Mickaël Jury

Annique Smeding

Nicole M Stephens

Jessica E Nelson

Cristina Aelenei

Céline Darnon

Mickaël Jury

Mickael Jury
email: mickael.jury@gmail.com

The Experience of Low-SES Students in Higher Education: Psychological Barriers to Success and Interventions to Reduce Social-Class Inequality 1

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The Experience of Low-SES Students in Higher Education: Psychological Barriers to Success and Interventions to Reduce Social-Class Inequality

At the same time that a college degree has become pivotal to success in society, access to a college degree is out of reach for many. Indeed, among students 25-34 years old in the United States, those whose parents do not have a high school degree represent 8.15% of the student population (OECD, 2014). This extends to contexts beyond the United States. In France, for instance, this student category represents 9.61% of the student population (OECD, 2014). In addition to the economic barriers students may face in their access to college [START_REF] Mourshed | PISA 2012 results: Excellence through equity: Giving every student the chance to succeed (Volume II)[END_REF], converging evidence shows that after students enter higher education, their socioeconomic status2 continues to influence their college experiences, academic achievement, and ultimately rates of graduation (e.g., OECD, 2014).

Past literature reviews and research reports that focus on differences in students' college experiences due to socioeconomic status have reported disparities in terms of college preparation, students' employment status while in college, and performance in college [START_REF] Aronson | Breaking barriers or locked out? Class-based perceptions and experiences of post-secondary education[END_REF][START_REF] Engle | Moving beyond access: College success for low-income, first-generation students[END_REF][START_REF] Merritt | First-generation college students: Then and now[END_REF][START_REF] Spiegler | First-generation students: What we ask, what we know and what it means: An international review of the state of research[END_REF][START_REF] Tym | First-generation college students: A literature review[END_REF][START_REF] Walpole | Socioeconomic status and college: How SES affects college experiences and outcomes[END_REF]. For example, first-generation students (i.e., students whose parents do not have a bachelor's degree or higher) tend to have a lower grade point average (i.e., GPA; Chen & Carroll, 2005;[START_REF] Harackiewicz | Closing the social class achievement gap for first-generation students in undergraduate biology[END_REF][START_REF] Stephens | Closing the social-class achieve-ment gap: A difference-education intervention improves first-generation students' academic performance and all students' college transition[END_REF] and are more likely to leave college without a degree than are continuing-generation students (i.e., students who have at least one parent with a bachelor's degree or higher; [START_REF] Deangelo | Completing college: Assessing graduation rates at four-year institutions[END_REF].

The present article discusses the potential psychological barriers that low-SES students face in higher education as a result of the foundational cultural practices that guide how universities function. Indeed, as noted by Aronson (2008, p. 50), "the vast majority of research on class differences in postsecondary education has focused on objective patterns of enrollment and attainment. Subjective processes are a black box that has not been fully explored." Therefore, the present article aims to fill this gap by reviewing research on the psychological barriers associated with students' SES in higher education. By psychological barriers, we mean students' emotional experiences (e.g., emotional distress, well-being), identity management (e.g., sense of belonging), self-perception (e.g., selfefficacy, perceived threat), and motivation (e.g., achievement goals, fear of failure). To identify all relevant articles, we conducted an exhaustive search with several databases (e.g., PsycINFO, Google Scholar), using search keywords such as: low-SES college students, low-income college students, first-generation college students, emotions, anxiety, stress, identity, sense of belonging, threat, stereotypes, self-efficacy, self-perception, motivation, achievement goals, as well as variations of these search terms. Additionally, we examined the reference lists of previous review articles (e.g., [START_REF] Aronson | Breaking barriers or locked out? Class-based perceptions and experiences of post-secondary education[END_REF][START_REF] Spiegler | First-generation students: What we ask, what we know and what it means: An international review of the state of research[END_REF][START_REF] Stephens | Unseen disadvantage: How American universities' focus on independence undermines the academic performance of first-generation college students[END_REF] in order to achieve a comprehensive list of articles. The first section reports the findings from research published within the last 20 years that illustrates the psychological barriers that low-SES students face in higher education.

The second section presents mechanisms recently identified in the social-psychological literature for how and why these psychological barriers, as well as differences in performance, are sustained. In particular, we focus on how specific university practices and cultural norms contribute to the production and reproduction of these psychological barriers.

Finally, the third section reports three recent psychological interventions that may increase the quality of low-SES students' experience in higher education and decrease performance disparities.

The Psychological Barriers Faced by Low-SES Students in Higher Education

Higher education is far from being a culturally neutral environment for low-SES students, notably because the system is "built and organized according to taken for granted, middleand upper-class cultural norms, unwritten codes, or 'rules of the game" (Stephens, Fryberg et al., 2012, p. 1178). As a result, when low-SES students enter contexts of higher education and face these unknown "rules," they can experience psychological barriers in (1) emotional experiences (e.g., emotional distress, well-being); (2) identity management (e.g., sense of belong-ing); (3) self-perception (e.g., self-efficacy, perceived threat); and (4) motivation (e.g., achievement goals, fear of failure). This section reviews these psychological barriers.

Emotional Experiences

In the context of higher education, low-SES students' emotional experiences are quite different from the emotional experiences of high-SES students, such that low-SES students are more likely to feel and express greater emotional distress than high-SES students. More precisely, first-generation students exhibit higher levels of self-reported depression [START_REF] Stebleton | First-generation students' sense of belonging, mental health, and use of counseling services at public research universities[END_REF][START_REF] Steptoe | Depressive symptoms, socio-economic background, sense of control, and cultural factors in university students from 23 countries[END_REF][START_REF] Wang | The role of generational status, self-esteem, academic self-efficacy, and perceived social support in college students' psychological well-being[END_REF] and lower self-reported well-being than do continuing-generation students (Jenkins, Belanger, Connally, Boals, & Duron, 2013;[START_REF] Padgett | First-generation undergraduate students and the impacts of the first year of college: Additional evidence[END_REF][START_REF] Tong | A study on general self-efficacy and subjective well-being of low-SES college students in a Chinese university[END_REF]. In addition, first-generation students report having fewer opportunities to talk about their negative experiences [START_REF] Barry | Differences in Self-reported disclosure of college experiences by first-generation college student status[END_REF] and are more likely to feel guilty about their educational achievement [START_REF] Covarrubias | Movin'on up (to college): First-generation college students' experiences with family achievement guilt[END_REF]. Such negative emotional experiences also appear to be reflected in students' physiological functioning: In lab contexts that seek to reproduce traditional college contexts, low-SES stu-dents demonstrate higher levels of physiological stress markers than do high-SES students [START_REF] John-Henderson | Performance and inflammation outcomes are predicted by different facets of SES un-der stereotype threat[END_REF][START_REF] Stephens | A cultural mismatch: Independent cultural norms produce greater increases in cortisol and more negative emotions among first-generation college students[END_REF].

Identity Management

Identity management is one of the toughest challenges low-SES students face when entering in the cultural context of higher education [START_REF] Jetten | When is individual mobility costly? The role of economic and social identity factors[END_REF]. [START_REF] Reay | Strangers in paradise"?: Working-class students in elite universities[END_REF] notably refer to this as an "out of field experience" (p. 1110)-"the idea that low-SES students in traditionally high-SES environments experience a feeling of disjointedness in navigating the college environment versus the environment they grew up in. Indeed, higher education is a new and often unfamiliar environment for low-SES students, and they often have difficulty embracing their new identity as college students [START_REF] Aries | The interactive relationship between class identity and the col-lege experience: The case of lower income students[END_REF][START_REF] Hinz | Upwardly mobile: Attitudes toward the class transition among first-generation college students[END_REF][START_REF] Lee | Out with the old, in with the new? Habitus and social mobility at selective colleges[END_REF][START_REF] Reay | Strangers in paradise"?: Working-class students in elite universities[END_REF][START_REF] Reay | Fitting in" or "standing out": Working-class stu-dents in UK higher education[END_REF][START_REF] Venus Moschetti | Social capital and academic motivation among firstgeneration community college students[END_REF].

In addition, as low-SES students are in the minority in higher education [START_REF] Alon | The evolution of class inequality in higher education: Competition, ex-clusion, and adaptation[END_REF][START_REF] Hearn | Socioeconomic diversity in selective private colleges: An organi-zational analysis[END_REF], their underrepresented identity is likely to be quite often salient to them, regularly reminding them of their differences from others (Orbe, 2004; see also [START_REF] Martin | Always in my face": An exploration of social class consciousness, salience, and values[END_REF]. Consequently, low-SES students regularly report feeling like they "do not belong" in the college context [START_REF] Harackiewicz | Closing the social class achievement gap for first-generation students in undergraduate biology[END_REF][START_REF] Ostrove | Social class and belonging: Implications for college adjustment[END_REF][START_REF] Pittman | Academic and psychological functioning in late adolescence: The importance of school belonging[END_REF][START_REF] Rubin | Social class differences in social integration among students in higher education: A meta-analysis and recommendations for future research[END_REF][START_REF] Soria | Social capital, academic engagement, and sense of belonging among working-class college students[END_REF] and are particularly prone to experience "imposter syndrome" (i.e., the feeling that they do not truly deserve to be there and that they fooled any-one who thinks otherwise, see [START_REF] Gardner | Those invisible barriers are real": The progression of firstgeneration students through doctoral education[END_REF]. Such a feeling might also be explained by these students being aware of their underprivileged background [START_REF] Sanders | Under the radar: The role of invisible discourse in understanding class-based privilege[END_REF]. Finally, in this process of negotiating their new identity, low-SES students perceive less support from their families [START_REF] Jenkins | First-generation undergraduate students' social support, depression, and life satisfaction[END_REF][START_REF] Sy | Parent support and stress among firstgeneration and continuing-generation female students during the transition to college[END_REF] compared to high-SES students.

Self-Perception

In higher education, low-SES students also have to deal with others' negative views of them. Indeed, much research shows that low-SES students suffer from negative stereotypes regarding their competence (i.e., low-SES individuals are perceived as less competent than high-SES individuals; [START_REF] Fiske | A model of (often mixed) stereotype content: Competence and warmth respectively follow from perceived status and competition[END_REF]; see also [START_REF] Durante | Poor but warm, rich but cold (and competent): Social classes in the stereotype content model[END_REF]Volpato, Andrighetto, & Baldissari, 2017).

Such negative stereotypes and associated practices have implications for students' psychological functioning. Notably, low-SES students have lower percep-tions of their competencies, report lower self-efficacy, and have lower self-reported perceptions of their own intelligence than do high-SES students [START_REF] Hellman | Academic Self-Efficacy: Highlighting the first-generation student[END_REF][START_REF] Ivcevic | The can and cannot do attitude: How self-estimates of ability vary across ethnic and socioeconomic groups[END_REF][START_REF] Kudrna | The influence of social class salience on self-assessed intelligence[END_REF]Ramos-Sanchez & Nichols, 2007;[START_REF] Stebleton | Breaking down barriers: Academic obstacles of first-generation students at research universities[END_REF]. These negative stereo-types negatively influence low-SES students' psychological functioning in terms of emotions and cognition in the college context (e.g., [START_REF] Croizet | Extending the concept of stereotype threat to social class: The intellectual underperformance of students from low socioeconomic backgrounds[END_REF][START_REF] Harrison | The consequences of stereotype threat on the academic performance of white and non-white lower income college students[END_REF][START_REF] John-Henderson | Performance and inflammation outcomes are predicted by different facets of SES un-der stereotype threat[END_REF][START_REF] Spencer | Social class is dead. Long live social class! Stereotype threat among low socioeconomic status individuals[END_REF]. Indeed, research on "stereotype threat" (for a recent review, see [START_REF] Pennington | Twenty years of stereo-type threat research: A review of psychological mediators[END_REF] shows that in situations where a group's negative stereotype is activated, members of the targeted group (e.g., African Americans on a test of intellectual abilities, [START_REF] Steele | Stereotype threat and the intellectual test performance of African Americans[END_REF] women on a math test, [START_REF] Spencer | Stereotype threat and women's math performance[END_REF] are likely to feel threatened by the stereotype. More precisely, the fear of confirming the negative stereotype increases the cognitive and the emotional load, which may ultimately hinder their ability to perform up to their potential.

These negative stereotypes also contribute to classism-that is, "negative attitudes, beliefs, and behaviors directed toward those with less power, who are socially devalued" (Lott, 2012, p. 654). Firstgeneration students can suffer from a form of classism that is specific to the higher education context [START_REF] Langhout | Classism in the university setting: Examining student antecedents and outcomes[END_REF][START_REF] Langhout | Assessing classism in academic settings[END_REF] and that strongly affects their life and academic satisfaction [START_REF] Allan | Outcomes of social class and classism in first-and continuing-generation college students[END_REF].

Motivation

Previous research suggests that some forms of motivation are more useful than others to succeed in higher education (i.e., approach-oriented goals; see [START_REF] Linnenbrink-Garcia | When are achievement goal orientations beneficial for academic achievement? A closer look at main effects and moderating factors[END_REF]. Recent work on social-class achievement gap in higher education demonstrated that low-SES students endorse more deleterious forms of achievement motivation (i.e., avoidance-oriented goals) than do high-SES students. More specifically, first-generation college students are more likely to be afraid of failure [START_REF] Bui | First-generation college students at a four-year university: Background characteristics, reasons for pursuing higher education, and first-year experiences[END_REF] and thus more likely to endorse performance-avoidance goals in college (i.e., trying not to be outperformed by others, [START_REF] Jury | When first-generation students succeed at university: On the link between social class, academic performance, and performance-avoidance goals[END_REF]Jury, Smeding, & Darnon, 2015) than do continuing-generation students. Moreover, in competitive departments (e.g., civil engineering), first-generation college students are more likely to ex-perience less fit and consequently, more difficulty in maintaining mastery-goal endorsement (i.e., desire to progress, to master tasks), compared with continuing-generation students [START_REF] Sommet | The student-institution fit at university: Interactive effects of academic competition and social class on achievement goals[END_REF]. Such motivation regulation may be particularly problematic for these students, due, notably, to the negative links usually observed between performance-avoidance goal endorsement and academic performance (Van Yperen, Blaga, & Postmes, 2014).

Summary

Taken together, the research reviewed in this first section suggests that low-SES students experience psychological barriers (e.g., emotional distress, identity-management issues, negative self-perception, and more damaging forms of motivation) that may help to explain their worse academic outcomes (e.g., taking fewer classes, higher drop-out rates, and lower GPAs)3 . The next section argues that the specific practices and cultural norms promoted in higher education play a key role in creating these psychological barriers.

Psychological Mechanisms Underlying Social Reproduction in Higher Education

We now turn to how the psychological barriers and the performance gaps experienced by low-SES students are maintained and reproduced. Recent social psychological research suggests that SESrelated differences in students' university experiences are best understood as a product of ongoing interactions between university settings and individuals [START_REF] Browman | The effects of warm or chilly climate toward socioeconomic diversity on academic motivation and self-concept[END_REF]Smeding, Darnon, Souchal, Toczek-Cappelle, & Butera, 2013;Stephens, Markus, & Fryberg, 2012). In particular, we argue that university contexts are set up in ways that advantage high-SES students but disadvantage low-SES students.

According to sociologist Pierre Bourdieu and his colleagues, the university system sustains the reproduction of social inequalities by promoting attitudes, speech, behaviors, and knowledge that are more congruent with the practices of high-SES families than of low-SES families [START_REF] Bourdieu | Reproduction in education, society and culture[END_REF]. Bourdieu andPasseron (1964, 1970) originally argued that low-SES students have a lower chance of success in higher education not only because of less access to economic capital (i.e., financial resources), but also because of less access to cultural capital (e.g., knowledge, behaviors, and values that can be more or less familiar to an individual and more or less promoted in a system; [START_REF] Becker | Cultural expressions of social class and their implications for ingroup-related beliefs and behaviors[END_REF]. According to Bourdieu, the parents of low-SES students lack familiarity with the dominant culture of the university system and as a result are unable to effectively transmit the "appropriate" cultural capital to their children [START_REF] Calarco | Coached for the classroom: Parents' cultural transmission and children's reproduction of educational inequalities[END_REF]; for a review on the difficulties between low-SES parents and school, see [START_REF] Lott | Low-income parents and the public schools[END_REF]. Below we describe two recent lines of research in social psychology that support Bourdieu's theories by documenting some specific university practices and cultural norms that contribute to the social reproduction of inequality.

First, the university's function of "selecting the best students" is one factor that contributes to the psychological barriers faced by low SES students. [START_REF] Darnon | Achievement goal promotion at university: Social desirability and social utility of mastery and performance goals[END_REF][START_REF] Darnon | Achievement goals in educational contexts: A social psychology perspective[END_REF][START_REF] Dornbusch | The social structure of schooling[END_REF]; see also [START_REF] Batruch | Re-establishing the social order: Restorative reactions against high-achieving low social class pupils[END_REF]. Generally, the educational system serves two distinct functions that can shape university practices. First, universities have a goal to impart knowledge and educate students (i.e., the education function). Second, they have a goal to select individuals who are "the most able and motivated" in order to sort them into positions that vary in their status (i.e., the selection function; Dornbusch et al., 1996, p. 405). To reach the goal of selecting the best students, different selection procedures can be used. For example, selection can occur at the admissions level (e.g., in the Ivy League schools, fewer than 10% of the applicants are admitted as freshmen) or through features of the curriculum (e.g., qualifying exams). All such procedures are driven by the goal to select the students who are ostensibly the most deserving. However, as compared to the education function, the selection function consistently advantages high-SES students [START_REF] Alon | The evolution of class inequality in higher education: Competition, ex-clusion, and adaptation[END_REF][START_REF] Mijs | The unfulfillable promise of meritocracy: Three lessons and their implications for justice in education[END_REF][START_REF] Smeding | Reducing the socio-economic status achievement gap at university by promoting mastery-oriented assessment[END_REF] and might act as a threat to low-SES students' identity. Illustrating this, one experiment asked students to read a text explaining that universities either have the function of selecting the most deserving students or have the function of helping all students succeed. When the university's function was described as selection, first-generation students performed worse than continuing-generation students on a math task (Jury et al., 2015; see also [START_REF] Smeding | Reducing the socio-economic status achievement gap at university by promoting mastery-oriented assessment[END_REF]. However, when the university's function was described as education, there were no performance differences between first-and continuinggeneration students.

Second, taken-for-granted cultural norms in university settings can create psychological barriers for low-SES students, and, in turn, fuel the social-class gap in students' performance. Universities, particularly elite institutions, tend to promote independent values as the cultural ideal (e.g., learn to express yourself, learn to work independently) and are less likely to support interdependent values (e.g., learn to work with others, learn to adjust to others' expectations; Stephens, Fryberg et al., 2012;Stephens, Markus et al., 2012). Although independent values tend to be relatively congruent with how high-SES students view themselves, these values are relatively less congruent with the interdependent understandings common among low-SES students [START_REF] Stephens | Social class culture cycles: How three gateway contexts shape selves and fuel inequality[END_REF]. Consequently, when exposed to university materials framed independently, first-generation students feel less comfort and fit, experience greater levels of stress, and ultimately do not perform up to their potential (Stephens, Fryberg et al., 2012;[START_REF] Stephens | A cultural mismatch: Independent cultural norms produce greater increases in cortisol and more negative emotions among first-generation college students[END_REF].

To summarize, these lines of research illustrate how university contexts can contribute to the reproduction of social inequalities and psychological barriers faced by low-SES students. First, they do so by institutionalizing "selection" as one of their purposes (i.e., in addition to the educational purpose), and second, by promoting cultural norms that align with high-SES students. The next section presents studies that have tested psychological interventions that seek to reduce the achievement gap between low-and high-SES students in higher education. Of course, for several reasons, including the fact that the achievement gap is susceptible to appear far before entering higher education [START_REF] Sirin | Socioeconomic status and academic achievement: A meta-analytic review of research[END_REF], eliminating the achievement gap is a challenge. Nevertheless, some interventions based on the above psychological mechanisms have proved to be effective in reducing this achievement gap.

Three Psychological Interventions Designed to Reduce the SES Achievement Gap in Higher Education

When considering how to reduce social-class achievement gaps in higher education, researchers and practitioners should recognize that these gaps are based not only on individual factors, but also the larger university contexts that students inhabit. This section presents three promising interventions that require little time and money to implement in college settings (for an additional example of psychological intervention, see [START_REF] Yeager | Teaching a lay theory before college narrows achievement gaps at scale[END_REF].

Self-Affirmation

Harackiewicz et al. (2014; see also [START_REF] Tibbets | Affirming independence: Exploring mechanisms underlying a values affirmation intervention for firstgeneration students[END_REF] conducted an intervention that sought to reduce the social-class achievement gap using self-affirmation, which has proven to be effective in reducing stereotype threat [START_REF] Cohen | Reducing the racial achievement gap: A social-psychological intervention[END_REF]. In this intervention, first-and continuing-generation students completed a writing exercise in their biology class: They read a list of values (e.g., independence, belonging to a social group) and chose either the two or three most important values for them (in the self-affirmation condition) or the two or three least important values (in the control condition). Then, students explained why these values were important to them (in the self-affirmation condition) or why these values might be important for someone else (in the control condition). At the end of the semester, first-generation students who had affirmed their values earned higher grades than those who did not affirm their values. This effect appeared both on the grades obtained in the biology class and on final overall GPA. Moreover, the intervention reduced firstgeneration students' concerns about their background. Thus, providing lower SES students with the opportunity to express their own values helped them to recover a sense of integrity, legitimacy, and fit in the academic context [START_REF] Cohen | The psychology of change: Self-affirmation and social psychological intervention[END_REF].

Difference-Education

Stephens et al. ( 2014) developed a novel intervention approach to reduce the social-class achievement gap, referred to as "difference-education." In this intervention, firstand continuinggeneration first-year students participated in a 1-hour panel where older students discussed their experiences in college. In the difference-education condition, panelists shared their stories of how they adjusted to college, and they did so in a way that highlighted how their SES shaped their experiences. In the control condition, panelists told similar stories of college adjustment, but did not discuss the role of their backgrounds.

At the end of the school year, a follow-up survey found that first-generation students who attended the difference-education panel had higher grades and sought more academic resources than those who attended the control panel. Additionally, in a follow-up laboratory study conducted at the end of the second year, all students (regardless of SES) who attended the difference-education panel were more comfortable discussing their backgrounds in a speech task than students who attended the control panel [START_REF] Stephens | A differenceeducation intervention equips first-generation college students to thrive in the face of stressful college situations[END_REF]. First-generation students who attended the difference-education panel were also better able to cope with stressful academic situations compared to first-generation students who attended the control panel. In sum, by helping all students to better understand how their SES matters in college, the difference-education intervention increased all students' comfort with their different backgrounds in university settings. At the same time, the intervention equipped first-generation students with the tools that they needed to overcome the challenges they faced in university settings.

Goal Reframing [START_REF] Smeding | Reducing the socio-economic status achievement gap at university by promoting mastery-oriented assessment[END_REF] conducted an intervention that sought to reduce the social-class achievement gap by changing the meaning of an exam. Specifically, they reframed its purpose to highlight the learning function of the exam instead of its selection function. Therefore, in this intervention, the exam was presented either as a tool to improve knowledge (i.e., a learning tool, "This exam has been designed to help students in their long-term learning in statistics") or as a tool to identify differences in students' abilities (i.e., a tool for selection, "This exam has been designed to compare students regarding their long-term learning in statistics"). Consistent with the idea that current exam practices foster the social-class achievement gap [START_REF] Autin | Social justice in education: How the function of selection in educational institutions predicts support for (non)egalitarian assessment practices[END_REF], results showed that when the exam was presented as a tool for selection, low-SES students performed worse than high-SES students. However, when the exam was presented as a tool for learning, low-SES students performed just as well as high-SES students. Therefore, reinforcing the formative, learning-oriented function of an exam can reduce the achievement gap and help low-SES students to perform up to their potential.

Limitations and Future Directions

This article highlights the psychological barriers that low-SES students en-counter on the pathway to higher education. However, the psychological barriers faced by low-SES students in the college context are only part of the story, and this review is limited in not thoroughly discussing other aspects that influence low-SES students' psychological experiences. For example, researchers should also consider students' early experiences within the school system, as well as their racial or ethnic backgrounds.

Limitations

Low-SES students face barriers far before they enter higher education. The Program for International Student Assessment (PISA) regularly highlights the discrepancy in performance between low-and high-SES students at elementary or secondary school (OECD, 2013; for a meta-analysis see [START_REF] Sirin | Socioeconomic status and academic achievement: A meta-analytic review of research[END_REF]. One reason for this discrepancy is that many low-SES children do not benefit from strong support at home (e.g., have access to fewer books, less cognitive stimulation from their parents, [START_REF] Evans | The Environment of Childhood Poverty[END_REF] to develop their academic skills in math or reading [START_REF] Bradley | Socioeconomic status and child development[END_REF][START_REF] Mcloyd | Socioeconomic disadvantage and child development[END_REF]. Another factor is that low-SES students already have a long experience of classism and threat in the school system (Desert, Preaux, & Jund, 2009;[START_REF] Regner | Stereotype threat in children: Past and present[END_REF]. These discrepancies in early experiences hinder low-SES students' preparation for college (Chen & Carroll, 2005) and also contribute to the gap between low-and high-SES students in obtaining higher education.

Additionally, students have multiple identities that may interplay with their SES in the higher education context. Indeed, many low-SES students are also racial or ethnic minorities [START_REF] Spiegler | First-generation students: What we ask, what we know and what it means: An international review of the state of research[END_REF]. Intersectionality of race/ethnicity and SES can produce issues such as double stigmatization. For example, [START_REF] Ivcevic | The can and cannot do attitude: How self-estimates of ability vary across ethnic and socioeconomic groups[END_REF] showed that low-SES students perceived their abilities as lower than high-SES students and that this effect was qualified by an interaction with ethnicity: Students who perceived themselves as having the lowest abilities were those who were both low-SES and members of ethnic minority groups (for another example of an interactive effect see [START_REF] Covarrubias | Movin'on up (to college): First-generation college students' experiences with family achievement guilt[END_REF]. Thus, examining how students' SES interacts with ethnicity or gender (for an example, see [START_REF] Jenkins | First-generation undergraduate students' social support, depression, and life satisfaction[END_REF] in explaining students' psychological experience in higher education is an avenue for future research.

Future Directions

The present article also makes salient that more research is needed to better understand the specific ways in which universities contribute to social inequalities. As the second section highlights, universities tend to promote cultural norms of independence (e.g., "pave your own path") rather than interdependence (e.g., "be part of a community"). Future work could examine how other ideas and practices that are part of the university culture might also fuel the social-class achievement gap and the barriers faced by low-SES students. For example, do universities endorse certain mindsets about the nature of intelligence (e.g., an entity theory) more than others (for an example of intervention based on a growth mindset of intelligence, see [START_REF] Yeager | Teaching a lay theory before college narrows achievement gaps at scale[END_REF]?

In addition, although much research, including the research reviewed in this article, focuses on low-SES students in university settings, more work needs to examine a broader range of students, as well as their experiences over time and beyond college. For example, the specific experiences of high-SES students are rarely investigated. How might these students fare when faced with the additional concerns that come with the possibility of downward social mobility [START_REF] Jetten | Fear of falling: Economic instability enhances collective angst among societies' wealthy[END_REF]? Moreover, examining the impact of college experiences on subsequent outcomes, such as graduate school or the workplace, might address how consequential these college outcomes are for students' subsequent career success and lifetime well-being.

Finally, two of the described interventions have been implemented in high-ranking universities. Future work should consider whether these types of interventions are equally effective at community colleges or lower ranked universities that enroll a much higher proportion of low-SES students. A better understanding of what makes these types of psychological interventions beneficial, and under what conditions, is crucial to increase their effective implementation across a wide variety of contexts.

Conclusion

As the Great Recession has increased the need to obtain a university degree, providing all students with equal chances to succeed in higher education has be-come a topic of rising interest for scholars and policymakers alike. Recently, the American government (White House Report, 2014, p.2) stated that "[Americans] need to reach, inspire, and empower every student, regardless of background, to make sure that [America] is a place where if you work hard, you have a chance to get ahead." Such meritocratic beliefs are shared and promoted in many countries [START_REF] Jost | Antecedents and consequences of system-justifying ideologies[END_REF][START_REF] Pratto | Social dominance orientation: A personality variable predicting social and political attitudes[END_REF][START_REF] Hing | The merit of meritocracy[END_REF], and in academic contexts in particular (Duru-Bellat & Ten-ret, 2012). In spite of this claim, and as shown in the present article, research consistently documents that in higher education meritocracy is an "unfulfillable promise" [START_REF] Mijs | The unfulfillable promise of meritocracy: Three lessons and their implications for justice in education[END_REF]. Indeed, the research reviewed in the present article sup-ports the idea that low-SES students not only face economic barriers but also psychological barriers to success in higher education. Thus, providing economic resources to low-SES students and facilitating their access to higher education are necessary steps for reaching more equality in higher education but are certainly not sufficient. Indeed, even if the economic obstacles are overcome, low-SES students may still experience more threat, more health problems, more negative emotions, and lower levels of motivation than their high-SES counterparts. Such "self-debilitating" motivations and behaviors [START_REF] Sidanius | Social dominance: An intergroup theory of social hierarchy and oppression[END_REF] may in turn produce and maintain the SES achievement gap, resulting in a vicious cycle. Thus, in addition to economic policies designed to help low-SES students to get access to universities, psychological interventions and institutional changes are a necessary and complementary way to minimize the barriers faced by low-SES students and reduce the SES-achievement gap.

This paper has been accepted for publication in the Journal of Social Issues in

This version is a postprint.

Students' socioeconomic status (SES) refers to family rank in society, based on income, parental education, or occupation[START_REF] Markus | Introduction: A wide-angle on the psychology of social class[END_REF]. This article reviews research using various measures of students' SES (income, occupation, education.

The negative psychological experiences described in this section are not limited to elite universities. Indeed, the research reported here was conducted in different kinds of colleges (e.g., private, public,

4-year, 2-year, selective, not selective, community colleges). Low-SES students seem to struggle even in institutions that are quite heterogeneous and that have missions to serve low-SES students (e.g., community colleges,[START_REF] Fike | Predictors of first-year student retention in the community college[END_REF][START_REF] Hellman | Academic Self-Efficacy: Highlighting the first-generation student[END_REF][START_REF] Pascarella | Experiences and outcomes of first-generation students in community colleges[END_REF].


Fatima Douma

Louwanda Lakiss

Oleg I Lebedev

Julien Cardin

L Krassimir

Jaafar El Kostov

Valentin Fallah

Mohamad Valtchev

El-Roz

Krassimir L Kostov

Jaafar El Fallah

Valentin Valtchev

Mohamad El-Roz
email: mohamad.elroz@ensicaen.fr

Silver quasi-nanoparticles: bridging the gap between clusters molecular-like and plasmonic nanoparticles

come   L'archive ouverte pluridisciplinaire

Introduction

The Downsizing the dimension of metallic particles to the nanometer, sub-nanometer and quasi-atomic level provides a new ground for achieving novel structural, electronic and optical properties. One of the consequences of reducing the size of the metallic particles to less than the Fermi wavelength is their molecular-like behavior with intralevel energy exceeding the thermal activation energy (KBT), which gives rise to size-dependent HOMO-LUMO band gap comparable with that of semiconductors. Thus, the layer-dependent transitions of silver clusters render them optically active emissive centers 1,2 . However, the interaction between the incident light, at a specific wavelength, and metal nanoparticles (MNPs) could induce collective oscillations of the conduction electrons, producing a Local Surface Plasmonic Resonance (LSPR) with no direct luminescence properties 3 . These behaviors make them very attractive for various applications in solar-cell 4 , imaging 5 , biomedecine [6][7][8] , (bio)sensing 9,10 , etc.

Several recent research studies aimed to prepare M-NPs and metal clusters (M-CLs) with ordered shape and size. [1][2][3][4][5][6][7][8][9][10] They focused on tuning the metal clusters physicochemical properties (size, morphology, charge) to improve their performance. The crucial key to reaching this objective is to control the metal atoms' selfassembling processes and stabilize their nucleation at a well distinct stage.

In the case of M-CLs, one of the main challenges is their stability, as they are susceptible to agglomerate into larger particles due to their high surface energy. Typically, organic stabilizing agents (micelles formation with M-NPs core) or porous host support are used to ensure their stability 11,12 . Several recent works show that the use of zeolite as a host environment is one of the most convenient strategies for promoting the formation of well-defined M-CLs. For instance, embedding few silver atoms and clusters in the welldefined dielectric cavities of zeolite reveals novel optical properties with respect to the plasmonic silver nanoparticles (Ag-NPs). This behavior is dependent on multiple factors, including the size, geometry, degree of hydration, and the electrostatic interaction between the metal and the local host environment 13,14. The silver clusters (Ag-CLs) in the zeolite pores are usually prepared by thermal treatment of zeolite exchanged with silver cations at a relatively high temperature (>450°C). The final materials' optical properties strongly depend on the framework topology and charge density of the zeolite, its counter-ions, the degree of silver exchange, and their hydration states 15,16 . However, a very low concentration of silver is required in order to minimize the migration of the silver clusters and their agglomeration into large particles. Therefore, only a few isolated clusters can be obtained with interesting emission behavior in visible light.

Recently, we demonstrated that Ag-CLs can be prepared under soft irradiation conditions at RT 17 . We showed that a UV irradiation of a suspension of faujasite X (FAUX) doped with vanadium oxide clusters (ZX-V), promotes a local reduction of silver nitrate into Ag-CLs, via a photocatalytic-like process in the presence of ethanol as an electron donor. Due to the limited loading of the highly dispersed vanadium oxide clusters in the zeolite, only isolated Ag-CLs and some external Ag-NPs were obtained. Herein, we report an innovative and selective strategy for preparing Ag-CLs in FAUX doped with Bi 2+ /Bi 3+ cations via a local photoassisted electrochemical reduction of Ag cations. The successful preparation of highly crystalline zeolite with abundant Bi active sites and the localization of these later in sodalite and double six-membered ring (6MR) prism offered a unique opportunity for bridging the reduced silver clusters through the zeolite channel instead of isolated clusters obtained previously. Contrary to our previous photocatalytic process, the new process is a photo-assisted electrochemical approach that can be even performed in dark. With the new approach, the optical behaviors of Ag-CLs can be easily tuned by adjusting the amount of Bi without changing the host environment.

Experimental

Materials:

The following chemical products were purchased and used without further purification: Al powder (325 mesh, 99.5 %, Alfa Aesar), NaOH (VWR Chemicals-Prolabo, 99 %), SiO2 (Ludox-AS 30, 30 wt. % SiO2, pH=9.8, Aldrich), Bi(NO3)3.5H2O (99.9%, Aldrich), Ag(NO3) (99.9%, Aldrich). The Ag@ZX-Bi sample was prepared using the synthesis procedure that was previously reported in our recent work for the preparation of Ag@ZX-V [Error! Bookmark not defined.]. First ZX-Bi is prepared by in situ incorporation of bismuth as a metal precursor for photoactive species in the synthesis mixtures:

Synthesis of ZX-Bi:

As a first step, the nanosized ZX-Bi-x samples were synthesized according to sol-gel method without employing an organic template (x: represents the weight percentage, which varies between 0.5 and 10 wt.%, relative to the weight of alumina and silica). In a typical synthetic procedure, sodium aluminate and sodium silicate are prepared separately (denoted solutions A and B, respectively). The sodium aluminate was prepared by adding ̴ 0.5 g of sodium hydroxide to 3 g of water in a plastic bottle. Then the aluminum powder was slowly dispersed under vigorous stirring until this latter is completely dissolved. After that bismuth (III) nitrate pentahydrate (Bi(NO3)3.5H2O) was introduced into the solution and was sonicated for 30 min to completely dissolve the Bi(NO3)3. Sodium silicate is prepared concurrently by adding 1 g of NaOH to 1 g of water and 10 g of colloidal silica suspension at room temperature. The obtained sol was heated for 7 min at 90°C until a clear sol was formed and kept under stirring. Solution A was added dropwise into the solution B under intensive stirring after placing this later in an ice bath in order to control the exothermicity of the reaction. The formed sol-gel mixture was aged for 14h to 24h at room temperature. The water content was adjusted using a freeze dryer to avoid aggregation. The solution was then crystallized at 50° C for 24h. The molar composition of the clear gel is given as: 10 SiO2: 1.1 Al2O3:

x Bi: 9 Na2O: 122H2O (x = 0.066, 0.20, 0.40, 0,66 atm.% equivalent to 0.5, 1.5, 3.0 and 5.0 wt.% of Bi in respect to (Si+Al), respectively). Then, very stable suspensions were obtained. The solid was recovered by centrifugation (25 min, 20000 rpm) followed by redispersion in double-distilled water. The procedure was repeated until reaching pH~8. It has been observed that the crystallinity and the purity of ZX-Bi are strongly affected by the amount of bismuth precursor added to the initial synthesis mixture. In order to obtain a high crystalline and pure ZX-Bi sample, the synthesis parameters such as the aging and crystallization times, the amount of water eliminated by freeze-drying and the Bi content have been optimized. The optimization processes and the results are detailed in the supplementary information. To estimate the crystallinity of zeolite samples, a Bi-free highly ZX sample is used as reference. Then the crystallinity of the as-synthesized samples was estimated by comparing the integrated area of the reflection peaks of ZX-Bi relatively to their corresponding peaks in the pure ZX.

Preparation of Ag@ZX-Bi:

Zeolite samples with different content of Bi are prepared to investigate the effect of Bi content on the evolution of silver species formation. Ag@ZX-Bi samples were prepared by dispersing ZX-Bi (2.4g/l) in pure water or in water solutions containing ethanol and silver nitrate (10 mM) with a ratio of ethanol/water=1/3. Upon UV irradiation (Hg-Xe lamp; polychromatic irradiance = 205 mW/cm 2 ; infrared cut filter), the color of the suspension changes from white to pinke, confirming the reduction of silver Agn n+ to Agn δ+ clusters (with δ<n). The optical penetration depth of zeolite-based structures has been evaluated leading to determine that UV radiations are transmitted at least partially through the walls of these structures. Thanks to the known refractive indexes of the zeolite material's constituent (H2O, SiO2, Al2O3), we estimate that the optical penetration depth, defined as the depth at which the intensity of the radiation inside the material, falls to 1/e at different UV wavelengths ranging from 240 to 365 nm. Penetration depths were found to range from 7.8 nm in pure Al2O3 to 6 meters in pure water at 365nm. These extreme values frame the depth of penetration into the walls of zeolite-based structures. In view of the dimensions of the zeolite-based structures, it can therefore be concluded that the UV radiation is transmitted at least partially through the walls of these structures. In addition, the concentration of the zeolite suspension (2mg/ml) as well as the optical pathway (10-20 cm) was optimized (based on our previous study) in order to obtain a very reproducible results (many grams of Ag@ZXBi-3 was prepared and correspond to a treatment of 10's of samples).

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Characterization techniques: UV-vis spectroscopy measurements were performed using a Cary 4000 UV-Vis spectrophotometer at room temperature in transmission mode using a quartz cuvette with a 1 cm path length. The absorbance spectra were corrected by subtracting the spectrum of pure zeolite (ZX). The DR-UV-Vis spectra of the dried powder samples were recorded using RSA-CU40 Diffuse Reflectance cell. The spectrum of the non-irradiated sample was subtracted from the spectra of irradiated samples. The chemical composition of the samples was measured on a Varian Vista AX CCD inductively coupled plasma atomic emission spectrometer (ICP-AES).

The continuous wave (CW) photoluminescence (PL) measurements were carried out at room temperature using two excitation lasers: a Crylas FQCW266 emitting at 266 nm with an average power of 27 mW, and a coherent Innova 90C argon laser with an average power of 150mW at 488nm. The excitation was done with an incident angle of 45° on a beam spot size of about 1 mm² and chopped at 3.00 Hz for samples. Emitted photons were collected by means of a set of lenses until a Horiba Jobin-Yvon Triax 180 monochromator. At the exit of this monochromator, a R5108 Hamamatsu photomultiplier tube ensures photon detection. This detected signal is amplified by means of a Femto DLPCA-200 trans-impedance amplifier and send to a SR830 lock-in amplifier referenced at the excitation laser chopper frequency. Different High pass filter cutting at 500 nm or 700 nm were used respectively to prevent the collection of excitation line by the detection system and prevent eventual second-order contributions. PL spectra were measured on sample powder stacked between two quartz Suprasil(TM) glass slides from Hellma Analytics. The water content and stability of the samples were investigated by Thermogravimetric analysis (TGA) using a SETSYS instrument (SETARAM) analyzer (heating rate of 5 °C min -1 under 40 ml.min -1 flow of air). The structural and morphological features of the samples were investigated with a transmission electron microscopy (TEM) using an aberration probe and image corrected JEM ARM200F cold FEG microscope operated at 200 kV equipped with a CENTURIO EDX detector and GIF Quantum spectrometer. Specimens for transmission electron microscopy studies were dispersed in ethanol and drop cast onto a Cu holey carbon grid. The crystallinity and phase purity of zeolite samples were studied by powder X-ray diffraction analysis (PXRD) using a PANalyticalX'Pert Pro diffractometer with CuKα-moy wavelength (λ = 1.5418 Å). The PXRD diagrams were recorded at room temperature between 3 and 50° (2θ) with a step size of ~0.014°(2θ). Variable divergent slits with a constant illuminated sample length of 6 mm were used. Phase identification was performed with a PANalyticalHighScore plus program. Nitrogen sorption analyses were performed on a Model ASAP 2020 apparatus. After degassing at 200 °C under vacuum for 12 h, the samples were then cooled, and nitrogen adsorption is performed. Specific surface areas were determined from BET equation. The total pore volume is the total volume adsorbed at P/P°=0.99. The t-plot method was used to distinguish the micropores from the mesopores in the samples. The pore size distribution (PSD) was derived from the desorption branch of the N2 isotherm using the Barrett-Joyner-Halenda (BJH) algorithm.

Aluminum MAS NMR experiments were performed on a 500 MHz Bruker AVANCE III spectrometer operating at 130.32 MHz for 27 Al, and equipped with a double resonance 4 mm probe head. X-ray photoelectron spectroscopy (XPS) measurements were carried out on an AXIS Supra electron spectrometer (Kratos Analyical Ltd.) with base vacuum in the analysis chamber in the order of 10 -8 Pa. The samples are irradiated with monochromatized Al Kα radiation with photon energy of 1486.6 eV. The photoemitted electrons were separated, according to their kinetic energy, by 180° -hemispherical analyzer with a total instrumental resolution of 0.6 eV (as measured by the FWHM of Ag 3d5/2 line) at pass energy of 20 eV. Due to charging effect, a resolution of ≈1.0 eV has been measured on the selected samples. Energy calibration was performed by normalizing the C 1s line of adventitious adsorbed hydrocarbons on silver folio 284.6 eV. The diameter of the analysis area was 700x300 µ 2 .

Operando IR experiments was performed using 'sandwich like' IR cell-reactor 18 . The FITR spectra of the samples (shaped as a pellet of ~ 20 mg with 65 µm of thickness) were recorded under Ar and at different temperatures. The employed ThermoNicolet NEXUS 670 FTIR was equipped with an MCT detector with a spectral resolution of 4 cm -1 and time resolution of 1 spectrum/s. The irradiation of the samples with visible light was performed using a Xe-lamp (LC8 Hamamatsu, 200 W) equipped with a cut-UV filter (l>390nm).

For CO-adsorption, the sample, activated in an in-situ IR cell, was first cooled to -173°C under vacuum. Then, small doses of 0.5-10 µmol of CO were introduced to the cell. IR spectra were recorded after each dose. IR spectra were recorded with a Nicolet Magna 550-FT-IR spectrometer at 4 cm -1 optical resolution. Prior to the measurements, the samples were pressed into self-supporting discs (diameter: 1.6 cm, 20 mg) and pretreated in the IR cell attached to a vacuum line (10 -6 Torr), at 100°C for 18 h. The boundary element method (BEM) approach presented by F. J. Garcia de Abajo et al. 19 and developed by U. Hohenester et al. 20 in MNPBEM MatLab toolbox was used in order to model the far-field response of singular silver objects. The silver nanostructures models were built based on the TEM data, which help to localize and determine particle sizes of Ag clusters. Then, the absorption, scattering, and extinction cross-sections sabs, ssca, and sext of the structure models have been calculated. The silver nanostructures in Faujasite structure are reproduced by the alternation of large diameter spherical silver nanoparticles (LNp; 1.1 nm) for the sodalite cages and small diameter (SNp; 0.7 nm) for the hexagonal prisms. The effect of Nps interconnection was investigated by comparing the response of silver nanostructure with isolated and interconnected nanoparticles. Silver nanostructures were also simulated with the increasing number of Faujasite unit cells of 1, 2, 5 and 10 to reproduce the different structures with various lengths observed by TEM. The surrounding medium was considered as water with constant permittivity (e=1.332) and silver permittivity was taken from reference [21]. It should be underlined that the simulations simplify our experimental system. For example, the global charge of the clusters, the presence of Bi 3+ , the zeolite-Ag interaction, and the silver Nps size may change the silver permittivity with respect to the one of bulk silver in the standard. Moreover, supplementary This journal is © The Royal Society of Chemistry 20xx

Please do not adjust margins Please do not adjust margins orientation effects and overlapping of many objects (silver nanostructures) may complicate the plasmon response. Although this simulation will not give a response strictly identical to that observed in complex experimental systems, its contribution is representative and significant for the interpretation.

Results and discussion

Highly crystalline ZX-Bi samples were obtained after an extensive optimization of the synthesis parameters. It should be noted that ZX-V is used in this work as a reference. Details about the ZX-V sample can be found in ref. 17 and the supporting information. The XRD results demonstrate that the presence of Bi in the synthesis media significantly affects the crystal growth kinetics of zeolite X (Fig. 1 and Figures S1-S3). We attribute this effect to the additional charge. Thus, the presence of bismuth reduces the crystallization time from 24h to 8 h, compared to the Bi-free sample (ZX) prepared under similar synthesis conditions. The maximal amount of bismuth introduced in the synthesis media without perturbing the crystallization processes is 3.0 wt.% (in respect to the Si and Al amounts) (Fig. 1). For simplification, the "x" values in the ZX-Bi-x samples correspond to the amount of Bi initially introduced. For a higher bismuth concentration (> 5 wt.% Bi), the crystallization of Faujasite is altered, and sodalite arises as the major crystalline product (Table S1; Figure S1-S3). Thus, at larger amounts the Bi perturbs the structure directing role of Na + and consequently the final zeolite product. 22 The XRD pattern of ZX-Bi-3 didn't show any evidence of the presence of crystalline Bi oxide (Fig. 1). The broadening of the XRD peaks is due to the nanosized dimensions of zeolite particles as shown by the DLS analysis. The hydrodynamic diameters of ZX and ZX-Bi particles is 45 and 50 nm, respectively (Figure S4). Hence, ZX-Bi shows similar crystallinity of the parent zeolite ZX with no extra-framework alumina detected (Figure S5), when it is prepared under the optimal conditions cited above. According to the ICP analysis, the bismuth amounts in ZX-Bi-0.5, ZX-Bi-1.5 and ZX-Bi-3 samples are 0.38, 1.15 and 2.3 wt.%, respectively (equivalent to 0.18, 0.33 and 0.74 atm. %). The Si/Al ratio was in the range of 1.3 to 1.5 in all samples, in agreement with the EDX results (Table S2). The loss of the Bi during the synthesis is relatively low (around 25%) in respect to that previously observed when vanadium precursor was used (around 90%) 17 . This is probably due to the counter-ion role that Bi n+ plays for compensating the negative charge of oxygen lattice.

The textural properties of the ZX-Bi-3 compared with the reference ZX sample are assembled in Fig. 2. The N2 adsorption data analysis of ZX and ZX-Bi-3 sample (Fig. 2A) shows a combination of I and IV type isotherms with a large hysteresis H1 type loop. The latter corresponds to a material with textural porosity originating from packed uniform in size nanoparticles 23 . The slightly lower micropore volume of ZX-Bi compared to pure ZX (Fig. 2B) might be due to a slightly lower crystallinity of the sample (95% in respect to the pure ZX). It should be noted that the pore size distribution for ZX and ZX-Bi-3 (Fig. 2B) in the mesopore range is identical pointing out a similar particle size and regular textural porosity. The thermal analysis of ZX sample (Fig. 2C) shows water weight loss with two endothermic peaks at around 80°C and 140°C that correspond to the dehydration of physiosorbed and structural water, respectively. However, the elimination of water in the ZX-Bi sample takes place at a relatively higher temperature (125°C and 200°C). This could be related to the higher water affinity of multivalent Bi cations.

The electron microscopy study (Fig. 3 and Figure S6) confirmed the presence of highly dispersed Bi in the zeolite. It is challenging to localize the Bi particle with precision in the zeolite structure base on the TEM analysis, which is due to their low concentration and high mobility under the electron-beam. In this respect, the HAADF-STEM is considered a more appropriate technique due to the atomic Please do not adjust margins Please do not adjust margins number dependence (~ Z 2 ) and sensitivity to a small amount of material (down to single atoms). The HAADF-STM images reveal the presence of ordered Bi atoms along the [112] plane (Fig. 3a), which are mainly located in the sodalite cage and double six-member ring (6MR) prism (Figure 3b-d).

Ag@ZX-Bi-3 samples were prepared by UV irradiation of the ZX-Bi-3 suspensions in the presence of AgNO3. Then the samples were washed with deionized water three times in order to remove the non-reacted silver nitrate. The XPS analysis performed on ZX-Bi-3 and Ag@ZX-Bi-3 samples shed more light on the Bi's nature before and after Ag + reduction. In the case of the parent sample (ZX-Bi), in addition to the main signal observed at 153.3 eV and attributed to silicon (Si 2s). The high-resolution XPS spectra (Fig. 4) shows characteristic peaks of Bi 3+ and Bi 2+ : 1) the Bi 4f core level spectrum with two spin-orbit doublet peaks (J=5/2 and 7/2) centered at 165 (Bi 4f5/2) and 159.5 eV (Bi 4f7/2) with a peak separation of 5.5 eV, and 2) the pair of peaks at lower binding energies 163 and 157.3 eV with a peak separation of 5.7 eV, respectively 24-26 The chemical composition obtained with XPS is very close to that obtained by ICP and EDX analyses, confirming the homogeneous distribution of Bismuth cations. The unexpected detection of Bi 2+ in ZX-Bi-3 sample reveals a chemical reduction of Bi 3+ precursor during the synthesis process. The reduction of Bi 3+ to its lower-valence bismuth ions usually occurs after thermal treatment at a relatively high temperature under anaerobic atmosphere conditions 27 . The low synthesis temperature (50°C) excludes the thermal reduction hypothesis, which is probably due to the in in-situ loading of Bi in presence of metallic alumina in the synthesis, characterized with a very low standard potential (-1.67 V SHE, for Al 3+ /Al at 25°C) in respect to Bi 3+ /Bi (+0.317V SHE at 25°C). The XPS analysis shows that Bi 2+ and Bi 3+ coexisted with Bi 2+ /Bi 3+ ratio of 2.3. This result was confirmed on two different ZX-Bi-3 samples (with an error of below 10 %). We anticipate two different positions of Bi 2+ and Bi 3+ counter cations compensating the (AlOSi) -charges of zeolite framework; Sun et al. 28 demonstrated by high-resolution synchrotron XRD analysis of dehydrated zeolite that Bi + with an ionic radius of 1.45 Å occupies single-type site I' in the sodalite cage of FAU-type zeolite, where it is coordinated with three oxygen atoms of the 6MR prism. However, the characteristic ionic radius of Bi 2+ and Bi 3+ are 1.16 and 0.96 Å (vs 1.0 Å for Na + ), respectively 28 . Considering the ionic radius, it is reasonable to presume that Bi 2+ and Bi 3+ ions could occupy the sodalite cage and the hexagonal prism of the FAUX, respectively. However, the XPS results of Ag@ZX-Bi reveals the presence of Bi 5+ with two characteristic peaks at 160.1 and 166.2 eV (Figure 4b) 29,30 . This result indicates that a redox reaction occurs between the Bi 2+ /B 3+ and Ag + . It is important to mention that similar result was obtained in the absence of ethanol (used as an electron donor in case of Ag@ZX-V; Figure S7). Also, similar results was recorded after 30 days in dark (with lower absorbance intensity), showing a different process of silver reduction in comparison with Ag@ZX-V (Figure S8). Hence, a photo-assisted redox process between Bi 2+ /Bi 3+ and Ag + is the most credible explanation supported by the selective detection of Bi 5+ on Ag@ZX-Bi samples.

Fig. 5 shows the time progression of the Ag@ZX-Bi UV-visible spectrum versus the UV-irradiation time. A distinctive color change from milky white to magenta can be clearly observed (Insert Figure 5). The absorbance band positions indicate the presence of isolated Ag-CLs (Agn d + ) with different nuclearities (≠n) inside FAUX. These Ag-CLs have a characteristic absorbance in the UV region, similar to that observed previously for Ag@ZX-V samples (Figure S8; Table S3). The broad absorption extended over the 266-310 nm region, is usually assigned to the electronic transitions of few-atoms containing silver ionic clusters (Agn δ+ ), e.g., Ag3 + , Ag4 2+ , ionic Ag8 δ+ 31,32 . The shoulders around 295 and 360 nm are characteristic fingerprints of hydrated Ag4 2+ . 33 A distorted symmetry due to the interaction with water molecules explains the Ag4 2+ absorption spectrum splitting with maximums at 295 and 360 nm 34 . A similar effect is most probably the origin of broad peak at 360 nm 35 . The super-atom orbitals model suggests that the Ag4 2+ electronic transitions are S-P transitions resulting from the contribution of 5S-based orbitals, i.e., from a doubly symmetric occupied orbital (S-Orbital) to an unoccupied orbital (P-orbital) 36 . The well-defined peak at 320 nm is usually assigned to the presence of the atomic Ag8 0 in the sodalite cage 37 . Each of the eight silver atoms is pointing toward one six-membered ring, thus forming a distorted cube inside the sodalite cages. The band at 380 nm is close to the characteristic absorption bands of silver species containing approximately seven nuclei embedded in zeolite cages 38,39 . It should be noted that the band's intensities reflect the abundance of the different Agn δ+ species (with n>δ), which can have different extinction coefficients.

Comparing to the metallic silver, the XPS analysis of Ag@ZX-Bi shows a shift to the higher bending energy (Figure S9-B), confirming the clusters/nanoparticles nature of the Ag particles. 40 The auger spectra (Figure S9-C) show two broad bands with kinetic energy near 349 nm and 354 nm with a shift of ≈2 eV compared to metallic silver. This demonstrates the ionic state of silver clusters. 41,42 This result is also confirmed by the IR analysis, using CO as probe molecule, which demonstrates the presence of both metallic and ionic silvers (Figure S10). Therefore, based on these results and the TEM data we conclude that silver particles are in form of reduced Agn d + clusters with a partial cationic charge (n>d). Contrariwise to the UV region, Ag@ZX-Bi illustrates a very distinctive optical behavior in the visible range than that usually observed for the classical Ag-CLs and/or plasmonic Ag-NPs confined/supported in/on the zeolite. Interestingly, the spectra of Ag@ZX-Bi sample shows new bands at around 460, 502 and 535, 600 and 675 nm, which might be the origin of the magenta color. The shallow pink color has been previously reported on Bi-doped FAU zeolite after intense thermal treatments and the absorbance intensity is generally proportional to the Bi + concentration 43 . Bi + was characterized with two significant NIRabsorption bands at 500 and 700 nm and promoted by the thermal reduction of Bi 3+ . However, no Bi + in Ag@ZX-Bi is detected, as reveals the XPS characterization of the sample and reported previously (Fig. 3). Therefore, we conclude that the appearance of these new bands in the visible region, results from a strong interaction of the zeolite ligand and the reduced silver clusters by a transitional charge transfer (CT) and/or to a strong connection between the silver clusters through the zeolite channels [START_REF] Gurin | [END_REF] . Similar optical features were observed for the brick-red samples prepared upon dehydration of Ag containing zeolites A [START_REF] Tuzovskaya | Advances in the Understanding and Application of Catalysts[END_REF]34,44 . It is important to note that the UV-visible band positions are not affected by the nature of the sample (powder or suspension). However, the bands' relative intensity is dramatically modified, showing a solvatochrome effect of the liquid water (Figure S11-A). We should also mention that the Ag@ZX-Bi sample, compared to Ag@ZX-V, is very stable. The analysis repeated eight months after the preparation, for both suspension and powder forms, showed no significant alteration in their UV-visible spectra and crystallinity (Figure S11-B). To the best of our knowledge, such an exceptional optical stability is not reported for silver species (clusters or nanoparticles) up to date.

units)

Si 2s

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A set of experiments was performed to better understand the Ag@ZX-Bi-3 sample behavior. First, the role of Bi n+ was investigated to highlight the characteristic of Ag@ZX-Bi. For this purpose, suspensions containing ZX, ZX-Bi-0.5, ZX-Bi-1.5 and ZX-Bi-3 were exposed to UV irradiation for 15 min in the presence of equimolar amounts of silver nitrate (Figure S12). No significant absorbance is observed for the pure zeolite showing the absence of any silver clusters. The UV-visible spectra Ag@ZX-Bi-3 and Ag@ZX-Bi-1.5 exhibit two mains narrow bands at 318 and 408 nm and differ from the ZX-Bi-0.5 spectrum (Figure S12). The 318 and 408 nm bands intensities are lower in case of Ag@ZX-Bi-1.5 with the appearance of the characteristic bands in the visible region, which is much more pronounced for Ag@ZX-Bi-3. On the other hand, zeolites prepared under similar conditions but at different crystallization times (tcristal) are used to prepare a series of Ag-CLs-containing ZX-Bi-3 samples. The ICP analysis proves that the Bi content and Si/Al ratio is similar for all samples (Table S4). As shown in Figure S13, the increase of the FAUX samples' crystallinity (tcristal<10h) impacts the abundance of the Ag-CLs but without a significant effect on their nature. However, when sodalite is the major phase (Figure S4g, tcristal=48h), the optical behavior of the sample is dramatically changed. Therefore, there is a direct relationship between the zeolite framework morphology and the optical behavior of Ag@ZX-Bi-3 samples. The undirect contribution of Bi cations on the Ag@ZX-Bi optical behavior can't be totally excluded. However, the variation of the optical behavior (and not only on the absorbance bands' intensities) with the Bismuth concentration allows to exclude an important impact of the bismuth. E.g. if the Bi is impacting the optical behaviour, the variation of its concentration would affect the intensity instead of the band positions (wavelength). This conclusion is fully supported by the simulation of the cluster absorbance. Connecting the silver via the prismatic cages shifts the absorbance bands from near visible to the red region. The effect of the zeolite topology is also clear. The optical behavior changes between the FAU type and Sodalite type frameworks. On the other hand, it is still clear that the optical behavior of Ag@ZX-Bi sample depends on the specific topology of the faujasite structure. Namely, the FAU supercages are connected through double six-member rings forming hexagonal prisms. This structural unit is absent in the sodalite structure. In Ag@ZX-Bi, Ag + has a strong affinity for the site I and I' that are located in the hexagonal prism faces between the sodalite cages 13 , while Bi n+ occupies mainly the sodalite cages. The positioning of Ag in the 6MR prism, in addition to the abundance of the Bi cations in the sodalite cages, contributes to the formation of Ag clusters and thus ensures the interconnection between silver clusters. This connection does not exist in the sodalite structure and consequently, the silver clusters remain isolated. On the other hand, the relatively low concentration of Bi active sites in FAUX leads solely to the formation of isolated Ag-CLs. Therefore, the increasing of the Bi 3+ /Bi 2+ active sites concentration might promote the connectivity of the Ag-CLs and the formation of quasi-AgNPs. This is due to the increase of the reduced silver concentration reducing then their inter-distance. The continuous wave (CW) photoluminescence (PL) measurements shed more light on the optical behavior of the Ag@ZX-Bi-x samples (x= 0.5, 1.5 and 3). The measurements were carried out at room temperature using two excitation lasers at 266 nm and 488 nm. For excitation at 266 nm, we observe several PL bands, depending on the Bi content. The most intense band position is at 2.06eV (600 nm). The less intense bands are observed respectively at 2.22eV (558 nm) and at 2.35eV (527nm). Upon excitation at 488 nm, new PL bands at 2.67eV (464 nm) and at 1.39eV (892 nm) are observed with very low intensity; the later are attributed to the high Bi content (Fig. 6 a and b). The bands observed in all spectra, 2.06eV (600nm), 2.22eV (558 nm) and 2.35eV (527 nm), seem independent of the type active center or its concentration (Figure S14). We attribute these bands to isolated silver clusters in the sodalite cages. For example, the emission bands observed at 527 and 558 nm are similar to the ones associated with the trinuclear and tetranuclear silver species 13,45 . These assignments agree with the UV-visible absorbance analysis of the samples, which indicates the presence of common Ag species with characteristic UV absorbance and new species with characteristic visible absorbance for Ag@ZX-Bi samples with relatively high Bi content. This latter seems have very low (or no) radiative emission in the visible range (characteristic behavior of plasmonic particles). Please do not adjust margins Please do not adjust margins Fig. 6. PL spectra of (a) Ag@ZX-Bi-0.5, (b) Ag@ZX-Bi-1.5 and (c) Ag@ZX-Bi-3.0 samples. The excitation laser wavelength is either 266 nm (black&grey) or at 488 nm (blue). PL bands were fitted using the Gaussian function (doted red lines). Long pass filters were used with cut-on wavelength at 700 nm for grey and blue curves.

This assignment was further supported by the TEM results. Figure 7 shows high-resolution TEM images along the most informative [011] FAU zone axis. The morphology of ZX nanoparticles remains intact with clearly expressed faceting with <111> planes. Ag clusters appear as dark spots in the TEM images (Fig. 7a-b) and as bright white dots (Z=47) in the HAADF-STEM images (Fig. 7c-e), where the contrast is proportional to atomic number (~Z 2 ) and thickness.

In the latter, the dark spots correspond to the supper cages and/or surrounding ZX framework, which is not clearly resolved due to the low sum HAADF-STEM contrast of silica-alumina zeolite framework (ZSi=14; ZAl=13, ZO=8) and large difference with the atomic number of Ag. Despite the high sensitivity of the Ag@ZX-Bi particles to the electron beam and tendency to agglomerate in larger silver NPs, it was possible to acquire high-resolution image along the [011] and [112] zone axis of Ag@ZX-Bi crystallite in both mode -HRTEM (Fig. 7b) and HAADF-STEM (Fig. 7e) -using low doses and weak e-beam conditions. Thus, the lattice planes of Ag clusters were resolved. The corresponding Fourier Transform (FT) patterns (insert Fig. 7b) confirm the presence and the 3D arrangement of Ag [111] particles within the pores of FAU-type structure. The Ag sub-nanoparticles appear in projection to have close to the ball shape with a typical diameter of about 1.0 nm, interconnected by Ag "nanobridges" and forming checker board pattern reflecting the [011] FAU structure symmetry. The size of Ag is compatible with the the 6-member ring prism of FAU structure. The Ag in such position makes a nanobridge between the silver clusters situated in the sodalite cage, as shown in Fig. 7e (insert). The [011] FAU skillet structural model is placed over the experimental [011] HAADF-STEM image (Fig. 7e). The HAADF-STEM image of Ag@ZX-Bi projected on [112] zone axis reveals the presence of silver nanoparticle situated in the sodalite cages connected through the 6MR prism thus creating a 3D nanostructure. The needles-like silver particles (Figure 7e) are more pronounced and longer than those observed in our previous work on Ag@-ZX-V. This could explain the characteristic visible absorbance of the silver quasi-nanoparticles. It should be noted that

Please do not adjust margins Please do not adjust margins due to the low concentration of the Bi, we assume that the white spot corresponds mainly to the silver particles which are clearly much more abundant than Bi (Fig. 3). Taking into account the redox process, the oxidation of Bi 2+ , Bi 3+ to Bi 5+ generate three to two reduced silver, respectively. This reduced silver atoms are surrounded with cationic silver atoms to form the clusters making silver much more abundant than bismuth. The oxidation of the Bi allows recompensate the positive charge of reduced silver. The IR spectra of Ag@ZX-Bi (Fig. S17) (1600-1300 cm -1 ) reveal the presence of residual nitrate indicating that the excess of the positive charge of bismuth (in respect to the zeolite framework) is recompensated by the negative charge of nitrate as counter ion.

MNPBEM modeling experiments (please see the SI for more details) were performed to understand better the optical behavior of nanostructured silver species in the Faujasite (Ag-FAU-Rep) pore space (Figure 8A). We have modelled the far-field response in terms of extinction absorption and scattering cross-section spectra and near field responses in terms of field enhancement map (Figure 8B). The extinction, absorption, and scattering cross-sections spectra of isolated and interconnected Ag-FAU nanoparticles with two perpendicular excitation polarizations (E//xO; E//yO) were calculated. The extinction cross-section sums of the absorption were found clearly greater than the scattering one. A plasmon narrow extinction band centered at 384 nm, independent of excitation polarization, is observed in the samples with isolated silver nanostructures in the sodalite cages (i.e., Ag-free 6MR prism). This band is similar to that observed for a single Ag cluster (Figure S15) and experimentally observed in the case of Ag@ZX-V and Ag-ZX-Bi-0.5 (ZX-Bi with low Bi content). However, in the case of Ag-FAU nanostructures with interconnected silver clusters (Ag in the sodalite cage and 6MR prism), the extinction spectra show new extinction bands distributed in the visible region (370-700nm).

The simplification of the modelled system is probably the reason for the difference in the band's positions between the simulated and the experimental spectra (more details in the experimental section).

Nevertheless, it reveals important information concerning the nanostructure shape effect on the global optical behavior. More significantly, the results confirm the hypothesis that the interconnection of silver clusters through the zeolite channels is responsible for absorbing multiple bands in the visible range. The simulation shows that with the exception of 384 nm and 370 nm bands the intensity depends on the polarization angle (Figure S16). This evidence that those resonances arise on structures with less symmetry than the individual particle in the sodalite cage or the 6MR prism and that they might be due to delocalized mode over several of those elements. The origin of different bands obtained on single silver faujasite nanostructures were investigated by exploring the plasmon response of elemental building blocks. Large and small individual nanoparticles were considered: rods formed by two large and a small nanoparticle (vertical and oriented at 19.47°/xOy plan); faujasite's basis ring formed by six rods; conical top structure formed by three rods oriented at q=120° (Figure S16). The plasmon response in term of extinction cross-section spectrum of those building blocks was calculated and compared to one of silver faujasite nanostructures. This comparison allowed us to confirm that the resonance centered at 383 nm is due to the individual/isolated response of large and small silver nanoparticles/clusters reported above or to rods parallel to wavevector (perpendicular to xOy plane). The resonances between 386 and 500 nm can be attributed to resonances occurring in rods oriented out of the perpendicular axis to xOy plane. Resonances occurring above 500 nm are attributed to resonance occurring in the whole ring and conical top structures (Fig. 8B). Larger structures created by adding several faujasite unit cells resulting in the linear chain were investigated, leading to comparable cross-section spectra (Figure 8) with resonance peaks ranging from 383 to 700 nm. The calculated near-field enhancement maps for isolated and interconnected silver nanostructures with resonance energy at 370, 383 and 549 nm are reported in Fig. 8B. Localized resonance around each constituting nanoparticle was found on isolated silver Faujasite nanostructures, while delocalized resonance over several constituting nanoparticles was found on interconnected silver Faujasite nanostructures. This result confirms the existence of delocalized plasmon modes on several Ag-FAU building blocks leading to numerous extinction bands whose positions are redshifted up to 700 nm. The near field enhancement map at 549 nm evidences that the red-shifted plasmon resonance occurs in a singlecell nanostructure. Considering that the plasmon decay follows three different channel processes with Drude friction-like dissipation, inter-band transitions, and generation of energetic hot electrons 46 , we may have a different kind of behavior depending on the spectral position of excitation. For short wavelengths (high energy), we may have energy transfer to resonant energy levels of luminescent species, leading to enhanced photoluminescence (as observed above under 266 nm of excitation). For larger wavelengths (lower energy), only the dissipation process may occur with Drude friction or electron-phonon interactions leading to heat release in the local nanostructure environment.

ARTICLE

Please do not adjust margins Please do not adjust margins The operando IR analysis have been used to elucidate the plasmonic behavior of the Ag@ZX-Bi-3 sample. Under visible light irradiation, it shows an immediate response with fast water desorption for almost 80 % in the first three minutes of irradiation (Fig. 9A). This loss is usually taking place at around 200°C (Fig. 9B), and in agreement with the TG result for ZX-Bi (Fig. 3B)). It is important to mention that this effect is more than two times higher than that observed with Ag@ZX-V (Figure S17) and that there are no responses for Ag@ZX-Bi and Ag + @ZX-Bi under monochromatic UV (366 nm) or visible (>390 nm) light irradiations, respectively. Therefore, this attitude is directly related to the presence of the Ag-quasi-NPs (Ag-QNPS). Another noteworthy behavior is observed on the activated samples (after 30 min of visible irradiation at 28°C). The IR spectrum of this sample shows a reversible and instantaneous shift toward the lower wavenumbers (for 3.4 cm -1 ) in the vibration region of v(OH) under visible irradiation in respect to that collected in dark (Figure 9C-D). Such an effect is usually a sign of a temperature increase for few hundreds of degrees 47 .

The relationship between the v(OH) band and the temperature was measured on Ag@ZX-Bi series of samples (Figure S18). The results show that the shift of 3.4 cm -1 corresponds to a heating temperature of 190°C. Under permanent visible irradiation and with the increase of the sample temperature, the variation of n(OH) (Figure S18-A) is almost constant (Dn(OH~3.5 cm -1 ) until 150°C. However, the shift is similar (in dark or under visible light) for a temperature equal or higher than 200°C. The HRTEM analysis shows that the activation of Ag@ZX-Bi at T≥200°C promotes the formation of external AgNPs (Figure S19), probably due to the migration and aggregation of the silver on the zeolite surface. This explains the major drop of the plasmonic behavior of the S-QNPs. It should be noted that the zeolite surface is rich in OH groups, but without direct contact with the Ag-QNPs. The instantaneous effect on these sites' vibration frequencies allows thinking that the energy transfer from the Ag-QNPs to the OH is probably radiative and agrees with the water desorption observed during the irradiation of Ag@ZX-Bi under visible light.

Conclusion

Bismuth (Bi 2+ /Bi 3+ ) doped zeolite nanoparticles (ZX-Bi), successfully prepared by one-pot hydrothermal synthesis and stabilized as colloidal suspensions, were used for the preparation of bridged silver clusters. The UV-irradiation of the ZX-Bi suspensions in the presence of silver nitrate promotes the formation of reduced silver particles (Ag@ZX-Bi) in the zeolite micropore space. The set of experimental results revealed that the optical behavior of the confined silver particles depends on the Bi concentration initially introduced in the zeolite. The composite sample's UV-visible absorbance shows narrow bands in the visible range (400-800 nm), characteristic of metal nanoparticles with well-defined sizes. The TEM analysis, the photoluminescence results, and the boundary element method (BEM) approach showed that the Ag's unique optical behavior is a consequence of the formation of interconnected silver clusters (silver quasi-nanoparticles) through the zeolite channels. The IR operando analysis of the samples in dark and under visible light confirmed the surprising plasmonic behavior of the Ag@ZX-Bi samples and registered local heating equivalent to 190°C under visible irradiation, a characteristic behavior of plasmonic nanoparticles. Therefore, to our knowledge, the gap between clusters and plasmonic nanoparticles with well-controlled and reproducible size of Ag-QNPs is bridged for the first time. The

Fig. 1 .

 1 Fig. 1. (A) X-ray diffraction patterns of ZX-Bi samples prepared with different bismuth contents: (a) 0 wt.% (ZX or ZX-Bi-0), (b) 0.5 wt.% (ZX-Bi-0.5); 1.5 wt.% (ZX-Bi-1.5), (c) 3.0 wt.% (ZX -Bi-3), and (d) 5.0 wt.% (ZX-Bi-5). Samples crystallized at 50 °C for 8 h after aging for 14h at RT. The weight percentage of bismuth corresponds to the initial quantities added and calculated by considering the quantity of Al and Si. (B) XRD patterns of (a) pure ZX and (b) ZX-Bi-3 crystallized for 24 h and 8 h, respectively. Crystallization temperature: 50 °C; after aging for 24 h and 14 h at RT, respectively.

Fig. 2 .

 2 Fig.2. (A) Nitrogen adsorption and desorption isotherms, (B) mesopore size distribution, and (C) TG (solid lines) and DTG (doted lines) curves of (a) ZX and (b) ZX-Bi-3 samples.

Fig. 3 .

 3 Fig. 3. HAADF-STEM analysis of the ZX-Bi sample: Low magnification HAADF-STEM images of different ZX-Bi nanoparticles viewed along two most informative directions: (a) [112]FAU and (b) [011]FAU zone axis; (c) and (d) correspond to the enlargement of the white boxes selected zones in figure (b)

Fig. 4 .

 4 Fig. 4. Gaussian deconvolutions of the XPS spectra of ZX-Bi-3 (a) and Ag@ZX-Bi-3 (b) samples. The red lines correspond to the intensity sum of the different peak contributions.

Fig. 5 .

 5 Fig.5. Time evolution of UV-vis absorption spectra of ZX-Bi suspensions after (a) 0,5, (b) 2, (c) 5 (d) 7, (e) 10 et (f) 15 min of UV irradiation in presence of 10 - 2 M of AgNO3. (Ref) correspond to the sample before irradiation. Conditions: lamp Hg-Xe; polychromatic = 205 mW/cm 2 ; filter cut infrared; total volume = 5 ml; [ZX-Bi] = 2.4 g/l. Inset: images of the corresponding suspensions.

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Fig. 7 .

 7 Fig. 7. TEM analysis of the Ag@ZX-Bi sample: (a) bright-field low magnification TEM images of Ag@ZX-Bi; (b) HRTEM image of single Ag@ZX-Bi crystallites revealing resolved Ag lattice planes following the FAU structure. The corresponding FT pattern confirmed the structure and the 3D arrangement of the Ag clusters aligned to the FAU framework (see enlargements of the central FT spots). (c) Low magnification HAADF-STEM image of Ag@ZX-Bi. Bright contrast Ag nanostructures are clearly visible. (d) High-resolution HAADF-STEM image of Ag@ZX-Bi. (e) High resolution HAADF-STEM images of Ag@ZX-Bi structures close to [112] (left panel) and to [011] (right panel) FAU zone axis. Ag nanostructures within ZX-Bi structure exhibiting bright contrast are marked with white arrows. The structural model of FAU is overlaid on the experimental image.

  Photoluminiscence(a.u)

Fig. 8 .

 8 Fig. 8. (A) Extinction cross-section of silver nanostructures with isolated and interconnected nanoparticles in a single unit cell, two interconnected unit cells, five interconnected unit cells, and ten interconnected unit cells with polarization along xO and yO axis. (B) Near-field enhancement maps for isolated silver (a) and interconnected (b,d) silver-replica nanostructures resonance energy of one (b) and five (c) unit cells at 370 nm (b,c), 383 nm (a), and 549 nm (d).

Fig. 9 .

 9 Fig. 9. Evolution of the IR band area (centered at 1645 cm -1 ) of the adsorbed water on the as-prepared Ag@ZX-Bi-3 samples versus (A) the visible light irradiation time at 25°C, and (B) the temperature in dark. Inset: 3D mapping of the corresponding original spectra. The arrow corresponds to the turn on the light (t=0s) and the starting point heating (rate = 1.75°C/min), respectively. Experiment conditions: Ar flow (25 cm 3 /min); mcat=20 mg (pellet with 2 cm 2 of diameter and 65 µm of thickness; Light source used in (A) Xe lamp with cut UV filter (l>390 nm) and irradiance of ≈ 150 mW/cm 2 . (C) IR spectra of Ag@ZX-Bi sample in the vibration region of the SiOH in dark (black; lamp-OFF) and under visible irradiation (red; Lamp-ON). Sample activated at 28°C under visible light for 30 min. (D) 2D mapping of the band intensity variation in the 3800-3650 cm -1 region at different cycles of lamp-OFF/lamp-ON (red and blue-green correspond to the higher and lower intensity, respectively). (light relative intensities (0% or 100%): low position: shutter close; high position: shutter open). The vertical scale axis corresponds to the time of the experiments. Note: (C) and (D) were measured on the same sample pellet and under the same conditions used in (A).

Acknowledgements

F. Douma and M. El-Roz acknowledge the PHC Tassili program (17MDU983) and European Regional Development Fund (ERDF), Normandy region (RAPHYD project), for the financial support. Authors acknowledges the colleagues' Mama Lafjah, Fatiha Djafri and Jaun Pablo Bolletta for the scientific discussions and supports.

Notes and references

Author Contributions

The manuscript was prepared written through the contributions of all authors. All authors have approved the final version of the manuscript.

Conflicts of interest

The authors declare that no competing interest.