Richard C Gerkin

Kathrin Ohla

Maria G Veldhuizen

Paule V Joseph

Christine E Kelly

Alyssa J Bakke

Kimberley E Steele

Michael C Farruggia

Robert Pellegrino

Marta Y Pepino

Cédric Bouyset

Graciela M Soler

Veronica Pereda-Loth

Michele Dibattista

Keiland W Cooper

Ilja Croijmans

Antonella Di Pizio

M Hakan Ozdener

Alexander W Fjaeldstad

Cailu Lin

Mari A Sandell

Preet Bab No Singh

V Evelyn Brindha

Shannon B Olsson

Luis R Saraiva

Gaurav Ahuja

Mohammed K Alwashahi

Surabhi Bhutani

Anna D'errico

Marco A Fornazieri

Jérôme Golebiowski

Liang-Dar Hwang

Lina Öztürk

Eugeni Roura

Sara Spinelli

Katherine L Whitcroft

Farhoud Faraji

Florian S Ph

Fischmeister

Thomas Heinbockel

Julien W Hsieh

Caroline Huart

Iordanis Konstantinidis

Anna Menini

Gabriella Morini

Jonas K Olofsson

Carl M Philpott

Denis Pierron

Vonnie D C Shields

Vera V Voznessenskaya

Javier Albayay

Aytug Altundag

Moustafa Bensafi

María Adelaida Bock

Orietta Calcinoni

William Fredborg

Christophe Laudamiel

Juyun Lim

Johan N Lundström

Alberto Macchi

Pablo Meyer

Shima T Moein

Enrique Santamaría

Debarka Sengupta

Hüseyin Yanik

Gccr Group

Thomas Hummel

John E Hayes

Danielle R Reed

Masha Y Niv

Steven D Munger

PhD Valentina Parma
email: valentina.parma@temple.edu

Sanne Boesveldt

Jasper H B De Groot

Caterina Dinnella

Jessica Freiherr

Tatiana Laktionova

Sajidxa Marino

Erminio Monteleone

Alexia Nunez-Parra

Olagunju Abdulrahman

Marina Ritchie

Paloma Rohlfs Domínguez

Thierry Thomas-Danguin

Julie Walsh-Messinger

Rashid Al Abri

Rafieh Alizadeh

Emmanuelle Bignon

Elena Cantone

Maria Paola Cecchini

Jingguo Chen

Maria Dolors Guàrdia

Kara C Hoover

Noam Karni

Marta Navarro

Alissa A Nolden

Patricia Portillo Mazal

Nicholas R Rowan

Atiye Sarabi-Jamab

Nicholas S Archer

Ben Chen

Elizabeth A Di Valerio

Emma L Feeney

Johannes Frasnelli

Mackenzie E Hannum

Claire Hopkins

Hadar Klein

Coralie Mignot

Carla Mucignat

Yuping Ning

Elif E Ozturk

Mei Peng

Ozlem Saatci

Elizabeth A Sell

Carol H Yan

Raul Alfaro

Cinzia Cecchetto

Gérard Coureaud

Riley D Herriman

Jeb M Justice

Pavan Kumar Kaushik

Sachiko Koyama

Jonathan B Overdevest

Nicola Pirastu

Vicente A Ramirez

S Craig Roberts

Barry C Smith

Mirjam Van Den Brink

Hongyuan Cao

Hong Wang

Recent smell loss is the best predictor of COVID-19: a preregistered, cross-sectional study

Background: COVID-19 has heterogeneous manifestations, though one of the most common symptoms is a sudden loss of smell (anosmia or hyposmia). We investigated whether olfactory loss is a reliable predictor of COVID-19.

Methods: This preregistered, cross-sectional study used a crowdsourced questionnaire in 23 languages to assess symptoms in individuals self-reporting recent respiratory illness. We quantified changes in chemosensory abilities during the course of the respiratory illness using 0-100 visual analog scales (VAS) for participants reporting a positive (C19+; n=4148) or negative (C19-; n=546) COVID-19 laboratory test outcome. Logistic regression models identified singular and cumulative predictors of COVID-19 status and post-COVID-19 olfactory recovery.

Results: Both C19+ and C19-groups exhibited smell loss, but it was significantly larger in C19+ participants (mean±SD, C19+: -82.5±27.2 points; C19-: -59.8±37.7). Smell loss during illness was the best predictor of COVID-19 in both single and cumulative feature models (ROC AUC=0.72), with additional features providing no significant model improvement. VAS ratings of smell loss were more predictive than binary chemosensory yes/no-questions or other cardinal symptoms, such as fever or cough. Olfactory recovery within 40 days was reported for ~50% of participants and was best predicted by time since illness onset.

Conclusions :

As smell loss is the best predictor of COVID-19, we developed the ODoR-19 tool, a 0-10 scale to screen for recent olfactory loss. Numeric ratings ≤2 indicate high odds of symptomatic COVID-19 (10<OR<4), especially when viral lab tests are impractical or unavailable.

Introduction (334)

The novel coronavirus SARS-CoV-2 is responsible for the worldwide global COVID-19 pandemic that has left a staggering level of morbidity, mortality, and societal and economic disruption in its wake. 1 Several early publications, [START_REF] Paderno | Smell and taste alterations in Covid-19: a cross-sectional analysis of different cohorts[END_REF][START_REF] Giacomelli | Self-reported olfactory and taste disorders in SARS-CoV-2 patients: a cross-sectional study [Internet[END_REF][START_REF] Yan | Association of chemosensory dysfunction and Covid-19 in patients presenting with influenza-like symptoms[END_REF][START_REF] Lechien | Olfactory and gustatory dysfunctions as a clinical presentation of mild-to-moderate forms of the coronavirus disease (COVID-19): a multicenter European study[END_REF][START_REF] Moein | Smell dysfunction: a biomarker for COVID-19[END_REF][START_REF] Hornuss | Anosmia in COVID-19 patients[END_REF] including our own work, [START_REF] Parma | More than smell. COVID-19 is associated with severe impairment of smell, taste, and chemesthesis [Internet]. Infectious Diseases (except HIV/AIDS)[END_REF] indicate that sudden loss of smell and taste are cardinal, early and potentially specific symptoms of COVID-19, [START_REF] Menni | Real-time tracking of self-reported symptoms to predict potential COVID-19[END_REF] including individuals who are otherwise asymptomatic. [START_REF] Menni | Quantifying additional COVID-19 symptoms will save lives[END_REF][START_REF] Kim | Clinical characteristics of asymptomatic and symptomatic patients with mild COVID-19[END_REF][START_REF] Walsh-Messinger | Standardized Testing Demonstrates Altered Odor Detection Sensitivity and Hedonics in Asymptomatic College Students as SARS-CoV-2 Emerged Locally[END_REF][START_REF] Hopkins | Presentation of new onset anosmia during the COVID-19 pandemic[END_REF] While fever and cough are common symptoms of diverse viral infections, the potential specificity of early smell and taste loss to COVID-19 could make it valuable in screening and diagnosis.

Anosmia and other chemosensory disorders have serious health and quality of life consequences for affected patients. However, there is a general lack of awareness of chemosensory disorders by clinicians and the public, including the association of disorders such as anosmia with upper respiratory infections. [START_REF] Soler | A primer on viral-associated olfactory loss in the era of COVID-19[END_REF] In the case of COVID-19 this lack of awareness led to an underappreciation of the role of chemosensory symptoms in the diagnosis of COVID-19. In addition, the impact of smell loss as a clinical consequence of COVID-19 has not been adequately addressed. Thus, there is an urgent need to better define the chemosensory dysfunctions associated with COVID-19 and to determine their relevance as predictors of this disease. It is critical to develop rapid clinical tools to efficiently and effectively integrate chemosensory assessments into COVID-19 screening and treatment protocols. Information on the duration and reversibility of post-COVID-19 chemosensory impairment is also lacking.

We used a combination of quantitative, binary, and categorical self-report measures to determine the chemosensory phenotype, along with other symptoms and characteristics, of COVID-19-positive (C19+) and COVID-19-negative (C19-) individuals who had reported recent symptoms of respiratory illness. We used those results in logistic regression models to identify predictors of COVID-19 and recovery from smell loss. Finally, we propose the O lfactory D eterminati o n R ating scale for COVID-19 ( ODoR-19), a quick, simple to use, telemedicine-friendly tool to improve the utility of current COVID-19 screening protocols, particularly when access to rapid testing for SARS-CoV-2 is limited.

Methods

Study design (175)

This preregistered, [START_REF] Parma | Is olfactory loss a sensitive symptomatic predictor of COVID-19? A preregistered, crowdsourced study[END_REF] cross-sectional online study was approved by the Office of Research Protections of The Pennsylvania State University (STUDY00014904); it is in accordance with the revised Declaration of Helsinki, and compliant with privacy laws in the U.S.A. and the European Union. The data reported here were collected between April 7 and July 2, 2020 from the Global Consortium for Chemosensory Research (GCCR) core questionnaire, [START_REF] Parma | More than smell -COVID-19 is associated with severe impairment of smell, taste, and chemesthesis[END_REF] an online crowdsourced survey deployed in 32 languages to measure self-reported smell, taste, and chemesthesis ability and nasal blockage -as well as dozens of other symptoms -in adults with current or recent respiratory illness ( Appendix 1 and https://gcchemosensr.org ). The GCCR questionnaire contains questions about symptoms using both binary responses (e.g. Appendix 1 , Question 9) and visual analog scales (e.g., Appendix 1 , Question 13). The data reported here include responses in Arabic, Bengali, Chinese (Simplified and Traditional), Danish, Dutch, English, Farsi, Finnish, French, German, Greek, Hebrew, Hindi, Italian, Japanese, Korean, Norwegian, Portuguese, Russian, Spanish, Swedish, Turkish, and Urdu.

Participants (247)

A convenience sample of 52,334 volunteers accessed the GCCR questionnaire and 25,620 met eligibility criteria (≥19 years old, respiratory illness or suspicion thereof within the past two weeks). After applying preregistered exclusion criteria, 15,747 participants were included in reported analyses ( Figure 1 ). . N = number of participants; yo = age in years; F = female; M = male. Gender percentages do not include <1% of participants who answered "other" or "preferred not to say".

Based on the reported outcome of a COVID-19 lab test, participants were labeled as either C19+ (positive result) or C19-(negative result). The specific collider bias characterizing this sample (greater fraction of C19+ than C19-participants and greater prevalence of chemosensory disorders across both groups) underestimates the positive correlation between smell loss and COVID-19 ( Figure S1 ) . Thus, it represents a conservative scenario to test the hypothesis that smell loss reliably predicts COVID-19 status. We also benchmarked the GCCR dataset to the representative samples collected with the Imperial College London YouGov Covid 19 Behavior Tracker by the international online survey research firm YouGov (henceforth, YouGov; countries shared across datasets: Brazil, Canada, Denmark, Finland, France, Germany, Italy, Mexico, Netherlands, Norway, Spain, Sweden, UK, USA; YouGov: N=8,674, GCCR: N=3,962; data publicly available at https://github.com/YouGov-Data/covid-19-tracker) . Benchmarking shows the GCCR sample underestimates the positive correlation between smell loss and COVID-19-positive status ( Figure S1E, Table S1 ) . The country-wise fraction of C19+ participants is correlated (r~0.5) when responses from the same calendar week are aligned ( Figure S2) . These findings are in line with other comparisons between crowdsourced versus representative health data, [START_REF] Kraemer | Crowdsourced data collection for public health: A comparison with nationally representative, population tobacco use data[END_REF] confirming that trends identified in crowdsourced data reasonably approximate population data. Nonetheless, because our cohort is not demographically balanced, it should not be used to estimate prevalence.

Statistical analyses (190)

Statistical analyses were performed in Python 3.7.6 using the pandas, [START_REF] Reback | pandas-dev/pandas: Pandas 1.0.5[END_REF] scikit-learn, 19 and statsmodels 20 packages. The data and annotated code will be available on GitHub ( http://github.com/GCCR/GCCR002 ) upon publication. Missing values in prediction models of COVID-19 status and smell recovery were imputed as follows: binary features = 0.5, numeric features = median, categorical variables = "Missing". Responses incompatible with model generalization (e.g., open ended questions) were excluded. A one-hot encoding was applied to all categorical variables to produce binary indicators of category membership. L1-regularized logistic regression (penalty α=1) consistently produced sparse models with comparable cross-validation accuracy and were therefore the prediction test of choice. Model quality was measured using receiver operating characteristic (ROC) area under the curve (AUC). Cross-validation was performed in 100 random splits of 80% training set and 20% test set, and ROC curves are averages over each test set. Note that for single feature models, AUC is independent of most modeling details, including all rank-invariant decisions. To correctly compute p-values for model coefficients, the normalized data were standardized (mean 0, variance 1) and then coefficients back-transformed to normalized form after fitting.

Results

Chemosensory loss associates with COVID-19 (190)

A preregistered replication of our prior study [START_REF] Parma | More than smell -COVID-19 is associated with severe impairment of smell, taste, and chemesthesis[END_REF] confirmed that reported smell, taste, and chemesthesis abilities drop significantly in both lab tested C19+ participants and those diagnosed by clinical assessment ( Figure S3, Table S2 ).

Next, we compared lab tested C19+ and C19-participants. C19+ participants reported a greater loss of smell (C19+: -82.5±27.2 points; C19-: -59.8±37.7 points, p=2.2e-46, extreme evidence of difference: BF 10 =8.97e+61, Figure 2A,B, Table S3 ), taste (C19+: -71.6±31.8 points; C19-: -55.2±37.5 points, p=6.7e-26, extreme evidence of difference: BF 10 =6.67e+24, Figure 2C,D, Table S3 ) and chemesthesis ability (C19+: -36.8±37.1 points; C19-: -28.7±37.1 points, p=1.6e-07, extreme evidence of difference: BF 10 =3182, Figure 2E,F, Table S3 ). However, both groups reported a similar degree of nasal obstruction ( Figure 2G,H, Table S3 ). Self-reported changes in smell, taste, and chemesthesis were highly correlated in both groups (C19+: 0.71<r<0.83; C19-: 0.76<r<0.87) and orthogonal to nasal obstruction changes (C19+: r=-0.20; C19-: r=-0.13).

Prediction of COVID-19 status from survey responses (436)

Examining binary (yes/no) reported symptoms, we found COVID-19 has a stronger association with chemosensory symptoms than with non-chemosensory symptoms, including fever, cough, and shortness of breath, which are the cardinal symptoms currently highlighted by the US Centers for Disease Control and Prevention (CDC; Figure 3A ). Using AUC to assess prediction quality ( Figure 3B ), we found the single survey question most predictive of COVID-19 status was self-reported smell ability during illness, reported on a continuous scale (AUC=0.71). Changes in smell as a result of illness, calculated from the difference between smell ability during and before illness, was similarly predictive (AUC=0.69). Changes in taste ability (assessed via rating) were the next most predictive features (AUC=0.64-0.65) ( Figure 3B ). Models fit to the same data but with shuffled COVID-19 status consistently produced AUC ~0.5 for all features. The most predictive non-chemosensory symptom sore throat, which was negatively associated with COVID-19, was substantially less predictive (AUC=0.58) than the top chemosensory symptoms. Nasal obstruction was not predictive (AUC=0.52). Notably, responses given on a continuous scale were more predictive (AUC=0.71) than binary yes/no responses to parallel questions (e.g., Appendix 1 , Question 13 versus 14) (AUC=0.60-0.62), likely because a continuous scale contains a greater amount of diagnostic information ( Figure S4 ).

Next, we examined which simple multi-feature model would best predict COVID-19 status. As some questions have highly correlated responses, the question most complementary to "Smell during illness" is unlikely to be one that carries redundant information. Adding "Days since onset of respiratory symptoms" (DOS) to "Smell during illness" (Smell Only) produced the largest incremental gain in predictive performance (AUC=0.72, +0.01 versus the Smell Only model) ( Figure 3C ).

We then directly compared the Smell Only+DOS model to other candidate models. The Smell Only+DOS model (AUC=0.72, Figure 3D ) yielded an equal or higher AUC than the model including the three cardinal CDC symptoms (AUC=0.55) or the full model using 70 features (AUC=0.72). The Smell Only+DOS model exhibits the same AUC as the Smell Only model and strikes a good balance between model parsimony and predictive accuracy for C19+. However, the Smell Only model also offers reasonable sensitivity of 0.85 (at specificity=0.51, cutoff=13 on the 100-point VAS) and/or specificity of 0.75 (at sensitivity=0.51, cutoff=1) as desired. By sharp contrast, fever has a sensitivity of only 0.54 with specificity of 0.49 and dry cough has sensitivity of 0.52 and specificity of 0.46.

Recovery from smell loss (312)

In the subset of C19+ participants with full or partial resolution of respiratory symptoms, recovery from smell loss was modest (approximately half the initial average loss). Overall, self-reported post-illness olfactory ability was still lower for C19+ (39.9±34.7) than C19-(52.2±35.2, p=2.8e-11, Figure S5A ). However, the mean recovery of smell (after illness relative to during illness) was greater for C19+ (30.5±35.7) than C19-(24.6±31.9, p=0.0002, Figure S5B ). A similar (but smaller) effect of COVID-19 status on recovery was observed for taste ( Figure S5C , D ), while little to no association with COVID-19 was observed for recovery of chemesthesis ( Figure S5E,F ) or nasal obstruction ( Figure S5G,H ). When illness-induced change in olfactory function (during versus before illness) and recovery of olfactory function (after versus during illness) were evaluated, we identified three respondent clusters: those who self-reported no loss of smell (Intact Smell), those who reported a loss of smell with recovery (Recovered Smell), and those who reported smell loss without recovery by up to 40 days (Persistent Smell Loss, Figure 4, Table S4). Intact smell was reported by only 8.5% of the participants in the C19+ group but by 27.5% in the C19-group. A greater proportion of C19+ participants were included in both the Recovered Smell group (C19+: 40.9%, C19-: 33.3%) and the Persistent Smell Loss group (C19+: 50.7%, C19-: 39.2%; Figure 4A,B ). C19+ participants in both the Recovered Smell and Persistent Smell Loss clusters reported similar extent of olfactory loss, irrespective of the number of days since respiratory symptom onset. By contrast, the rate of self-reported smell recovery increased over time, with a plateau at 30 days ( Figure 4C ). Finally, DOS was the best predictor (AUC=0.62) between the Persistent Smell Loss and the Recovered Smell groups ( Figure S6A, Table S4 ).

Simple screening for COVID-19: the O lfactory D eterminati o n R ating scale in COVID-19 (ODoR-19) (103)

Our results indicate that a continuous rating of current olfactory function is the single best predictor of COVID-19 and improves the discrimination between C19+ and C19-over a binary (y/n) question on smell loss. We propose here a simple numeric variation of the rating scale (0-10), the ODoR-19, that can be administered in person or via telemedicine to improve early COVID-19 screening for individuals without preexisting smell and/or taste disorders. Responses to the ODoR-19 scale ≤2 indicate high odds of COVID-19 positivity (4<OR<10, Figure 5D ). An ODoR-19 response of 3 indicates a borderline risk (OR=1.2) of COVID-19.

Discussion (706)

Self-reported smell loss was present in both C19+ and C19-participants, but more common in the C19+ group. The use of a VAS to assess olfactory loss better predicted COVID-19 status than using a binary (yes/no) question. We found that the best predictor of COVID-19-associated smell recovery, within the time frame captured by the survey (~40 days), was days since onset of COVID-19.

The SARS-CoV-2 pandemic requires healthcare providers and contact tracers to quickly and reliably assess an individual's COVID-19 risk and is often performed remotely. Thus, reliable screening tools are critical to assess a person's likelihood of having COVID-19 and to justify self-quarantine and/or testing recommendations. Indeed, some reports suggest that COVID-19-associated smell loss might be an indicator of disease severity. [START_REF] Paderno | Smell and taste alterations in Covid-19: a cross-sectional analysis of different cohorts[END_REF][START_REF] Yan | Self-reported olfactory loss associates with outpatient clinical course in Covid-19[END_REF] Current screening measures and symptom criteria (e.g., travel history, fever, dry cough) are less specific than severe olfactory loss. Indeed, the value of our ODoR-19 tool lies in the high specificity of values ≤2 for indicating COVID-19 positivity, therefore representing a valuable addition to the current repertoire of COVID-19 screening tools.

Our online survey and sampling methodology likely selected participants with a heightened interest in smell and taste and/or their disturbances. This could be viewed as a limitation since the C19-group also showed chemosensory loss. However, the finding of a difference between groups in a sample with a higher barrier for discriminating between C19+ and C19-supports the robustness of this tool when used in a typical clinical population.

Our results suggest that chemosensory impairment has strong COVID-19 predictive value when access to viral testing is limited or absent. While it is important to recognize that the self-selection bias of our sample could limit the generalizability of these results, our collider bias ( Figure S3 ) suggests that our findings are likely conservative estimates. As with any self-report measure, veracity of self-reports cannot be guaranteed, however the ability to screen individuals in real-time should outweigh this potential confound [START_REF] Mermelstein | The Science of Real-Time Data Capture: Self-Reports in Health Research[END_REF] . Finally, while objective smell tests are the gold standard for assessing olfactory function, [START_REF] Hummel | Sniffin' Sticks': Olfactory Performance Assessed by the Combined Testing of Odor Identification, Odor Discrimination and Olfactory Threshold[END_REF][START_REF] Doty | University of pennsylvania smell identification test: A rapid quantitative olfactory function test for the clinic[END_REF] they are costly, time consuming to administer, and can require in-person interactions with a potentially infectious patient. [START_REF] Doty | University of pennsylvania smell identification test: A rapid quantitative olfactory function test for the clinic[END_REF][START_REF] Oleszkiewicz | Updated Sniffin' Sticks normative data based on an extended sample of 9139 subjects[END_REF] By contrast, the ODoR-19 is free, quick, and can be administered in person or remotely. We cannot exclude that our C19sample contains COVID-19 false negatives. [START_REF] Kucirka | Variation in False-Negative Rate of Reverse Transcriptase Polymerase Chain Reaction-Based SARS-CoV-2 Tests by Time Since Exposure[END_REF] However, self-reported smell during illness distinguishes between C19+ and C19-, but not between randomly shuffled C19+ cases, suggesting that the difference between C19+ and C19-, even in a sample with over-represented chemosensory dysfunction, is substantial and can be captured via self-report. Those who receive a negative outcome from a COVID-19 viral test, yet report significant idiopathic smell loss, should be considered as high-priority candidates for COVID-19 re-testing.

Approximately half of the participants in the C19+ group recovered their sense of smell within 40 days from the onset of respiratory symptoms. This finding suggests the presence of at least two subgroups of patients: one that recovers quickly (<40 days, 40.9%) and another that may present a more variable time course of recovery (50.7%). Since these data are collected before the full recovery of all symptoms, we cannot offer a complete picture of recovery from olfactory loss in COVID-19-positive individuals, but they align with other early reports. [START_REF] Chiesa-Estomba | Patterns of smell recovery in 751 patients affected by the COVID-19 outbreak[END_REF] The COVID-19 pandemic will greatly increase the number of patients suffering from anosmia and other chemosensory disorders, [START_REF] Rawal | Prevalence and Risk Factors of Self-Reported Smell and Taste Alterations: Results from the 2011-2012 US National Health and Nutrition Examination Survey (NHANES)[END_REF] conditions that significantly affect quality of life, [START_REF] Smeets | Sense of smell disorder and health-related quality of life[END_REF][START_REF] Croy | Olfactory Disorders and Quality of Life--An Updated Review[END_REF] dietary behavior, 31 cardiovascular health [START_REF] Gallo | Associations of olfactory dysfunction with anthropometric and cardiometabolic measures: Findings from the 2013-2014 national health and nutrition examination survey (NHANES)[END_REF] , and mental health. [START_REF] Croy | Olfaction as a marker for depression in humans[END_REF][START_REF] Malaty | Smell and taste disorders in primary care[END_REF] Thus, it is necessary to prepare healthcare providers to address the long-term needs of these patients.

Based on our results, we propose the use of the ODoR-19 tool, a quick, free, and effective smell-based screening method for COVID-19. This 0-10 rating scale accurately predicts COVID-19 in individuals without pre-existing smell and taste disorders (e.g., from head trauma, chronic rhinosinusitis [START_REF] Hummel | Smell and taste disorders[END_REF] ). ODoR-19 combines the utility of a continuous scale with the ease and speed needed for a screening tool. ODoR-19 is safe for remote administration during an illness with high viral spread and can precede and complement viral testing, especially when lab tests are unavailable, impractical or cost prohibitive. This tool will improve screening for patients with limited or no access to medical care around the globe.

Figure 1 .

 1 Figure 1. Flow diagram showing the demographics of participants included and excluded in the present analyses. Participants included inr the prediction of COVID-19 status are highlighted in blue. Participants included in the smell recovery models are highlighted in green. Participants included in the replication of our prior work are highlighted in orange.. N = number of participants; yo = age in years; F = female; M = male. Gender percentages do not include <1% of participants who answered "other" or "preferred not to say".

Figure 2 .

 2 Figure 2 . Chemosensory ability and nasal obstruction in C19+ and C19-participants. Self-reported smell ( A,B ), taste ( C,D ), chemesthesis ( E,F ), and nasal obstruction ( G,H; formulated as "How blocked was your nose?") before and during respiratory illness in C19+ (darker shades) and C19-(lighter shades) participants. Ratings were given on 0-100 visual analog scales. Left panels (A,C,E,G) show mean values. Right panels (B,D,F,H) show distributions of the change scores (during minus before). Thicker sections indicate relatively more subjects (higher density of responses). The thick black horizontal bar indicates the median, the shaded area within each violin indicates the interquartile range. Each dot represents the rating of a single participant.

Figure 3 .

 3 Figure 3 . Smell loss is the strongest predictor of COVID-19 status. ( A ) A normalized measure of association (Cramer's V) between binary or categorical responses on COVID-19 status. V=0 reflects no association between the response and COVID-19 status; V=1 reflects a perfect association; V>0.1 is considered a meaningful association. Features in red are positively associated with C19+ (odds ratio > 1); features in blue are negatively associated with C19+ (odds ratio < 1). ( B ) Logistic regression is used to predict COVID-19 status from individual features. Top-10 single features are ranked by performance (cross-validated a rea u nder the ROC c urve, AUC). Chemosensory-related features (bold) show greater predictive accuracy than non-chemosensory features (non-bold). Responses provided on the numeric scale (italic) were more informative than binary responses (non-italic). Red arrows indicate differences in prediction quality (in AUC) between features. ( C ) Adding features to "Smell During Illness" results in little improvement to the model; only D ays Since O nset of Respiratory S ymptoms (DOS) yields meaningful improvement. ( D ) ROC curves for several models. A model using "Smell during illness" (Smell Only, abbreviated "Smell" in figure) is compared against models containing this feature along with DOS, as well as models including the three cardinal CDC features (fever, dry cough, difficulty breathing). "Full" indicates a regularized model fit using 70 dozen survey features, which achieves prediction accuracy similar to the parsimonious model "Smell Only+DOS".

Figure 4 :

 4 Figure 4 : Smell loss, recovery, and time course. ( A, B) Joint distribution of smell loss (during minus before illness ratings) and smell recovery (after minus during illness ratings) for C19+ (A) and C19-(B) participants. Darker color indicates a higher probability density; the color map is shared between (A) and (B); dashed lines are placed at a third of the way across the rating scale to aid visualization of the clusters. Severe smell loss that is either persistent (lower left) or recovered (upper left) was more common in C19+ than C19-. n indicates the number of participants in each panel. % indicates the percentage of participants in each quadrant. ( C ) In C19+ participants who lost their sense of smell (Recovered Smell + Persistent Smell Loss), the degree of smell recovery (right y axis) increased over ~30 days since onset of respiratory symptoms before plateauing; the degree of smell change (left y axis) did not change in that window of observation. Solid lines indicate the mean of the measure, the shaded region indicates the 95% confidence interval.

Figure 5 .

 5 Figure 5. The odds of a COVID-19 diagnosis as a function of olfactory loss. ( A ) The solid line indicates the probability of a COVID-19 diagnosis as a function of "Smell during illness" ratings. The shaded region indicates the 95% confidence interval. ( B ) The solid line expresses the probability of a COVID-19+ diagnosis as a function of "Smell during illness" in odds (p/(1-p)); it is shown on a logarithmic scale. The shaded region indicates the 95% confidence interval. ( C ) Stylized depiction of change in the odds of a COVID-19 diagnosis and of the odds ratio. ( D ) The ODoR-19 screening tool. After healthcare providers or contact tracers have excluded previous smell and/or taste disorders such as those resulting from head trauma, chronic rhinosinusitis, or previous viral illness, the patient can be asked to rate their current ability to smell on a scale from 0-10, with 0 being no sense of smell and 10 being excellent sense of smell. If the patient reports a value below or equal to 3, there is a high (red) or moderate (orange) probability that the patient has COVID-19. Values in yellow (ratings above 3) cannot rule out COVID-19.

Acknowledgements

The authors wish to thank all study participants, patients, and patient advocates that have contributed to this project, including members of the AbScent Facebook group. The authors wish to thank Micaela Hayes, MD for her input on clinical relevance of this project and Sara Lipson for her support.

Funding Deployment of the GCCR survey was supported by an unrestricted gift from James and Helen Zallie to support sensory science research at Penn State. Paule V. Joseph is supported by the National Institute of Nursing Research under award number 1ZIANR000035-01. PVJ is also supported by the Office of Workforce Diversity, National Institutes of Health and the Rockefeller University Heilbrunn Nurse Scholar Award. Mackenzie Hannum is supported by NIH T32 funding (DC000014). Masha Niv is supported by Israel Science Foundation grant #1129/19

Conflict of interest

Richard C. Gerkin is an advisor for Climax Foods, Equity Compensation (RCG); John E. Hayes has received speaker honoraria, consulting fees, and/or travel expenses from numerous organizations, including federal agencies, universities, nonprofit organizations, trade groups, and for-profit corporations, to present data on taste biology, perception and consumer behavior. Additionally, the Sensory Evaluation Center at Penn State routinely conducts product tests for industrial clients to facilitate experiential learning for students. Since 2018 Thomas Hummel did research together with and received funding from Sony, Stuttgart, Germany; Smell and Taste Lab, Geneva, Switzerland; Takasago, Paris, France: aspuraclip, Berlin, Germany. Christine E Kelly is the founder of AbScent, a charity registered in England and Wales, No. 1183468. Christophe Laudamiel has received fundings from scent related institutions and corporations, however for work totally unrelated to the field of the present study.

Supplementary Material

Collider bias results in underestimation of an association between smell loss and COVID-19

As others have noted, [START_REF] Griffith | Collider bias undermines our understanding of COVID-19 disease risk and severity[END_REF] collider bias, resulting from selection or conditioning on variables involved in the analysis, may result in the distorted association between COVID-19 and candidate symptoms or patient attributes. In the present sample, it is likely that we have selected for both a higher probability of COVID-19 and a higher probability of smell and taste disorders than the population at large. However, rather than leading to an overestimation of the positive correlation between smell loss and COVID-19, collider bias is expected to lead to an underestimation of this correlation ( Figure S1) . If we consider the hypothetical scenario in which there is no association between smell loss and COVID-19 status in the general population, we would expect a distribution similar to that depicted in Figure S1A , where the correlation between the likelihood of smell change and likelihood of COVID-19 is r = 0. Based on our recruitment method, we expect that the participants who elected to complete the GCCR core questionnaire were likely to have COVID-19, smell loss, or both. We can simulate participant selection to reflect this hypothesis by censoring subjects which do not meet a fixed sum of smell loss and COVID-19 probabilities (i.e., the red dots are excluded from the calculation of the correlation; Figure S1B ). As a result, the estimated correlation between smell loss and COVID-19 status originating from a population with r = 0 would be negative ( Figure S1B) . A similar scenario would manifest if the association between smell loss and COVID-19 status in the general population is positive ( Figure S1C ) . Again, simulating the removal of participants with low likelihood of having COVID-19 and/or reporting smell loss would result in a bias of the estimated correlation towards more negative values ( Figure S1D ). This collider bias indicates that the positive correlation between smell loss and C19+ is underestimated in the present sample. Therefore, our analyses represent a conservative scenario for the prediction of C19+ and C19-based on chemosensory alterations.

How representative is the GCCR sample?

As with most COVID-19 studies, 19 the sample studied here is not representative of the general population. To better understand the extent to which this is the case, we computed a cross-correlation between GCCR and YouGov data. [START_REF]YouGov-Data/covid-19-tracker[END_REF] These data were aligned by weighting YouGov samples to achieve an identical survey date distribution to the GCCR samples. Specifically, GCCR survey dates were converted to a YouGov "week number" because YouGov surveys only weekly. The distribution of week numbers was computed for each country in the GCCR data. The YouGov data for the same country was then weighted by week number to match the corresponding GCCR distribution for that country. So, for example, if a country had 10 GCCR survey responses in week 1 of the YouGov survey period, and 30 in week 2 of that period, then the YouGov data in week 1 would be weighted at 25% and in the YouGov data in week 2 at 75%. This procedure was applied independently for each country, and the weights were used to compute a weighted mean COVID-19-positive rate for each country from the YouGov data. This was then directly compared against the raw COVID-19-positive rate for each country in the GCCR data. A lag (x-axis value in Figure S2 ) of 0 exactly reflects the above description. Other values of the lag indicate that the alignment was shifted: for example, a lag of one week means that the YouGov data would be weighted 25/75 towards weeks 2 and 3, instead of weeks 1 and 2. Under the hypothesis that the COVID-19-positive rates in the two surveys are related, but may have different temporal dynamics, changing the lag allows these dynamics to be estimated. Figure S2 depicts the country-wise correlation in participants with a positive COVID-19 test results (C19+) fraction between the two datasets, as a function of the lag between GCCR survey date and YouGov survey date. The country-wise C19+ fraction is correlated (r ~0.5) when responses from the same calendar week are aligned, but GCCR COVID-19 status also anticipates YouGov status by up to 4 weeks (r~0.4).

Sample description

Based on responses to question 7 of the GCCR survey (" Have you been diagnosed with COVID-19? ", Appendix 1 ), participants can be split into six groups (see Figure 1 ). Participants who responded with Option 2 ("Yes -diagnosed with viral swab") or 3 ("Yes -diagnosed with another lab test") were classified as C19+; participants who responded with option 5 ("No -I had a negative test, but I have symptoms") were classified as symptomatic C19-; participants who responded with option 4 ("No -I was not diagnosed, but I have symptoms") were classified as C19 Unknown; participants who responded with option 6 ("No -I do not have any symptoms"), with option 7 ("Don't know"), or with option 8 ("Other") were classified as undefinable and excluded from the final analyses. To replicate our previous findings, [START_REF] Parma | More than smell. COVID-19 is associated with severe impairment of smell, taste, and chemesthesis [Internet]. Infectious Diseases (except HIV/AIDS)[END_REF] we first compared individuals newly included in the GCCR dataset (responses from 14 May to 2 July, 2020, replication sample in Figure 1 ) with COVID-19 who were lab tested and those who were diagnosed by a clinician based on the self-reported quantitative changes in smell, taste, chemesthesis, and nasal obstruction ( Figure S3) . Participants with lab-test confirmed C19+ did show slightly greater chemosensory deficits than did those diagnosed with C19+ clinically, but the difference was not clinically meaningful (smell: 4.4±28.6, p=2.7e-13) ( Figure S3, Table S2 ). We then focused our descriptive and predictive analyses of participants who received a positive (C19+) or a negative (C19-) lab test for COVID-19. We also computed descriptive and predictive analysis for the C19+ subsample who reported partial or full signs of recovery from their recent respiratory illness. Lastly, the unknown group was originally hypothesized as similar to the C19-group. Yet, the ratings of smell ability during illness suggest that the majority of these participants has a smell profile closer to C19+ than C19-( Figure S4 ). To maximize the validity of the COVID-19 diagnosis in our sample, we therefore excluded the C19 Unknown group from further analyses. ( E-F ) Principal component analysis shows that smell, taste, and chemesthesis changes in both the lab test (E) and clinical assessment (F) groups) were orthogonal to blocked nose changes, i.e., the three chemosensory changes were highly correlated across subjects whereas blocked nose changes were mostly uncorrelated.

Replication of previous analyses

The replication of Parma et al. [START_REF] Parma | More than smell. COVID-19 is associated with severe impairment of smell, taste, and chemesthesis [Internet]. Infectious Diseases (except HIV/AIDS)[END_REF] used the same Bayesian linear regression approach with Cauchy prior [r = sqrt(2)/2]. This approach is appropriate for estimating the strength of the evidence in support of the null hypothesis: the clinical assessment and the lab test C19+ groups show similar smell, taste, chemesthesis and nasal obstruction changes before vs. during the illness. The interpretation of the Bayes factors BF follows the classification scheme proposed by Lee and Wagenmakers [START_REF] Lee | Bayesian Cognitive Modeling: A Practical Course[END_REF] and adjusted from Jeffreys [START_REF] Jeffreys | The Theory of Probability[END_REF] , which considers BF > 3 as moderate evidence, BF > 10 as strong evidence, BF > 30 as very strong evidence and BF > 100 as extreme evidence for H 0 or H 1 .

Chemosensory characterization of C19+ and C19-

We asked how accurately COVID-19 status could be predicted from the survey responses. The data matrix had strictly non-negative values and was normalized (column-wise min=0, max=1) to apply regularization in an equitable fashion across features and give regression coefficients the same interpretation for each feature. Compared with the main text, similar results (but with non-zero coefficients for additional, likely spurious features) were obtained for smaller values of α, and inferior results for larger ones (which contained fewer or no non-zero coefficients). Quantitatively similar AUC values were obtained for other models predicting COVID-19 status using multiple features including ridge regression and random forest, but L1-regularized logistic regression consistently produced sparser models with comparable cross-validation accuracy. Each logistic regression model included an intercept term and one or more normalized features. Each model attempted to predict, using the value of the response to a single question (and an additive constant), whether a subject reported a C19+ or C19-status. Each ROC curve --constructed using predictions on holdout test sets and averaged over these test sets --summarizes the tradeoff between sensitivity (fraction of C19+ cases correctly identified) and specificity (fraction of predicted C19+ cases who were actually C19+) as the threshold value for the predictor is varied.

Value of using a scale rather than a binary response to detect C19+

We quantified the information entropy for each survey question used the following standard equation: I=sum_i(-p_i*log2(p_i)). Re-binning to mimic new scales was achieved by dividing response values by a constant and rounding to the nearest integer. Relative mutual information was calculated by computing the mutual information based on the following standard equation: MI=sum_i_j(p_i_j*log2(p_ij/(p_i*p_j)) between each feature and the C19+/C19-status, and then dividing by the entropy available from that same status distribution. Results indicate that soliciting responses on either a continuous 100-point scale or a downsampled 10-point numeric version of the scale is more informative about symptoms themselves and about COVID-19 status (given the symptoms) than soliciting binary responses ( Figure S5 ).

Figure S5. (A)

Relative information available from the distribution of responses to the two primary "Smell" survey questions. Binary refers to the yes/no question about symptomatic smell loss. A relative information of 1 would correspond to a question whose response is perfectly informative about COVID-19 status. By contrast, a similar question asked on a numeric scale (0-100, the original scale; or a hypothetical 10-point scale obtained by rounding responses) contains substantially more information due to the resolution of the scale. A 10-point scale may be familiar from clinical self-reports of pain. ( B) ROC Area-under-the-curve (AUC) is also higher for the full numeric scale or the hypothetical 10-point scale than for the binary question.

Prediction of recovery from COVID-19-associated smell loss

We applied the same predictive modeling framework used in Figure 4 to try to predict smell recovery in C19+ participants. In other words, we asked which survey responses predicted that a subject would fall into the Recovered Smell rather than the Persistent Smell Loss cluster, given both smell loss during the disease and C19+ status. The only predictive feature of any practical significance was "Days Since Onset" of respiratory symptoms (AUC=0.62), indicating that those who experienced their first respiratory symptoms less recently are more likely to have Recovered Smell ( Figure S6A ). Adding additional features to the model provided modest improvement (AUC=0.65 for the optimal model), but overall it was difficult to predict whether a C19+ participant would exhibit Recovered Smell or Persistent Smell Loss based on the data available ( Figure S6B ). Table S3 includes the means and SD by recovery group for C19+ and C19-participants. The core questionnaire of the Global Consortium for Chemosensory Research (GCCR) has been deployed in Compusense Cloud in 32 languages. The questionnaire was published previously [START_REF] Parma | More than smell -COVID-19 is associated with severe impairment of smell, taste, and chemesthesis[END_REF] and also appears in the NIH Office of Behavioral and Social Sciences Research (OBSSR) research tools for COVID-19. [START_REF]COVID-19_BSSR_Research_Tools[END_REF] Responses to the GCCR core questionnaire in 23 languages were collected between April 7 and July 2, 2020 and included in the final dataset, on which we conducted the analyses reported in this paper.


Diego Magaldi

Maria Ulfa

Sébastien Péralta

Fabrice Goubard

Thierry Pauporté

Thanh-Tuân Bui
email: tbui@cyu.fr

Carbazole-based material: Synthesis, Characterization and Application as hole transporting material in perovskite solar cells

Keywords: hole transporting materials, perovskite solar cell, carbazole, arylamine, oligomer

come

Introduction

Generally the active component of a perovskite solar cell (PSC), composed of an inorganic or hybrid perovskite material layer acting as the light absorber, is sandwiched between two charge transporting layers. The hole transporting layer (HTL) extract holes at the HTL/perovskite interface and transport them towards the cathode while the electron transporting layer (ETL) permits the electrons extraction and transport. It has been clearly established that the incorporation of HTL and ETL in PSC's device structure is highly important for the devices to be efficient and stable. [1] In this context, accurate design of HTL is of major interest. The HTL is usually made from a single hole transporting material (HTM). However, in some case a mixture of different HTM can be employed. [2] Hole transporting capacity is key for achieving Cite this paper as: D. Magaldi, M. Ulfa, S. Péralta, F. Goubard, Th. Pauporté, T.-T. Bui, Carbazole-based material: Synthesis, Characterization and application as hole transporting material in perovskite solar cells. J. Mater. Sci.: Mater. Electron. 32 (10) (2021) 12856-12861.

2 high photovoltaic efficiency. Usually, some additives and/or chemical dopants are incorporated to increase the charge transport capacity of the organic semiconductors. In addition, the HTM EHOMO and ELUMO must be compatible with the valence band maximum and conduction band minimum of the perovskite in order to provide the suitable driving force for charge transfer and block the electrons back transfer. Other researched characteristics include excellent thermal, morphological and photochemical stability, and good hydrophobic properties to protect the perovskite materials from moisture. The hole transporter can be either inorganic, organic or coordination compounds. [3,4] Organic HTMs are subclassified into molecular and polymeric materials. If molecular HTMs have well-defined molecular weight and structure and good synthetic reproducibility, the polymeric counterparts have important advantages in terms of processability, thermal and mechanical stability, and higher intrinsic hole mobility. [5] Among various organic HTM, carbazole-based HTMs is of particular interest thanks to their unique features suitable for developing photovoltaic materials. [6,7,[16][17][18][19][20][START_REF] Gratia | Carbazole-based material: Synthesis, Characterization and application as hole transporting material in perovskite solar cells[END_REF][22][23][24][25][8][9][10][11][12][13][14][15] We have previously exploited carbazole platform to design HTMs for solid-state DSSCs [9] and PSCs. [6,11,26] Bulky dendritic carbazole compound, which take the best of both molecular and polymeric HTMs features, has been proved to be an effective design strategy, giving improved PSCs in terms of the stability. [6,7] Herein a new oligomeric cyclic carbazole based HTM (Scheme 1) is proposed which provide device performance comparable to that of Spiro-OMeTAD and also markedly improves the device stability.

Scheme 1. Molecular structures of investigated HTMs

The presence of the 2,4,6-trimethylaniline unit as hydrophobic and structuring agent is expected to favor the nanostructuration in HTM inducing a better conductivity in PSC device. This singularity has already Cite this paper as: D. Magaldi, M. Ulfa, S. Péralta, F. Goubard, Th. Pauporté, T.-T. Bui, Carbazole-based material: Synthesis, Characterization and application as hole transporting material in perovskite solar cells. J. Mater. Sci.: Mater. Electron. 32 (10) (2021) 12856-12861.

3 been observed with PTAA as HTM in PSC. [27] Moreover, methyl groups decrease the arrangement disorder between molecules due to lower repulsion of methyl groups and benzene rings, favoring also the carrier mobility within the HTM. [28] 2. Results and discussion mixture. The obtained materials presented a good solubility in organic solvents commonly used for the deposition of HTM such as chlorobenzene, toluene, chloroform, etc. We then directly characterized this mixture for the purpose of application as HTM in PSCs.

Material Characterization

DFT Calculation

These oligomers (tris, tetra and penta cyclic compounds) were investigated by DFT. The optimized geometries and energies of the tetra-oligomer, the most abundant one, are given in the Figure 1 and the data for the other two compounds are provided in the ESI (Figures S1, S2 and Table S1). As can be observed for PCz1, their HOMO and LUMO were partially developed over the whole molecular structure. This is explained by the fact that the cyclic compounds have a distorted structure. All oligomers have nearly an identical optimized energy per repeated unit (Table S1, ESI). While the ELUMO levels are nearly identical for all the compounds, the tris oligomer has a deeper HOMO level compared to the tetra and penta oligomers.

Thermal, morphological and optoelectrochemical properties

PCz1 showed a good thermal stability with a Td > 340°C (Figure 2a) which is high enough for the applications in PSC. No melting point was observed until 280°C in differential scanning calorimetry (DSC) analysis (data not shown) suggesting that the material obtained is amorphous. However, the DSC curves revealed a complex behaviour due to the presence of different oligomers. The absorption properties of the PCz1 mixture recorded in dilute dichloromethane solution is shown in Figure 2b. It presents a strong absorption band centered at 338 nm and shoulders at lower energy attributed to an extended conjugated system including carbazole/tertiary amine ones. The simulated absorption spectra in CH2Cl2 of different oligomers are also displayed in Figure 2b with the maximum absorption wavelength are 331, 347 and 378 nm for tris, tetra and penta cyclic molecules, respectively. This is in good accordance with the assumption that PCz1 is a mixture composed mainly of these cyclic oligomers. PCz1 mainly absorbs in the UV spectral domain, which will not cause a competitive absorption effect with the perovskite materials. The optical bandgap (Eg = 2.74 eV) is calculated from the absorption onset wavelength (Figure 2b, 460 nm). This value is somewhat smaller than that of the Spiro-OMeTAD (3.00 eV). [9] Cyclic voltammetry (CV) was employed to investigate the redox behavior of PCz1 in dilute solution (Figure 2c). The cyclic voltammogram of PCz1 shows multiple oxidation processes. The first reversible oxidation could be attributed to the formation of radical cation of the carbazole moiety. PCz1 has an oxidation onset potential of -0.36 V versus Fc/Fc + , which is lower than that of Spiro-OMeTAD (-0.11 V/ Fc/Fc + ) measured under the same conditions. [9] These redox potentials were then employed to estimate their EHOMO and ELUMO in the solid state by using the ferrocene ionization energy as the standard.

EHOMO was estimated from the first oxidation potential while ELUMO was estimated from the EHOMO and Eg.

The EHOMO values of PCz1 and Spiro-OMeTAD are -4.74 eV and -4.99 eV, respectively, assuming that the ionization potential of Ferrocene is 5.1 eV versus vacuum. The corresponding ELUMO are -2.0 and -1.99 eV, respectively. The EHOMO level of PCz1 is thus higher than that of Spiro-OMeTAD meanwhile both Cite this paper as: D. Magaldi, M. Ulfa, S. Péralta, F. Goubard, Th. Pauporté, T.-T. Bui, Carbazole-based material: Synthesis, Characterization and application as hole transporting material in perovskite solar cells. J. Mater. Sci.: Mater. Electron. 32 (10) (2021) 12856-12861.

6 compounds have very similar ELUMO levels. This suggests that, energetically, PCz1 could be suitable as an alternative HTM to Spiro-OMeTAD.

In order to improve the device stability against moisture in real operation, the HTM layer must protect the perovskite layer as far as possible. Hydrophilic HTM will be more susceptible to moisture ingress than the hydrophobic ones. Even if the permeability to liquid water/moisture can also depend on other factors such as thin film porosity, it remains that hydrophobicity should play a key role in moisture resistance for films of similar smoothness and compactness. We performed water contact angle measurements on the PCz1 layers studied in this work . LiTFSI and tert butylpyridine were incorporated into the film for mimicking the condition in PSC. PCz1 film exhibited an average contact angle value of 93.7 ± 5.6° which correspond to rather high hydrophobic properties compared to Spiro-OMeTAD (70°) (Figure S3 in ESI). [29] It has been demonstrated that such hydrophobic properties are extremely beneficial to PSC stability. [30] A low surface roughness is an important requirement to decrease the current losses in PSC and get a better contact with the counter-electrode. For this purpose, the film forming property of doped PCz1 was examined by atomic force microscopy (Figure S4 in ESI). The PCz1 film showed an uniform and smooth surface with a mean surface roughness values of 0.97 nm. This low value should improve the contact with the perovskite layer and counter-electrode.

Photovoltaic application

PCz1 was then studied as HTMs using Cs0.08FA0.80MA0.12Pb(I0.88Br0.12)3 perovskite as the absorber material which has proved to be highly efficient for PSC application. [START_REF] Wang | Effects of Perovskite Monovalent Cation Composition on the High and Low Frequency Impedance Response of Efficient Solar Cells[END_REF][START_REF] Zhu | Interfacial Engineering through Chloride-Functionalized Self-Assembled Monolayer for High Efficiency Perovskite Solar Cells[END_REF] 2020) 744-752. The device structure employed was: Glass-FTO/cTiO2/mpTiO2/perovskite/HTM/Au with cTiO2 a compact layer prepared by spray pyrolysis and mpTiO2 a mesoporous TiO2 layer prepared by spin coating. [33,[START_REF] Viana | Proc. SPIE[END_REF] According to the experimental energy band alignment, PCz1 should be suitable and work as well as Spiro-OMeTAD. The best cell J-V curves are displayed in Figure 3a and the J-V curve parameters are gathered in Table 1. The initial performances of Spiro-OMeTAD cells were superior to those of the PCz1 cells. The Cite this paper as: D. Magaldi, M. Ulfa, S. Péralta, F. Goubard, Th. Pauporté, T.-T. Bui, Carbazole-based material: Synthesis, Characterization and application as hole transporting material in perovskite solar cells. J. Mater. Sci.: Mater. Electron. 32 (10) (2021) 12856-12861.

7 former exhibited a better Voc and Jsc. Both cells had a rather large hysteresis between the reverse and the forward scan curves. This trend was observed for both the record cells and the averaged data (Table S2 in the Electronic Supporting Information). Table 1 also report the J-V curve parameters after 7 weeks of aging (storage in the N2 filled glovebox). We remark that the efficiency of the Spiro-OMeTAD PSCs decreased during this period of time. On the other hand, the PCE of PCz1 cells increased significantly reaching a value of 18.04 % which is slightly superior to the initial PCE of the Spiro-OMeTAD PSC. Aging reduces the hysteresis in the case of the Spiro-OMeTAD device, while this parameter was slightly enlarged in the case of the PCz1 cell. Nevertheless, some of us have shown recently that with the triple cation perovskite employed in this study, the systems have a slow response in time and that the steadystate PCE is close to the PCE determined in the reverse bias scan conditions. [START_REF] Zhu | Interfacial Engineering through Chloride-Functionalized Self-Assembled Monolayer for High Efficiency Perovskite Solar Cells[END_REF] Figure 3b

Conclusion

In summary, novel carbazole-based oligomers were synthetized, characterized, and applied as hole transporting material in perovskite photovoltaic devices. The thermal, morphological, optical and electrochemical characterizations have shown that the PCz1 oligomer material is suitable for the role of HTM. Initial photovoltaic measurements gave a PCE of 16.84 % which increased over 6 weeks to reach a value of 18.04 %. This PCE is superior to the PCE of the benchmark Spiro-OMeTAD cell. Moreover, the former has been shown much more stable than the latter. Current study is being conducted to optimize the synthesis of this family of molecules. More compounds of this family will be prepared in order to better understand the molecular structure/photovoltaic performance relationship of this series of carbazole derivatives.

Materials and methods

The detailed descriptions of materials synthesis, characterization, computational DFT calculation, perovskite-based photovoltaic device fabrication and measurements are given in the Electronic Supporting Information.

Cite this paper as: D. Magaldi, M. Ulfa, S. Péralta, F. Goubard, Th. Pauporté, T.-T. Bui, Carbazole-based material: Synthesis, Characterization and application as hole transporting material in perovskite solar cells. J. Mater. Sci.: Mater. Electron. 32 (10) (2021) 12856-12861.

2. 1

 1 Material synthesis PCz1 was synthetized in three steps as detailed in Scheme 2. Briefly, N-(4-methoxyphenyl)carbazole (1) was produced by a copper-catalyzed Ullmann amination reaction. 1 was then treated by Nbromosuccinimide giving 3,6-dibromo-N-(4-methoxyphenyl)carbazole (2).

Scheme 2 .

 2 Scheme 2. Synthesis of carbazole-based HTM PCz1 The final step consisted in a Pd-catalyzed Buchwald Hartwig amination reaction. The treatment of 2 with an equimolar equivalence of 2,4,6-triphenylaniline gave an inseparable mixture of oligomers. HRMS analysis suggests clearly the co-existence of tris, tetra and penta co-oligomers in the mixture. The theoretical DFT calculations have confirmed the presence of a mixture of different oligomers in the

4

 4

Figure 1 .

 1 Figure 1. Optimized geometry and HOMO LUMO distribution of the tetra-oligomer.

5 Figure 2 .

 52 Figure 2. (a) TGA curve of PCz1. (b) Absorbance of PCz1 in dichloromethaneand calculated spectra for tris, tetra and penta cyclic molecules (CycloN, N=3, 4 and 5). (c) Cyclic voltammetry of PCz1 in dichloromethane.

  Add P. Wang, M. Ulfa, T. Pauporté, Effects of Perovskite Monovalent Cation Composition on the High and Low Frequency Impedance Response of Efficient Solar Cells. J. Phys. Chem. C, 122 (2018) 1973-1981 + T. Zhu, J. Su, F. Labat, I. Ciofini, Th. Pauporté*, Interfacial Engineering through Chloride-Functionalized Self-Assembled Monolayer for High Efficiency Perovskite Solar Cells. ACS Appl. Mater Interfaces, 12 (

8 Figure 3 .

 83 Figure 3. J-V and EQE curves of PSC devices (a) J-V curves of PCz1 and Spiro-OMeTAD cells, fresh and 7 weeks old; (b) EQE curves of PCz1 and Spiro-OMeTAD cells.

a 7

 7 weeks old cells. b Hysteresis index defined as: 𝐻𝐼(%) = 𝑃𝐶𝐸 𝑟𝑒𝑣 -𝑃𝐶𝐸 𝑓𝑜𝑟 𝑃𝐶𝐸 𝑟𝑒𝑣The aging of the devices and the stabilization of the perovskite layers and interfaces are important parameters for the development of this technology. We have followed the evolution of the J-V curve parameters and PCE of solar cells prepared with the two different HTMs and stored in a N2 filled glovebox. The results are shown in FigureS5in ESI. Clearly, the PCz1 cell presents a high stability with a PCE increase during the first weeks of aging. The improvement is mainly due to the enlargement of the Voc and, in a less extent, of the Jsc. On the other hand, the Spiro-OMeTAD PSCs present stable Voc and Jsc parameters while the FF decreases slightly. Overall, during the first 60 days, the PCE of the PCz1 cell increased by 3% while it decreased by 5.5% for the Spiro-OMeTAD cell. It can be explained by the rather large size and flexibility of the oligomer HTM compared to the molecular one. The perovskite is covered by a denser layer and then better protected against moisture and other degrading agents. We can also suppose that the alternation of mesitylene and anisole in the PCz1 groups allows the close packing of the molecules by the low repulsion derived of fewer methoxy groups on the molecule. The latter can be partially confirmed by the low roughness values of the deposited layer. This is in good agreement with Cite this paper as: D. Magaldi, M. Ulfa, S. Péralta, F. Goubard, Th. Pauporté, T.-T. Bui, Carbazole-based material: Synthesis, Characterization and application as hole transporting material in perovskite solar cells. J. Mater. Sci.: Mater. Electron. 32(10) (2021) 12856-12861. 9 higher hydrophobic properties of the PCz1 film compared to the Spiro-OMeTAD one. The enlargement of the Voc also suggests an improvement of the perovskite/PCz1 interface during the weeks following the cell preparation. Long-term stability and increment of the final PCE observed over time of 60 days is mainly due to the increase of final the voltage output from 0.98 V to 1.04 V. In parallel a small reduction of the current output going from 22.88 mA.cm -2 to 22.41 mA.cm -2 is observed. Accordingly, the final PCE went from 16.84 % to 18.04 % for the forward scan. It represents an increment of 7 % from the first PCE measurement. Many factors can contribute to the observed phenomena like the solid-state diffusion of the different components of HTL towards its self-stabilization.[START_REF] Lamberti | [END_REF] It is evident that the stabilization of the PCz1 cell signifies an amelioration of the interphase contact derived of the HTL nature.

Table 1 .

 1 Photovoltaic J-V parameters and PCE of best PSC cells prepared with PCz1 and Spiro-OMeTAD.

		HTM	Scan direction	Voc (V)	Jsc (mA.cm -2 )	FF (%)	PCE (%)	HI (%)
	Fresh cells	Spiro-OMeTAD	Reverse Forward	0.99 0.91	22.25 22.19	81.20 65.06	17.94 13.12	26.9
		PCz1	Reverse	0.95	21.88	80.87	16.84	30.1
			Forward	0.86	21.91	62.45	11.76
	Aged cells a	Spiro-OMeTAD PCz1	Reverse Forward Reverse Forward	1.00 1.02 1.04 0.97	22.62 22.55 22.41 22.44	75.55 68.79 77.55 55.70	17.14 15.76 18.04 12.17	8.1 32.5

Acknowledgements M.U. and D. M. acknowledges the Indonesian and Mexican governments for doctoral scholarships, respectively.

Conflicts of interest: the authors declare that there is no conflict of interest