell equipped with KBr windows. Spectra were recorded in the 400-4000 cm -1 range at 4 cm -1 with 128 scans. A homemade IR cell was used to evacuate the samples; then samples were heated up to 200 °C for 3 h under vacuum before the measurements. Precise amount of isobutanol (0.5 Torr) were introduced into the cell and kept in equilibrium for 10 min at 35 °C until their corresponding FTIR bands reached saturation. To allow the comparison of different products, the spectra were normalized to the samples' mass and plotted as absorbance per gram over the wavelength. Magnetization measurements were performed on a MPMS XL superconducting quantum interference device (SQUID) magnetometer at 25 °C.

Adsorption studies:

The main text of the article should appear here with headings as appropriate.

The adsorption of aniline was investigated using batch experiments at 25 °C as follows: 20 mg of adsorbents was dispersed into 20 mL of aqueous solutions with initial concentrations (C0) equal to 200 mg g -1 . The mixture was sonicated for about 1 minute to ensure uniform dispersion and then vigorously stirred for different time. The adsorbent was separated by using a magnet.

The supernatant solutions were subjected to UV-vis analysis (Varian Cary 4000 UV-vis spectrometer) for determining the equilibrium concentrations of aniline (Ct). The adsorption capacity, qt (mg g -1 ), was calculated as follows:

𝑞 𝑡 = (𝐶 0 -𝐶 𝑡 )𝑉 𝑀 (1) where Ct, V and M are the volume of the aqueous solution (L) and mass of the adsorbent (g), respectively (the calibration curve is presented in Figure S3). shell containing ultra-small domains of silicalite-1 (-Fe2O3@mSiO2/Silicalite-1) by the solidstate reorganization of the amorphous SiO2 shell formed onto Fe2O3 into silicalite-1 using TPA + as an OSDA under hydrothermal condition. Additionally, adsorption capacity towards aniline from aqueous solution is improved due the presence of ultra-small domains of silicalite-1.

Figure 1 .

 1 Figure 1. (a) XRD patterns and (b) Raman spectra of calcined samples after hydrothermal treatment for different periods of time (3 -48 h).

Figure 2 .

 2 Figure 2. (a) FTIR spectra of samples prepared for different crystallization time (3-48 h) and (b) in situ FTIR spectra of isobutanol adsorbed at 35 °C on calcined samples prepared for different periods of time (3-48 h) in comparison to the initial Fe@Si sample. Inset; normalized peak area.

Figure 3 .

 3 Figure 3. N2 physisorption isotherms of calcined samples after hydrothermal treatment for different periods of time (3-48 h). Inset: pore size distribution curves.

Figure 4 .

 4 Figure 4. SEM images of the precursor Fe3O4@mSiO2 nanostructure and of the as-synthesized samples after different HT times (scale bar = 500 nm).

Figure 5

 5 Figure 5 DLS particle size distribution curves expressed as a function of intensity (a) and number of particles (b) for as-synthesized samples prepared for different HT time (3-48 h).

Figure 6

 6 Figure 6 TEM images of as-synthesized samples (a,b) Fe@Si-6, (c,d) Fe@Si-12 and (e,f) Fe@Si-24. Arrows are pointing to different crystalline orientations of zeolite fringes in the mSiO2 shell.

Figure 7 .

 7 Figure 7. (a) Kinetic adsorption of aniline; (b) hysteresis loops measured at 300 K of the sample Fe@Si-12; and (c) a photo showing the magnetic separation by applying a magnetic field to the aniline solution in the presence of the sample Fe@Si-12 after 1 min (left).

  D. H. Piva*, R. H. Piva, S. Ghojavand, A. Pautrat, M. Debost, E. A. Urquieta-González, S. Mintova* Synthesis of core-shell magnetic nanoparticles containing ultra-small domains of silicalite-1

Table 1 .

 1 Textural properties of calcined samples after different time of HT treatment.

Acknowledgements

This work was funded by São Paulo Research Foundation (grant numbers 2020/02289-1 and 2018/25649-3) and the Centre for zeolites and nanoporous materials (CLEAR) supported by the Region of Normandy.

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Supporting Information

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

Conflict of Interest

The authors declare no conflict of interest.