The geometry in self-assembled superlattices of colloidal quantum dots (QDs) strongly affects their optoelectronic properties and is thus of critical importance for applications in optoelectronic devices. Here, we achieve the selective control of the geometry of colloidal quasi-spherical PbS QDs in highly-ordered two and three dimensional superlattices: Disordered, simple cubic (sc), and face-centered cubic (fcc). Gel permeation chromatography (GPC), not based on size-exclusion effects, is developed to quantitatively and continuously control the ligand coverage of PbS QDs. The obtained QDs can retain their high stability and photoluminescence on account of the chemically soft removal of the ligands by GPC. With increasing ligand coverage, the geometry of the self-assembled superlattices by solution-casting of the GPC-processed PbS QDs changed from disordered, sc to fcc because of the finely controlled ligand coverage and anisotropy on QD surfaces. Importantly, the highly-ordered sc supercrystal usually displays unique superfluorescence and is expected to show high charge transporting properties, but it has not yet been achieved for colloidal quasi-spherical QDs. It is firstly accessible by fine-tuning the QD ligand density using the GPC method here. This selective formation of different geometric superlattices based on GPC promises applications of such colloidal quasi-spherical QDs in high-performance optoelectronic devices. Gel permeation chromatography can finely control ligand coverage of PbS quantum dots. Self-assembly of these QDs with different ligand density leads to the formation of 2D square, hexagonal and 3D simple cubic and face-centered cubic superlattices. However, such techniques to control the ligand coverage render the solubility, processability, and stability of the QDs poor because controlling the degree of ligand removal on the QDs is difficult on account of their high reactivity. Currently, the liquid/air interface method is widely used to form ‘confined-but-connected’ epitaxially fused QD superlattice structures by excessive ligand removal,7,8,19,29 resulting in a severe deterioration in the photoluminescence quantum yield (PLQY),30 and the resulting QD self-assembled superlattices are thus difficult to use in luminescence-related applications.Precipitation in a poor solvent and redispersion in a good solvent (PR),31–35 On the other hand, the GPC method in this study controls ligand coverage of the QDs and does not show any size-exclusion effect on the size of the QDs. This GPC process enables the soft, quantitative, and continuous control over the ligand coverage of the QDs, which retain sufficient solubility for processability and luminescence properties. The different self-assembled superlattices of GPC-processed QDs can be prepared predictively and selectively, not only with 2D  arrangements, but also with 3D (sc and fcc) arrangements. It is worth noting that unusual highly long-range-ordered 2D square arrays and 3D sc supercrystals along the [100]QD facet can be prepared from quasi-spherical PbS QDs by direct solution-casting on solid substrates. At this point, we think that the mechanism for the selective formation of different self-assembly geometries is due to the anisotropy on the QD surface as a result of the gentle ligand removal by GPC.Here, we develop gel permeation chromatography (GPC) as a method to remove ligands from PbS QDs in solution. GPC with a size-exclusion effect for the purification and separation of colloidal QDs has been reported to remove residual long alkyl ligands and high-boiling solvents such as octadecene after synthesis.2) according to a literature procedure.36 After the synthesis, a QD sample was purified once by PR prior to the GPC process, as unreacted precursors should be removed while as many ligands as possible should remain on the QD surface; this sample was named before-GPC. Transmission electron microscopy (TEM) measurements of the before-GPC sample revealed uniform sphere-like structures with an average diameter of 7.3 nm  ligands were synthesized from lead chloride (PbCl−1. The eluted QD solution was collected in five consecutive portions of 0.8 mL (GPC-1 to GPC-5). The absorption spectra for before-GPC and GPC-1–5 in tetrachloroethylene  to GPC-5 (22.5 wt%). These weight losses of bound OA can be converted into weight ratios of OA ligands bound to a QD. In order to estimate the number of ligands on a QD, i.e., in order to transform the weight ratio into a ligand density, the atomic Pb/S ratio of the QDs is required. For that purpose, we used Rutherford backscattering (RBS) spectrometry .39 The average Pb/S ration (1.27) was subsequently used for the estimation of the ligand density   was carried out in order to evaluate the ligand coverage of the PbS QDs . In the y Fig. S3 of the b.1 nm−2) . It shou1H nuclear magnetic resonance (NMR) spectroscopy using ferrocene as an internal standard. The NMR spectra showed two peaks corresponding to vinyl protons of the free and bound OA ligands at 5.45 ppm and 5.60 ppm, respectively    are consThese TGA and NMR measurements clearly demonstrate that the ligand density of the PbS QDs gradually increases from GPC-1 to GPC-5, and that the GPC method is thus able to continuously and precisely control the ligand density of the PbS QDs by collecting different fractions according to the retention time. This is far superior to other methods of ligand-coverage control such as the PR method, the liquid/air interface method, and oxidatively removing ligands in air. Another advantage of the GPC method for controlling the ligand coverage of colloidal QDs is that the QD samples retain their solubility in toluene and are stable in solution during and after the GPC process.In this GPC process, the QDs with fewer ligands elute early, while the QDs with more ligands elute later. Interestingly, this order stands in sharp contrast to conventional GPC methods, given that QDs with fewer ligands should have a smaller hydrodynamic volume in toluene than those that contain more ligands. Therefore, ligand control by the GPC method in this study is not based on a size-exclusion effect, which is further corroborated by the exponential increase of the ligand density from GPC-1 to GPC-5 . At this6–9 A selected area electron diffraction (SAED) analysis of this square superlattice revealed a clear diffraction pattern, which was ascribed to the [200] facet of the PbS crystals, along the direction of the square arrangement, demonstrating that the GPC-2 QDs in the square superlattice are highly oriented along the [100]QD facets (QD direction in the square superlattice (12.2 nm) is longer than that in the hexagonal lattices of the before-GPC and GPC 3–5 QDs (9.2–9.6 nm). This difference may be attributed to the different interparticle interactions between QDs in square and hexagonal assembled superlattices. The direct weak [100]QD-to-[100]QD facets interactions between GPC-2 QDs in oriented square superlattices can produce a longer interparticle spacing. In contrast, the different direct facet-to-facet contacts , especially the strong [111]-to-[111] contacts, and the orientation of ligands at [111]QD can strengthen the packing density of surface ligands and QD–QD interactions to reduce the interparticle spacing in other hexagonal superlattices.22The 2D self-assembly of the PbS QDs was achieved by drop-casting dilute toluene solutions onto solid substrates. Before-GPC and GPC-1–5 formed 2D superlattices with different and unique geometric arrangements that were examined by TEM . Before-D facets . ImportaD facets . The GPCD facets , similarD facets . The dis42 The triangular shape of the supercrystals suggests that they are formed on the [111]supercrystal plane of the fcc packing from a hexagonal layer on the substrate.43 High-resolution scanning electron microscopy (HR-SEM), TEM, and scanning transmission electron microscopy (STEM) measurements of the supercrystals supported this fcc supercrystal structure of the GPC-5 QDs , the PbS QDs became insoluble and fused Fig. S11. Therefo Fig. S12.6 This f50,51 We presume that clear [100] facets cannot be formed on the surface of the small PbS QDs and that the crystallographic orientation is not achieved in the square and sc self-assembly superlattices.52To investigate the effect of the size of the QDs on the self-assembly after the GPC process, we synthesized smaller PbS QDs (diameter: 4.3 nm) with an absorption peak at 992 nm Fig. S13. Then, w40 Thus, although the distribution of ligands on different QD facets cannot be accurately evaluated by experiments, the ligand-removal process in our GPC method and the mechanism of their tunable self-assembly geometry can be discussed based on the ligand-density curve  using a gel permeation chromatography (GPC) technique. This GPC technique is, in contrast to conventional GPC methods, not based on a size-exclusion effect, but able to finely and continuously remove ligands coordinated to the surface of the PbS QDs, thus providing precise control over the ligand density in different GPC fractions. Direct solution-casting of colloidal PbS QDs with different ligand density led to the selective formation of 2D square and hexagonal superlattices as well as 3D supercrystals with simple cubic (sc) and face-centered cubic (fcc) packing. Based on the quantitatively controlled ligand coverage, the quasi-spherical PbS QDs can self-assemble into unusual square arrangements and sc supercrystals with highly ordered crystallographic orientation. Anisotropy on QD surfaces as a result of gentle ligand removal in the GPC is considered a reliable mechanism for the formation of distinct self-assembly geometries. The stability of the QDs in solution after controlling the ligand density and the near-infrared-photoluminescence (NIR-PL) in solid state are superior to those obtained using other ligand-removal techniques. It is obvious that this soft removal of ligands by GPC technique as a new function exhibits unique advantages in the modulation of QD self-assembled geometry and their optoelectronic properties. The various multidimensional self-assembly geometries of QDs by this operationally simple GPC method can thus be expected to provide significant insights into the formation mechanism and applications of QD superlattices. The significance of this GPC technique also inspires us to further investigate its potential applications to the functional metal or metal oxide nanoparticles with weakly bound ligands in the future.J. Liu synthesized and purified the QDs, and measured TGA, NMR, XRD, UV and PL. J. Liu and K. Enomoto measured TEM and SEM. K. Enomoto measured RBS. D. Inoue measured STEM. T. Kotaro contributed to get preliminary results on the GPC treatment of QDs. All experiments were performed under discussion with Y.-J. Pu. J. Liu, K. Enomoto, and Y.-J. Pu analyzed the data. J. Liu and Y.-J. Pu wrote the manuscript. All authors discussed and commented on the manuscript.There are no conflicts to declare.SC-012-D1SC02096J-s001


The early detection of clusters of infectious diseases such as the SARS-CoV-2–related COVID-19 disease can promote timely testing recommendation compliance and help to prevent disease outbreaks. Prior research revealed the potential of COVID-19 participatory syndromic surveillance systems to complement traditional surveillance systems. However, most existing systems did not integrate geographic information at a local scale, which could improve the management of the SARS-CoV-2 pandemic.The aim of this study is to detect active and emerging spatiotemporal clusters of COVID-19–associated symptoms, and to examine (a posteriori) the association between the clusters’ characteristics and sociodemographic and environmental determinants.This report presents the methodology and development of the @choum (English: “achoo”) study, evaluating an epidemiological digital surveillance tool to detect and prevent clusters of individuals , aged 18 years or above, with COVID-19–associated symptoms living and/or working in the canton of Geneva, Switzerland. The tool is a 5-minute survey integrated into a free and secure mobile app (CoronApp-HUG). Participants are enrolled through a comprehensive communication campaign conducted throughout the 12-month data collection phase. Participants register to the tool by providing electronic informed consent and nonsensitive information . Symptomatic participants can then report COVID-19–associated symptoms at their onset  by tapping on the @choum button. Those who have not yet been tested are offered the possibility to be informed on their cluster status . At each participation step, participants are redirected to the official COVID-19 recommendations websites. Geospatial clustering analyses are performed using the modified space-time density-based spatial clustering of applications with noise (MST-DBSCAN) algorithm.The study began on September 1, 2020, and will be completed on February 28, 2022. Multiple tests performed at various time points throughout the 5-month preparation phase have helped to improve the tool’s user experience and the accuracy of the clustering analyses. A 1-month pilot study performed among 38 pharmacists working in 7 Geneva-based pharmacies confirmed the proper functioning of the tool. Since the tool’s launch to the entire population of Geneva on February 11, 2021, data are being collected and clusters are being carefully monitored. The primary study outcomes are expected to be published in mid-2022.The @choum study evaluates an innovative participatory epidemiological digital surveillance tool to detect and prevent clusters of COVID-19–associated symptoms. @choum collects precise geographic information while protecting the user’s privacy by using geomasking methods. By providing an evidence base to inform citizens and local authorities on areas potentially facing a high COVID-19 burden, the tool supports the targeted allocation of public health resources and promotes testing.DERR1-10.2196/30444 Since 2020, many countries worldwide, including Switzerland, have experienced multiple fluctuations of SARS-CoV-2 transmission associated with cycles of increased followed by relaxed measures ,2. TheseTraditional disease surveillance systems, relying on SARS-CoV-2 cases confirmed by real-time polymerase chain reaction (RT-PCR) or antigenic tests, suffer from underreporting and a delay of notification due to the time between the onset of symptoms and case declaration . There iThe @choum (English: “achoo”) study proposes a digital participatory epidemiological surveillance tool using precise space-time data to monitor COVID-19–associated symptoms at their onset in the canton of Geneva, Switzerland. The @choum tool is a 5-minute survey that profits from being integrated into a free and secure preexisting app for COVID-19 resources and information developed by the University Hospitals of Geneva (HUG). Upon registration to @choum, participants can report COVID-19–associated symptoms at their onset by tapping on the @choum button visible on the home screen. They are then redirected to the official COVID-19 recommendations websites. This study provides an innovative combination of best practices in participatory surveillance and spatiotemporal epidemiology. This report describes the rationale and methodology of the @choum study, and presents the development procedure of the @choum digital tool, which has been publicly available since February 11, 2021.The specific study objectives are as follows: (1) to prospectively detect active and emerging spatiotemporal clusters of reported COVID-19–associated symptoms by running a daily automated spatiotemporal cluster detection algorithm; and (2) to retrospectively analyze the outbreaks detected over the 12-month data collection phase, and examine the association of outbreak characteristics with sociodemographic and environmental factors.The @choum study evaluates a digital participatory epidemiological surveillance tool to detect and prevent spatiotemporal clusters of individuals, aged 18 years or above, with COVID-19–associated symptoms living and/or working in the canton of Geneva, Switzerland. Its design integrates both prospective and retrospective analytical features. The study began on September 1, 2020, and will be completed on February 28, 2022.This study takes place in the canton of Geneva. It is carried out by an interdisciplinary team based at the HUG in close collaboration with experts from the University of Geneva (UNIGE), the Swiss Federal Institute of Technology Lausanne (EPFL), and the University of Paris (UP). The different components of the study have been distributed across the governance team members as follows: digital tool development, Direction of Information Systems, HUG; communication campaign strategy, Communication Directorate, HUG; spatiotemporal analyses, Geographic Information Research and Analysis in Population Health, HUG, UNIGE, and EPFL; and smart testing referral strategy, Division of Primary Care, HUG. Collaborators at the Center for Research and Interdisciplinarity at UP provide expertise on citizen-based participation strategies. Prior research has demonstrated our extended team’s productive collaboration, generating impactful research that integrates expertise in the fields of population health, spatial epidemiology, and virology ,22,25.Participant eligibility criteria include (i) aged≥18 years, (ii) living and/or working in the canton of Geneva, (iii) having access to a smartphone with a Swiss or French phone number and a connection to the internet, and (iv) being capable of providing informed consent. Individuals not meeting these criteria are prevented from completing study registration but are redirected to official recommendation websites. All participants provide electronic informed consent (eIC) to participate in the study. Participant enrollment began on February 11, 2021, and will be completed on February 28, 2022, meaning that participants can enroll during the entire data collection phase.Our approach to eIC follows the guidelines provided by the local institutional ethics committee, and requires participants to (i) read the study electronic information letter and declaration of consent displayed within two scrolling menus, (ii) check the consent box, and (iii) tap the “I accept” button. The study does not ask for the name or signature of participants to ensure individual privacy. Participants are informed that they can opt out at any time via their profile or by contacting the research team. In the absence of an opt-out request, all participants are followed throughout the data collection phase.A 5-month preparation phase, starting on September 1, 2020, has preceded the 12-month data collection phase. The development milestones included: (1) frontend development ; (2) backend development (database); (3) spatiotemporal clustering analysis implementation and testing; (4) data exchange between the app, server, and Python script running daily clustering analyses; and (5) complete security assessment. Following an iterative process, the tool’s content was reviewed and refined by experts in infectious disease, epidemiological research, citizen science, health communication, and app design. Multiple technical and analytical tests have been performed at various development stages of the tool in preproduction and production modes.A 1-month pilot phase was performed among 38 pharmacists working in seven pharmacies located in the canton of Geneva before the study launch. The pharmacists were asked to follow expected app utilization scenarios  and to complete a 10-minute satisfaction online questionnaire.We developed a comprehensive two-phase multichannel communication campaign launched together with the @choum tool on February 11, 2021 . This caIndividuals wishing to participate in the study can download the CoronApp-HUG, freely available on Android (Google Play Store)  and iOS Participation in the @choum study is simple, takes approximately 5 minutes, and consists of three steps: (1) consent and registration, (2) report of COVID-19–associated symptoms by tapping on the @choum button, and (3) COVID-19 recommendations. Registration is completed only once, whereas episodes of COVID-19–associated symptoms can be reported at multiple time points throughout the data collection phase. An information button is displayed on the upper right corner of some selected screens, which provides participants with more detailed explanations relevant to each participation step. The main study screens are presented in First, individuals interested in participating receive information on the study goals, benefits, and impacts of participation. Second, they go through the electronic letter of information and the declaration of consent to give their eIC via the “I accept” button, designed for this purpose. Individuals are informed that they will be asked for their work and home addresses but that these addresses are not used for tracking purposes and are replaced with masked geographic coordinates. The geographic masking procedure consists of displacing geographic coordinates randomly within a circular radius of 200 meters around their original position . IndividWhen perceiving COVID-19–associated symptoms, participants can start the symptoms report process by tapping on the @choum button. Users are required to go through a list of symptoms before proceeding further into their symptom report. This step aims to ensure that participants are informed about the symptoms typically associated with COVID-19. Participants are asked a set of questions (detailed in the Data Collection section) concerning their symptoms. If they have not yet been tested for SARS-CoV-2, they are given the possibility to be informed on whether they currently live or work in an area where a high number of other participants have reported COVID-19–associated symptoms . This feedback is based on the automated spatiotemporal clustering analysis results updated daily and returned to participants via specific screens within the app to encourage user engagement. Symptomatic participants with addresses located within such a cluster are further encouraged to get tested for SARS-CoV-2 and subsequently report the test date and result. This information is used to filter the addresses included in the prospective spatiotemporal cluster detection analyses.Lastly, participants are redirected to publicly available and official recommendation websites based on their reported information. Participants who have symptoms but have not yet been tested for SARS-CoV-2 are redirected to a survey tool by the Swiss Federal Office of Public Health (SFOPH) . The tooEnrollment and follow-up data are collected at study registration and at each report of COVID-19–associated symptoms, respectively . EnrollmData on adoption, participation, and usage behaviors are collected from the Google Play Store, Apple Store, and CoronApp-HUG app. Data obtained from these sources include the number of CoronApp-HUG landing page visits per day on each store, the number of CoronApp-HUG downloads per day, and app utilization pattern metrics.Since the tool’s launch, data are extracted and analyzed weekly by the research team to monitor adoption, participation, and currently active and emerging spatiotemporal clusters. Information on areas currently facing increased COVID-19–associated symptoms may be shared with local public health authorities to guide local interventions. Individual-level data will not be shared at any time, and an assessment of possible participation biases will be conducted before sharing any information to avoid risks of misguiding public health efforts. The evolution of the participation rates and the geographical distribution of participants are used to adapt the communication campaign activities in intensity and location.All collected data are treated confidentially and securely stored in the HUG-located server, respecting the general data protection regulation legislations of the European Union (GDPR) and Geneva (LIPAD). A unique validation code sent to the participant is used to confirm the participant device’s ownership and reduce abusive system use. The data collected on the frontend of the app are sent to the server at specific time points: upon study registration completion, after being asked about SARS-CoV-2 test results, and every time the validation code is sent . The geoThe sample size was calculated as ~5000 participants to detect active and emerging spatiotemporal clusters of reported COVID-19–associated symptoms. This analysis assumed a total of 500,000 potential participants aged≥18 years  ,35. Out Analyses will be conducted using Python 3.8 , R 4.0.5EpsS) and a time window defined by two parameters, EpsT1 and EpsT2, which determine the threshold value for the longest and shortest transmission period, respectively. Similar to the traditional DBSCAN, the MST-DTSCAN algorithm then classifies points into three categories: core, border, and noise points [core point is a point with enough spatiotemporal neighbors to be considered the major structure of a cluster with high incidence. The minimum number of neighbors that a point must have to be classified as a core is defined by the parameter MinPts. A border point is a point that is part of a cluster without being itself the center of a cluster because it does not have enough neighbors. Finally, noise points are outliers located in low-incidence areas [The prospective detection of spatiotemporal clusters will be achieved using the modified space-time density-based spatial clustering of applications with noise (MST-DBSCAN) algorithm, designed to detect, characterize, and visualize disease cluster evolution . The MSTe points ,46. A coce areas .MinPts=3, EpsT1=14, EpsT2=1, and EpsS=600 meters. The time window is specified according to the current knowledge on incubation and transmission periods [MinPts is set to 3 according to the cluster definition used by local health authorities [To obtain the most up-to-date information on active and emerging clusters, a Python (v3.8) script, based on the package pySDA 0.1.6 , is auto periods . The spa periods ,25. The horities . The parDescriptive statistics of user participation, retention, and collected participant data will be computed using means (SD) and medians (IQR), and frequencies for continuous and categorical variables. These will enable assessing our sample’s representativity compared to the canton of Geneva’s general population.The number of monthly active users and the number of created and deleted accounts will be monitored during the entire study period, which will enable estimating the retention rate of the @choum study. The performance and the conversion rate of the digital communication campaign will be monitored for each employed social media platform  by collecting metrics on the number of views and the number of users interacting with each communication post. Additionally, the number of downloads of the CoronApp-HUG and the number of @choum registered users will be monitored weekly following these communication campaigns. Together, this will allow us to estimate the conversion rate of the communication efforts.We will analyze outbreaks across the entire study period using spatiotemporal and purely spatial statistic methods to understand the evolution of the outbreaks detected over the 12-month data collection phase. Unusual utilization patterns such as repeated reports  of symptoms with positive RT-PCR test results will be classified as “spam” and discarded from retrospective analyses. However, this potential risk of high users falsely inflating reports in location and time has been reduced by limiting users to only report COVID-19–associated systems once every 14 days to ensure that reports correspond to a single episode of COVID-19–associated symptoms. Multiple methods will be used to provide a more complete and robust assessment of the underlying spatiotemporal process ,51, and The study began on September 1, 2020, and will be completed on February 28, 2022. The @choum study received final approval on November 12, 2020, by the Cantonal Research Ethics Commission of Geneva, Switzerland (2020-01586). A 5-month preparation phase has preceded the 12-month data collection phase starting on September 1, 2020, and February 11, 2021, respectively. Multiple tests performed at various time points throughout the first 4 months of the preparation phase have helped to improve the tool’s user experience. Subsequently, the 1-month pilot phase conducted among 38 pharmacists confirmed that the spatiotemporal clustering analyses worked appropriately, and the feedback helped to improve the user experience and address technical bugs. Since the tool’s launch to the entire population of Geneva on February 11, 2021, and as of May 15, 2021, we have enrolled over 1000 eligible participants. The primary study outcomes are expected to be published in mid-2022.The @choum study seeks to develop, implement, and test an innovative participatory epidemiological surveillance tool to rapidly detect and prevent clusters of COVID-19–associated symptoms by using precise geographic information. Multiple tests and a 1-month pilot study conducted among 38 pharmacists working in 7 Geneva-based pharmacies have confirmed the proper functioning of the tool. Since the tool’s launch to the entire population of Geneva on February 11, 2021, daily prospective clustering analyses at a local scale help detect active and emerging spatiotemporal clusters, and thus inform citizens and local authorities on areas potentially facing a COVID-19 burden.The retrospective analyses performed at the end of the study will deliver critical insights into the mechanisms underlying the diffusion dynamics of SARS-CoV-2.The rationale underlying the @choum study builds on the well-recognized need to rapidly inform key stakeholders  about geographical areas facing a higher burden of disease ,9,54. PrSince the start of the pandemic, a growing body of literature has highlighted the existence of health inequalities in COVID-19 infection and mortality -59. HoweA distinctive feature of the @choum tool lies in the unique possibility for participants who have not yet been tested to be informed on their current cluster status . In the context of testing fatigue , this reSeveral limitations of this study merit comment. First, generalization will be limited to the adult population living or working in the canton of Geneva. Second, adoption and utilization of the @choum tool might suffer from a participation bias, as attitudes toward digital COVID-19 public health tools such as contact tracing apps vary among sociodemographic groups ,65. HoweTo the best of our knowledge, @choum is the first participatory epidemiological surveillance system combining fine-scale spatiotemporal data with prospective clustering analyses. Such a tool could be helpful to effectively prevent the spread of COVID-19 and related diseases. This tool has the potential to provide timely and precise geographical information to encourage infection control interventions at a local scale and public health recommendations compliance. The study findings will also advance other research contributions investigating the complex relationship of environment and health using novel digital technologies. It is thus hoped that the @choum study will be a valuable support in the fight against COVID-19.