Artificial intelligence and machine learning (AI/ML) tools have the potential to improve health equity. However, many historically underrepresented communities have not been engaged in AI/ML training, research, and infrastructure development. Therefore, AIM-AHEAD  seeks to increase participation and engagement of researchers and communities through mutually beneficial partnerships. The purpose of this paper is to summarize feedback from listening sessions conducted by the AIM-AHEAD Coordinating Center in February 2022, titled the “AIM-AHEAD Community Building Convention (ACBC).” A total of six listening sessions were held over three days. A total of 977 people registered with AIM-AHEAD to attend ACBC and 557 individuals attended the listening sessions across stakeholder groups. Facilitators led the conversation based on a series of guiding questions, and responses were captured through voice and chat via the Slido platform. A professional third-party provider transcribed the audio. Qualitative analysis included data from transcripts and chat logs. Thematic analysis was then used to identify common and unique themes across all transcripts. Six main themes arose from the sessions. Attendees felt that storytelling would be a powerful tool in communicating the impact of AI/ML in promoting health equity, trust building is vital and can be fostered through existing trusted relationships, and diverse communities should be involved every step of the way. Attendees shared a wealth of information that will guide AIM-AHEAD’s future activities. The sessions highlighted the need for researchers to translate AI/ML concepts into vignettes that are digestible to the larger public, the importance of diversity, and how open-science platforms can be used to encourage multi-disciplinary collaboration. While the sessions confirmed some of the existing barriers in applying AI/ML for health equity, they also offered new insights that were captured in the six themes. Artificial intelligence and machine learning (AI/ML) have gained significant traction in the field of healthcare over the past several years. Innovative clinical applications are at the forefront of this “next frontier;” however, AI/ML can also be used to push the envelope for health equity by providing these tools to stakeholders at the grassroots level. We held nation-wide listening sessions with stakeholders from diverse organizations and institutions who are interested in using AI/ML to address health disparities, in order to shed light on the unique barriers these groups face. We were able to gather insight on how AI/ML tools could be improved, opportunities to increase diversity in data science, and using trusted community networks to ensure sustainability. This manuscript offers an approach to improving AI/ML data, infrastructure, and training that places community voices at the forefront in order to empower stakeholders to effect change. Artificial intelligence (AI) involves computer science approaches to designing applications that can carry out intelligent tasks ,2. MachiSDoH are social factors such as built environment, access to healthcare, education, and distribution of resources ,11. SDoHAlgorithms have the potential to significantly impact patients and communities, and it is vital that equity is a top priority . ML apprInstead of continuing to use algorithms that are non-transparent and were built with data lacking diversity, a proactive approach should be taken to develop models that promote health equity ,14. To iFurthermore, there needs to be an increased emphasis on using ethical frameworks for AI/ML to promote health equity and justice throughout the AI lifecycle ,20. TraiTo advance health equity and researcher diversity in AI/ML, a consortium model was proposed by NIH with four key areas: partnership, research, infrastructure, and data science training. The consortium model is gaining momentum in the biomedical research community given the ability for stakeholders to share resources, collaborate on a shared vision, and build strong partnerships . The AIMA-CC. The four cores include the Leadership/Administration Core (LAC), Data Science Training Core (DSTC), Data and Research Core (DRC), and Infrastructure Core (IC). Within the LAC, there are six hubs separated by region: North & Midwest Hub, West Hub, South Central Hub, Southeast Hub Meharry, Southeast Hub Morehouse, and Northeast Hub. The other two hubs within the LAC serve as overarching support: Central Hub and Communications & Dissemination Hub. The AIM-AHEAD Consortium consists of the A-CC (cores/hubs), NIH, partners, and stakeholders.One goal of the A-CC includes gathering input from key stakeholders on their health disparities and AI/ML research priorities, training needs, and data and infrastructure needs in the near-, medium-, and long-terms. As learned through the NIH AIM-AHEAD Stakeholder Engagement Forum, developing a consortium on the topic of AI/ML with the goal of addressing health disparities requires thoughtful and early feedback from communities and stakeholders . The thoAs an overview, we conducted nation-wide listening sessions that were developed to reach a large swath of persons from different fields . Social media and other virtual networks were used for recruitment of participants to the listening sessions. Semi-structured questions were used to elicit participant perspectives about AI/ML and health equity, and feedback was collected via typed and verbal responses. Qualitative thematic analysis was used to assess de-identified transcripts from these sessions and identify key themes. The North Texas Regional Institutional Review Board determined this analysis of de-identified transcripts to be Not Human Subjects Research.https://www.slido.com), hosted mock sessions for support staff, wrote scripts for moderators, created templates and checklists for notetakers/facilitators, etc. During the mock sessions a risk assessment was also performed for the moderators in the event that the conversation turned hostile, was hacked, or if they did not know how to respond to a participant. It was determined that in addition to plenty of A-CC members being in attendance during the sessions, Slack would also be used to communicate behind the scenes with the moderators and other A-CC support to assess any situations and to help the moderators in real time. Meanwhile, the Program/Theme Committee was charged with developing the content for the sessions, guiding questions, ACBC presentation slides, and pre- & post-surveys. The listening session categories were vetted by a team of experts within the A-CC. Sessions were developed with the mindset to ensure everyone felt free, safe, and comfortable sharing their thoughts, ideas, and suggestions. It was acknowledged that attendees’ voices, shared experiences, and insights will help AIM-AHEAD prioritize needs and resources in the AI/ML field. All levels of knowledge regarding AI/ML and healthy equity were welcomed to join.A Logistics Committee and Program/Theme Committee was developed with participation from all four A-CC cores. The Logistics Committee planned the event day/time, set up the meeting in HIPAA-compliant Zoom, developed plans for using the Slido platform : colleges, universities, minority serving institutions, etc.; (2) Consortiums on AI/ML & Data Science—this session was held for those in other consortiums, such as (but not limited to): consortiums on AI/ML, big data, data science, NIH-funded programs/organizations, etc.; (3) Industry—this session was held for those in industry, such as (but not limited to): business enterprises of all sizes, pharmaceutical & biotechnology industries, etc.; (4) Healthcare—this session was held for those in healthcare, such as (but not limited to): healthcare delivery systems, insurers/payers, regulatory bodies, healthcare providers, community-based health clinics, health-related associations/organizations, professional societies, local/regional/national healthcare advocates, local/state/federal health departments, etc.; (5) Community-Based—this session was held for those in community-based organizations, such as (but not limited to): advocacy/justice organizations, faith-based organizations, service-based organizations, non-profits, etc.; (6) General Listening Session—this session was held for individuals who felt that their affiliation did not align with one of the other categories, or if they wanted to know more about AI/ML and health equity. While it was thought that each of these groups would have unique perspectives regarding AI/ML and health equity, it was also assumed that there would be relatable themes present within each of the groups’ sessions. Participants self-identified which session they felt most closely aligned with their affiliation and/or interest.The sessions were geared toward listening to the unique needs of various stakeholder groups, as determined by NIH during the creation of AIM-AHEAD and by consensus of the A-CC: (1) The session began with a short introduction video featuring AIM-AHEAD’s Contact Multiple Principal Investigator, Dr. Jamboor Vishwanatha. Attendees were encouraged to engage verbally with moderators and other attendees during the sessions, and/or enter comments into the chat. Slido is a tool that was used for chatting, in which attendees could respond to particular topics and upvote comments they agreed with. Each session had three moderators, representing each of the four cores across the A-CC. There were also A-CC notetakers to take notes on major themes from the discussion. Lastly, the Central Hub from the LAC provided support staff to facilitate the Zoom sessions, provide technical support, and assist the moderators with monitoring the Slido chat. A pamphlet was sent out to registrants before the event in order to provide some background information on AIM-AHEAD and AI/ML, as well as introduce key terms that might be used during the sessions.https://aim-ahead.net/). This site served as a source of information as well as a registration portal for each of the listening sessions. The registration process for ACBC utilized a new software called SignUp, which was developed for the AIM-AHEAD Connect (https://connect.aim-ahead.net/) platform. When a user registered for a session, they were prompted to complete the registration fields and were then provided a link to add the event to their personal calendar. The registration fields included demographics, research interests, interest in AIM-AHEAD, if they will be attending other sessions, how they think AI/ML could support their interests/mission, what they were hoping to gain from joining AIM-AHEAD, any questions they would like to pose prior to the session, and if they would like to join the AIM-AHEAD Listserv. To lead people to the ACBC page, an alert banner was added to the top of every AIM-AHEAD.net page with information about ACBC.Listening sessions were promoted through social media advertisements, emails, and snowball outreach procedures. To provide information about ACBC, a new page was created on the AIM-AHEAD.net website , the Community Engagement Alliance (CEAL) Against COVID-19 Disparities, Texas Center for Health Disparities (TCHD), Society for the Advancement of Chicanos/Hispanics and Native Americans in Science (SACNAS), Annual Biomedical Research Conference for Minority Students (ABRCMS), STEMConnect, etc. Over the next few weeks, the social media campaign included highlights of each listening session, moderator spotlights, calls to register, a countdown to the sessions, and tips to prepare for each session. LinkedIn Boosts were also utilized to help target healthcare and community outreach initiatives to spread the word. Google Analytics was then used to assess where in the United States people were accessing the registration to determine if any regional areas needed additional promotion. This strategy resulted in 977 registrants from across the United States and territories for the six listening sessions. It was important to ensure that there was equitable distribution based on population across the country to provide as much opportunity as possible for diverse representation within these sessions.The Marketing Committee launched recruitment for the ACBC in January 2022, by sending a Save the Date to all members of the A-CC. It was asked that members of the A-CC share this Save the Date with their networks, and then share their networks with the Marketing Committee in order to initiate AIM-AHEAD’s ‘Network of Networks’ for future outreach. The Save the Date was also shared via the AIM-AHEAD Listserv, on our social media channels , and announced on During each session, there was an icebreaker, framing activity, guiding questions, and closing questions. The icebreaker was: “We invite you to share: What communities, groups, and/or organizations do you represent?” It was helpful to hear which groups the attendees represented before diving into discussion. The framing activity listed the goals of ACBC: (1) Listen to community voices to learn from their experiences; (2) Capture the AI/ML and health equity needs of individuals/communities to support capacity building; and (3) Gather ideas and direction for the activities of the AIM-AHEAD Consortium through a fair, equitable, and transparent process. Community agreements were also discussed during the framing activity, in order to reiterate that the moderators were there to listen to their needs, concerns, questions, and comments.There were eight guiding questions to cover throughout each session. To allow as many people as possible the opportunity to speak, a time limit was set for each response. However, attendees who may have been cut short were encouraged to enter their comments in the Slido chat. By evaluating responses from NIH’s previous listening session prior to funding of the A-CC, the planning committees were able to identify topics to include when developing the guiding questions. Furthermore, moderators emphasized that AIM-AHEAD will work to continue this conversation throughout the life of the consortium. Some anticipated topics of discussion included education, research, training, infrastructure, administrative support/human resources, trust, sustainability, and diversity/inclusion. The ACBC Pamphlet that was sent to participants prior to the event included background information on the constructs of AI/ML and health equity. Prompts and clarification were used as needed to guide participants in the discussion. For example, participants requested clarification from the moderators on how to participate in AIM-AHEAD, how to connect with AI/ML experts, and ways to get the community more involved with AIM-AHEAD initiatives. The eight guiding questions were developed to generate discussion around the topics, and are as follows:(1.1) Your barriers to using AI/ML;(1.2) Ways to overcome some of these barriers;(1.3) The resources you might need to address these barriersTell us about:Tell us about: Your thoughts on the importance of using AI/ML to promote health equity.(3.1) Concerns you have in using AI/ML in special or unique populations ;(3.2) How we can begin to address these concerns and/or biases;(3.3) Ways to engage these populations in AI/ML use and put the community firstTell us about:(4.1) The best strategies for addressing diversity and inclusion in the field of AI/ML;(4.2) What practicing cultural humility should look like when doing this workTell us about:(5.1) Encouraging collaboration among all types of stakeholder groups and promoting multi-disciplinary AI/ML to promote health equity;(5.2) Suggestions for building trustTell us about:Tell us about: What success for AIM-AHEAD would look like to you.Tell us about: Ensuring sustainability to help support communities, organizations, and institutions in the long term.Tell us about: How you would want to participate and engage with the AIM-AHEAD consortium.The closing questions were intended to summarize all of the discussion in the attendees’ own words and to get specific input from stakeholders about how they felt these items should be actionable moving forward. The closing questions were as follows:What 3–5 words do you feel summarize ALL of the topics discussed?What are the 2 main priorities that AIM-AHEAD should focus on?How can we ensure inclusivity of ideas and insights moving forward? These questions were curated to be open-ended, resulting in an organic generation of themes during a community-based conversation. The questions were developed for community-building discussions rather than for research purposes.Data analysts and other A-CC members were present during all of the live sessions. Afterwards, a professional third-party provider transcribed the audio to text files. All transcripts were then de-identified and stored in a secure, password-protected, and restricted-access database. Data analysis procedures included qualitative thematic analysis of voice and chat transcripts . The theQuantitative data analysis procedures included word cloud creation and aggregate counts of stakeholder type representation . A word cloud . was creFrequency counts of phrases were also used, in order to provide context to the text. The word cloud was developed by TA who identifies as a South Asian male from a biomedical informatics, education informatics, and computer science background.Out of the 977 people that registered for ACBC, 557 attended. Based on the participants who registered for specific sessions, we defined the attendees as a “community based on a common interest, namely AI/ML.” The number of participants that attended each session are as follows:Academic Session: 178 attendees Community-Based Session: 73 attendees Consortiums Session: 99 attendees General Session: 58 attendees Healthcare Session: 85 attendees Industry Session: 64 attendees (11%)https://worditout.com, also helped data analysts visualize main ideas and commonly used words/phrases. After combining the transcripts and comments from all six sessions, removing non-relevant words , and merging like terms , we found the words data, AI, communities, people, trust, research, and health equity most prevalent, demonstrating like-minded ideas across all sessions. Results of the ACBC were shared with the AIM-AHEAD Consortium through the AIM-AHEAD Newsletter and social media. However, to retain the privacy of participants, the recordings and transcripts from the sessions were not released.Key topics that emerged within each area of discussion are listed in Several sessions emphasized the importance of using existing networks for facilitating AI/ML activities. The attendees also suggested the involvement of communities on the local level such as barbershops, ministries, community health workers, and citizen scientists. As two session attendees described approaches to reaching community locally:“In many cultures, there’s a thing, the Griot, right? The Griot, the storyteller, the person that keeps the thing going. The—“train the trainer.” So there’s a lot of ways that we can and have not historically used that whole concept of training the folks who are in there on the ground so that they can then replicate and duplicate and carry on…” (Industry Session).“I think it’s really important to get in touch with your community health worker network and have these individuals who are very familiar with the area and the people and the boundaries and the cultures to get involved to assist. Because after all these—these individuals, these community health workers, know more about the culture and the aims and can get you there a lot faster. That will in turn help sustain the project.” (Academic Session).This notion of local networks will help AIM-AHEAD tap into social relationships to reach individuals and organizations at various-levels of AI/ML capacity and sustain activities. Another pattern that was identified in the data relates to global vs. local in terms of the AI/ML models themselves. Participants discussed how generic models would need to be adapted to the community for localized implementation.“So you can have an AI…that will see different outcomes with different problems that have to be put back into the algorithm in a system that is—I’ll just say, more community oriented. That has a more community focus and [is] understood by the community that you’re actually working with.” (Consortiums Session).Empowering communities to customize AI/ML tools for their specific local needs will be an important focus for AIM-AHEAD moving forward.Diversity, inclusion, and retention were also discussed in all of the sessions. Attendees stated several aspects of the AI/ML field that would benefit greatly from diversity—including (but not limited to) data, workforce, training, and research.“And the thing we have to be careful about is not homogenizing the group and making sure we don’t try to do that. I remember very explicitly [stakeholder group] said to me, "We can’t lose our cultural identity. We can’t lose the way we look at things just because you want to help all of us." Very explicit. And so we have to be very sensitive about that because we’re trying to help everybody…So we really have to listen and try not to homogenize it because we’ll lose them and we might not even realize we’re losing them. They may be gracious about it and we won’t even know.” .“I’m just speaking for myself. We don’t want people to feel like they were just brought in to be representative, right? We actually want them to be here, right, working with us and trying to figure out how to take the next steps.” .Engagement techniques will need to be tailored to each individual stakeholder group’s needs, homogenizing within or across groups should be avoided, and cultural identities should be held to the utmost respect.ACBC attendees felt there was not enough information readily available that clearly articulated how AI/ML can be used in everyday life. ACBC attendees communicated that AI/ML needs to be demystified, with hopes that “AI/ML” would become a household term. The attendees also pointed out the need for ongoing points of engagement through future listening sessions, social media, and other tailored community-centric approaches.“Let’s engage our neighbors. Let’s have them part of the conversation because again, this is frightening to them, artificial intelligence. Let’s get that out in the open and demystify this. That means that we have to demystify and be humble and talk plain language to the very communities that are suffering.” (Academic Session).“Sci-fi can make AI seem deterministic or flawed or whatever. And if that’s the way that most people perceive AI or, or consume information about AI, it doesn’t really help our case. So you have to sort of humanize the process of creating algorithms and models and saying, okay, they’re it… They’re influx, they can change, they can be improved and we’re not always listening to like our robot overlords for, for all advice.” .There should be standards for language and terminology in the field of AI/ML to accommodate collaboration across disciplines. Providing context about how algorithms can be improved allows for a greater understanding and reduces fear of the unknown about the topic.ACBC attendees listed barriers they have personally faced. These barriers included general lack of awareness, limited access to data/infrastructure/training, and insufficient support systems around AI/ML. However, the attendees also provided potential solutions to overcome these barriers. Some areas for improvement included: collaboration, equitable distribution of resources, providing education and training, creating a standardized repository to foster data sharing across disciplines, addressing bias in data and algorithms, acknowledging historical and social context, and applying ethical considerations.“What we are seeing right now is, the biggest barrier, what we are seeing is access and opportunity. And how we can overcome this is really exposure and training so people are aware of really what artificial intelligence and machine learning is. And once we’re able to get to that point, then they can trust certain systems.” (Community-Based Session).“What I think the biggest challenge I’ve had… barrier to using machine learning and AI is having adequate representative data sets to test and train your algorithms on. If there was a way that we could work with some of the organizations like NIH to create these test and training data sets for medical knowledge that were de-identified, that would really help with the development of these technologies.” (Academic Session).“I think a real impact will be made when we can get people that are non-STEM background to not look at AI and ML as “that other science,” but see it as something that they can do themselves and then they will apply it to their appropriate disciplines in ways that a STEM person probably would never think of.” (Academic Session).By gaining greater insight into the needs of stakeholders, AIM-AHEAD is able to better gear programs and interventions to target population interests. Additionally, having those groups discuss proposed solutions opens the dialogue for shared decision making and creates a sense of comradery for overcoming barriers together.Discussions also centered around making AI/ML more transparent, accessible, and explainable—dismantling the “black box.” In using AI/ML to promote health equity, there needs to be a balance of access to data while protecting privacy. Therefore, an open science platform could be used. Aspects of privacy and security should be emphasized when creating these tools, especially when incorporating EHR and SDoH data into a dedicated space for collaboration. Additionally, a platform such as this could be beneficial in disseminating information back to the community from where it came.“I always think about what are going to be the financial impacts, particularly on pharmaceutical companies and the like. I think about Henrietta Lacks, the drugs and the profits that were made from that. I kind of think about, well, there’s going to be profit even in healthcare. How is that somehow shared in some way, shape, or form with those who are contributing the data.” (Academic Session).“So it’s leveraging AI/ML to promote health equity…it’s understanding what data are you using, how you’re using it, what algorithms you’re using, how often you look at them, what decisions you’re making and being very open and honest and transparent about it, and actually testing yourself.” ."One of the other areas that I’m thinking about because I’m coming from the medical community is that our medical platforms don’t always communicate with each other. So if you are using one electronic record, someone else may be using a different format and those systems do not communicate. If you’re thinking of transferring patient information and utilizing that, you need to have it capable of communicating across various platforms. Otherwise it becomes frustrating." .Part of what is needed to make AI/ML an effective tool to address health disparities is a multi-disciplinary approach. However, collaboration amongst different groups will require platforms to share data, AI/ML approaches, and results. An open science platform could provide a space to work together in a transparent and equitable way.Attendees listed several ideas that they felt would contribute to the overall success of AIM-AHEAD. AIM-AHEAD should have stakeholders involved in every step of the process of building the consortium, which will in turn lead to buy-in from communities. Capacity building through education, training, mentoring, and supporting a diverse pipeline of AI/ML researchers/workforce were also considered markers of success. Many attendees were interested in engaging with the AIM-AHEAD Consortium after ACBC.“I think success in my estimation would be you have readily accessible, interoperable, high quality system that it doesn’t matter whether you’re in Dallas or California or New York or Mississippi, the level and quality of the services that you’re providing through AI/ML is of the same level. And it doesn’t matter whether you have $2 billion or you have $2, you still have the same access and use of that information and your health outcomes are going to be high quality, mortality rates going to be down.” .Participants shared many ways in which they felt AIM-AHEAD could improve reach, collaborate with existing data science programs, and overcome current challenges in the field of AI/ML and health equity. Engaging stakeholders in refining visionary ideals of success and setting program goals also increases sustainability of the initiative.For social, behavioral, and biomedical fields to benefit from advanced technologies such as artificial intelligence and machine learning, it is important to first understand the needs of those receiving and providing care before moving forward with any advancement or change ,5,6. ThiThe thematic analysis also revealed recommendations that emerged from the listening sessions. These included promoting awareness of AI/ML through podcasts, webinars, inclusion of diverse stakeholders, clarification of the goals of A-CC, achieving equity in entire ecosystem of A/ML such as: data quality, identifying and using diverse sources of data, training algorithms to recognize the diversity, and developing/deploying AI/ML models through the lens of health equity. The listening sessions also reinforced the need for integrating cultural humility into all programs and activities of the A-CC. The focus sessions confirmed some of the existing barriers to cross-collaboration among health sciences such as bridging the gap in cross-domain knowledge, lack of capacity in under-represented institutions. Moreover, new knowledge emerged because existing knowledge typically comes from white papers and reports from non-academic work such as Harvard Business Review. Scholarly literature in this area is limited and studies have focused on patients and not the larger public from diverse communities . Thus, nhttps://aim-ahead.net/convention/p/aihes). As part of AIHES, to expand capacity in health equity and AI/ML, free training opportunities were provided in July 2022 through a month-long series of workshops covering a wide range of health equity and AI/ML topics.Key messages from the listening sections will guide the A-CC in how to engage with organizations/institutions/communities, develop use cases, and communicate AI/ML terminology and success stories. The shared perspectives and experiences of the community members have also guided the implementation of first year activities at an accelerated pace and planning of future programming/initiatives. For example, efforts were directed toward a smaller, more targeted scale in order to dive deeper into ideas for carrying out tailored approaches for local communities. In response to the call for increasing awareness on AI/ML, a two-day free virtual symposium on AI/ML and health equity was held titled “AI for Health Equity Symposium” (AIHES) (https://aim-ahead.net/ResearchFellows) and a Leadership Fellowship (https://aim-ahead.net/fellows/LeadershipFellows) with a significant focus on diversity and inclusion. Applicants from diverse backgrounds were encouraged to apply, and the first cohorts for both fellowships proudly represent a diverse pool of individuals.Diversity in capacity-building efforts and projects is also a main focus of the A-CC. As such, AIM-AHEAD started a Research Fellowship . Some limitations of information gathered in the listening sessions include selection bias and social desirability bias . First, AI/ML still remains a mystery to many, especially when it is used in healthcare settings, which can lead to mistrust. Equitable partnerships are needed to overcome this barrier. AIM-AHEAD is developing and growing a national consortium of patients, researchers, other individuals, organizations, institutions, and communities to address challenges in AI/ML and health equity. Potential target audiences to collaborate with when developing AI/ML tools for health equity will likely depend on the specific context of local communities. Therefore, it will be important for AIM-AHEAD to continue to draw upon the strengths of the local hubs to reach key stakeholders from different disciplines, demographic areas, and regions. The aim of ACBC was to connect with AIM-AHEAD stakeholders/partners and continue the conversation NIH first initiated prior to funding the program to assess data, infrastructure, and training needs. The listening sessions indeed provided rich insight into the AI/ML needs around these topics. AIM-AHEAD was able to contribute to immediate outcomes and share these reflections with the A-CC, communities who participated, and NIH networks. The findings of ACBC are relevant to a wider audience of those in the field of AI/ML, data science consortiums, and healthcare as they shed light on areas to make implementation of these tools more sustainable and resilient. AIM-AHEAD will use these findings to contribute to practice both internally and externally. Furthermore, it is important to create a space where stakeholders from various disciplines can share concerns, discuss ideas, and foster innovation. As such, AIM-AHEAD will continue to engage with stakeholders through a variety of mediums in order to promote an open dialogue around AI/ML and health equity.S1 Fighttp://bit.ly/3V5jqogThis word cloud was derived from all six listening sessions and depicts the most prominent themes across all sessions. The largest word, “data” in blue, reveals that this was the most common word spoken, followed by “AI,” “communities,” “people,” “need,” and so on as the words decrease in size. (PNG)Click here for additional data file.


Graphene is widely used in tunable photonic devices due to its numerous exotic and exceptional properties that are not found in conventional materials, such as high electron mobility, ultra-thin width, ease of integration and good tunability. In this paper, we propose a terahertz metamaterial absorber that is based on patterned graphene, which consists of stacked graphene disk layers, open ring graphene pattern layers and metal bottom layers, all separated by insulating dielectric layers. Simulation results showed that the designed absorber achieved almost perfect broadband absorption at 0.53–1.50 THz and exhibited polarization-insensitive and angle-insensitive characteristics. In addition, the absorption characteristics of the absorber can be adjusted by changing the Fermi energy of graphene and the geometrical parameters of the structure. The above results indicate that the designed absorber can be applied to photodetectors, photosensors and optoelectronic devices. With the advancement of terahertz technology, terahertz metamaterial absorbers exhibit significant application values and development prospects in fields such as sensing, imaging, filtering, electromagnetics, absorption, detection and so on ,2,3,4,5.To address the above challenges, researchers in this field turned to graphene , a remarIn this paper, we propose a broadband absorber that is polarization independent and angle insensitive. It is composed of disc graphene, open ring graphene, two insulating dielectric layers and a metal base substrate. According to the simulation results, it achieves over 90% absorption in the 0.53–1.50 THz range. Within the range of 0.59–1.38 THz, the absorption rate exceeds 95%. The mechanism of perfect absorption is revealed through the analysis of the electric field and current distribution diagrams. The effect of different incidence conditions is also explored, and the results reveal that the designed absorber is insensitive to polarization and angles. The absorption can also be enhanced efficiently by adjusting the Fermi energy and dimension of graphene. In addition, a comparison with published graphene absorbers has been conducted to provide valuable insights for the design and optimization of future absorbers.The schematic diagram and geometric parameters of the double-layer graphene absorber proposed in this paper are shown in ne layer b, a polyent wave . Assuminent wave . For theFor the top layer of graphene, a disc shape was deliberately selected to provide uniform properties when external waves come in from every direction, guaranteeing uniformity within the interior of the structure. For the middle layer of graphene, the open ring shape was chosen to better generate resonance and accomplish perfect absorption. The scheme of applying multiple graphene layers alternating with dielectric layers can better bind the electromagnetic field energy of the incident waves within the structure.In this study, graphene is regarded as a conductive surface. The thickness of graphene tg=1 nm , and thetg=1 nm ,28:(1)σwIn the formula, ·V−1·s−1 . The graessed as ,30:(2)σwIn this study, the absorptivity A can be expressed as We used CST Microwave Studio 2019, commercial software for numerically simulating high-frequency electromagnetic fields. Specifically, we adopted the frequency domain solver module to set the structure with periodic boundary conditions in the x and y directions and open boundary conditions in the z direction. The incident wave was vertically incident from the upper surface of the absorber, and the entire computational field was discretized using a user-controlled tetrahedral grid. The Fermi energy of graphene is As illustrated in To investigate the influence of the double-layer patterned graphene layer on absorption characteristics, we maintained other material and structural parameters unchanged and modified the opening width of the open ring graphene layer and the radius of the disk graphene, respectively. The Fermi energy of graphene was set to 0.8 eV, and simulation frequency ranged from 0.2 THz to 2 THz. The resulting changes are illustrated in The width of the open ring graphene was adjusted to 2 μm, 4 μm, 6 μm and 8 μm, as shown in The above phenomenon can be well explained using impedance matching theory. The absorber is regarded as an integral structure, and as previously mentioned Furthermore, we investigated the number of openings in the ring structure, as shown in To explore the dynamic tunable properties of the designed absorber, we changed the Fermi energy of graphene To better investigate the absorption mechanism of the proposed absorber, we also simulated the electric field distribution of incident waves. When the Fermi energy level surpassed 0.8, the top disc graphene exhibited metal-like properties. As a result, when significant amounts of incident electromagnetic waves passed through the top graphene, part of them were reflected, which led to fewer electromagnetic waves entering the interior of the structure. Such a reduction inevitably causes a decrease in the absorption performance. Consequently, when designing graphene absorbers, it is not always optimal to increase the Fermi energy.From our study, it can be concluded that two key factors need to be optimized for perfect absorption. First, it is crucial to ensure that the incident electromagnetic waves are maximized into the interior of the structure. Second, it is necessary to adjust the structural parameters for generating stronger resonances and enhancing the absorption of incident electromagnetic waves within the structure. The above findings provide informative inspiration for the future of absorbers design.In practical applications, a critical factor to be considered is the polarization sensitivity of the absorber. As depicted in The above discussion is based on the case where the electromagnetic wave is incident vertically. In practical applications, it is difficult to incident vertically all the time. To verify that the designed absorber had the absorption performance of wide angle incidence, the angle of inclined incidence, angle As the angle of incidence increased gradually in the range of 0° to 50°, the absorption performance showed outstanding performance. Once the angle of incidence increased to 60°, it could be observed that the absorption performance decreased. The potential reason was the reduction in the electric field component parallel to the absorber surface, which led to a weaker interaction between the incident wave and the graphene. Nevertheless, the overall absorption remained above 80%. In conclusion, the proposed absorber effectively demonstrated a remarkable insensitivity to wide incidence angles.In our comparison with previously published graphene absorbers, we evaluated the three crucial performance criteria of broadband absorption, incidence angle insensitivity and polarization angle insensitivity. While most previous studies excelled in only one of the three criteria, our designed absorber exhibits a promising performance in all three areas. This critical observation indicates that the designed absorber is more versatile and adaptable for wider applications and scenarios. As a result, our absorber provides a valuable reference for future absorbers’ design.In summary, this paper proposes a novel idea for designing wide-angle incidence insensitive absorbers through the utilization of double-layer patterned graphene. Simulation results demonstrated that this absorber achieved exceptional absorption performance independent of polarization angles. Additionally, the study highlights that the absorber’s performance could be significantly improved by adjusting both the Fermi energy and geometric parameters. Consequently, the designed absorber is remarkable as it is characterized by dynamic tunability, multilayer structural stacking and broadband absorption capability, making it a valuable reference for the design and fabrication of future absorbers.


In cases with advanced glaucomatous disc changes, further changes associated with other optic neuropathies cannot be easily identified. We present a case of preexisting open-angle glaucoma and concurrent involvement of sarcoidosis-associated optic neuropathy.A 53-year-old man presented with gradual visual loss in his left eye, which began 1 year ago and accelerated 3 months ago. The best-corrected visual acuity in the right eye was 20/20 and counting fingers in the left. Intraocular pressures (IOP) were 12 mmHg in the right eye and 34 mmHg in the left. We diagnosed him with advanced open-angle glaucoma in the left eye based on the advanced glaucomatous cupping of the left optic disc. The IOP in the left eye dropped to 10 mmHg and was well controlled with antiglaucomatous medication; however, his left optic disc developed pallor 3 months after the treatment. The patient was revealed to be diagnosed with sarcoidosis a month ago and had been treated with systemic corticosteroids thereafter by a pulmonologist. Orbital magnetic resonance imaging revealed sarcoidosis-associated optic neuropathy in the left eye. Subsequently, optic neuropathy occurred in his right eye.In eyes with advanced glaucomatous disc change, detecting the coexistence of other optic neuropathies can be difficult. This report highlights the importance of careful ophthalmic examinations and investigation for etiologies of other optic neuropathies if non-glaucomatous changes are suspected even in eyes with advanced glaucoma. Sarcoidosis is a systemic granulomatous inflammatory disease characterized by the formation of noncaseating granulomas in the affected organs , 2. AlthOcular or neurologic involvement of sarcoidosis should be suspected in patients with systemic sarcoidosis who develop visual loss , 8. HoweA 53-year-old man presented with gradually deteriorating vision in his left eye that began one year prior and accelerated over the last three months. At the time vision deterioration three months ago, the patient complained of periorbital pressure on his left upper eyelid. His best-corrected visual acuity (BCVA) in the right eye was 20/20, and counting fingers in the left. Intraocular pressures (IOP) measured by Goldmann applanation tonometry in the right and left eyes were 12 and 34 mmHg, respectively. Anterior segment examinations showed no abnormalities other than mild nuclear cataract in both eyes, and the gonioscopic examination using four-mirror gonioscopy lens showed an open angle with clearly visible angular structures. The right optic disc had an intact neuroretinal rim  associated with sarcoidosis-associated optic neuropathy, distinctive of pre-existing glaucomatous cupping and neuroretinal rim thinning. Moreover, the sequential and progressive nature of the bilateral involvement in this case of sarcoidosis-associated optic neuropathy highlights the importance of careful ocular examinations during the course of disease.Sarcoidosis can affect any segment of the eye and orbital structures . Ocular Sarcoidosis of the nervous system (neurosarcoidosis) occurs in 5–16% of patients with sarcoidosis and involves cranial nerves, anterior visual pathway, and pituitary-hypothalamic regions , 10, 11.Neurosarcoidosis is diagnosed based on the clinical manifestations and diagnostic evaluation, which includes MRI, cerebrospinal fluid, and electromyogram or nerve conduction study findings that are typical of granulomatous inflammation of the nervous system, as well as the rigorous exclusion of other causes . The diaThe most common MRI findings of sarcoidosis-associated optic neuropathy is diffuse enlargement of the optic nerve (granulomatous infiltration) as well as thickening and enhancement of the optic nerve dura , 7. ExteTreatment of ocular or neuro-ophthalmic manifestations of sarcoidosis remains empiric, mainly with corticosteroids initially followed by steroid-sparing immunosuppressants , 13. MetSystemic corticosteroid for sarcoidosis treatment can cause steroid-induced IOP elevation or glaucoma based on the dose, frequency, and duration of corticosteroid use . TherefoIn eyes with advanced glaucomatous damage, other optic neuropathies can be easily missed. However, non-glaucomatous changes are distinctive even in eyes with a preexisting advanced glaucomatous disc change. In such cases, careful ophthalmic examinations as well as thorough investigation for underlying systemic etiologies should be considered. Moreover, since sarcoidosis-associated optic neuropathy can affect both eyes, regular follow-ups with bilateral examinations may be necessary.


The diaphragm function assessed by ultrasound has been well-studied in COPD, asthma, and intensive care. However, there are only a few studies on diffuse interstitial lung disease, while dyspnea and quality of life are major issues in the management that may depend on the diaphragm.We retrospectively included idiopathic pulmonary fibrosis (IPF) patients followed in our center  between January 2020 and February 2023 who underwent diaphragmatic ultrasound. Our objectives were to describe the diaphragmatic function of IPFs compared to healthy controls and to correlate with clinical, functional, and lung density on CT-scan.24 IPF patients and 157 controls were included. The diaphragmatic amplitude in IPF was increased at rest  and decreased in deep breathing . Diaphragmatic thickness was significantly reduced at rest on the right side  and in deep breathing on both sides compared to controls . Diaphragmatic amplitude in deep breathing was moderate to strongly correlated with FVC, DLCO, and 6MWT and negatively correlated with the dyspnea and lung density on CT scan.The diaphragmatic amplitude and thickness were impaired in IPF compared to controls. Diaphragmatic amplitude is the parameter best correlated with clinical, functional, and lung density criteria. Further studies are needed to determine if diaphragmatic amplitude can be a prognostic factor in IPF. In interstitial lung disease (ILD), dyspnea and quality of life are major issues in patient care –3. SeverDifferent methods are used to assess diaphragmatic function including imaging by fluoroscopy, computed tomography to assess both the structure and function of the diaphragmatic muscle or the measurement of trans-diaphragmatic pressures, stimulation of the phrenic nerve, and electromyography exclusively evaluating neuromuscular function. However, all these methods have interpretation limits, and sometimes technical constraints related to their invasive nature –12. ThusThe measurement of diaphragmatic function by ultrasound has already been studied in many lung diseases such as asthma , COPD 2–22, cystThis study aimed to describe the diaphragmatic structure and function by ultrasound in a homogeneous population of IPF and to compare them with healthy subjects.We conducted a retrospective monocentric study at the North University Hospital of Marseille, France. All patients with IPF consecutively evaluated between December 2020 and February 2023 for lung transplantation in the Department of respiratory medicine were included. Patients benefited from a systematic evaluation of the diaphragmatic function by ultrasound in the pulmonary function test laboratory in the pre-transplant assessment.Participants had a diagnosis of IPF based on clinical, biological, functional, CT scan, and possibly histological criteria accordingly to the 2018 ATS/ERJ criteria . PatientHealthy subjects from a previously published study by Boussugues et al.  that desThe Institutional Review Board of the French learned society for respiratory medicine-Société de Pneumologie de Langue Française—approved the protocol (CEPRO 2022-033bis), and a notice of information and non-objection was given to all participants according to French law.For all the subjects, we collected the following data carried out in clinical routine, the closest to the diaphragmatic evaluation: age, sex, BMI, smoking status, dyspnea according to the mMRC  scale, 6-min walk test (6MWT), oxygen supplementation, co-morbidities, treatments used such as corticosteroids or immunosuppressants. All participants benefited from spirometry, plethysmography, and diffusion analysis when available .The diaphragm ultrasound measurements were performed with the patient in a seated position, on the right and the left side, from the same ultrasound device . A single experienced operator (AB) performed all the measurements (IPF and controls). To strengthen the accuracy of the results, all measurements were averaged from at least three different breathing cycles.Measurement of the diaphragmatic excursion (or amplitude) was carried out using a cardiac probe placed between the midclavicular and anterior axillary lines, in the subcostal area, and directed medially, cranially, and dorsally, so that the ultrasound beam reached perpendicularly the posterior third of the right hemidiaphragm. On the left side, a subcostal or low intercostal probe position was chosen between the anterior and mid-axillary lines to obtain the best imaging of the hemidiaphragmatic dome. Diaphragm movements were recorded in M-mode. Ultrasonographic measurements were performed during quiet breathing (QB), deep breathing (DB), and voluntary sniffing (VS). This maneuver began at the end of normal expiration, and the subjects were asked to breathe in as deeply as they possibly could.Diaphragmatic thickness was measured using a high-frequency linear 9L probe placed on the diaphragm insertion on the rib cage between the anterior axillary and mid-axillary lines, according to a previously published method . Measurehttps://www.slicer.org). From the parenchymal window, the lungs were segmented and reconstructed in 3D by selecting the Hounsfield unit (HU) from − 1024 to − 350. The trachea and main bronchi were excluded. The densitometric analysis consisted in quantifying the voxels on the whole lungs by HU. The threshold of -600 HU was used to calculate fibrosis . The voxel quantification histogram by HU was extracted and the curve flattening coefficient (Kurtosis) and the asymmetry coefficient (Skewness) were calculated (because linked to the quantity of voxel with HU between − 600 and − 350 and therefore to fibrosis) [We analyzed thoracic CT scans closest to the diaphragmatic ultrasound evaluation in IPF patients exclusively. All thoracic CTs were performed according to the following parameters: 120 kV and 1 mAs/kg with care dose modulation and reconstruction in joint slices of 1:1 mm. Doses were adjusted manually according to the patient template: 100 kV if they weighed < 60 kg, if above 120 kV. The thoracic CT scans were acquired during breath-hold inspiration from the adrenal glands to the neck and at the end of forced expiratory flow. The total lung volume was recorded from the CT inspiratory volume using the post-treatment station, Thoracic VCAR . CT scans were performed on various systems . Analyses of the lung parenchyma density were performed using dedicated 3D analysis software  .A descriptive analysis was performed on the IPF group and the healthy controls. Continuous variables are expressed in median and interquartile or mean and standard deviation, depending on the distribution (Shapiro–Wilk test), and qualitative variables are expressed in numbers and percentages. Analyzes were performed to compare patient characteristics and ultrasound measurements between groups. Qualitative parameters were compared using Chi-square tests. Quantitative parameters were compared using a student’s test or a Mann–Whitney-Wilcoxon non-parametric test depending on the distribution. Correlation tests between the diaphragmatic measurements, the clinical and functional pulmonary data, and the CT scan measurements were performed by the Pearson method.https://www.R-project.org/).All tests are two-sided. A p < 0.05 was considered significant. The analysis was performed using version 4.2.1 (2022-06-23) of the R software (R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL .During the study period, 24 IPF patients met the inclusion criteria and were compared to 157 control patients. The patient and control characteristics are in Table 1Measurements of the diaphragmatic structure and function by ultrasound of IPF patients and controls are in Table The results are depicted in Fig. The analyses of the lung parenchyma density in IPF patients are presented in Table This study demonstrates that diaphragmatic function assessed by ultrasound in IPF patients showed significant differences compared to healthy controls. First, we found that diaphragmatic amplitude at rest in IPF was significantly increased compared to controls. This finding might be due to diaphragmatic compensation, indicating the need for greater muscular work to maintain the same degree of hematosis. Such muscle work at rest can lead to a decrease in reserve strength and faster dyspnea. Second, a decrease in the maximal diaphragmatic excursion, which is consistent with available evidence-based medicine , 31, 32,The diaphragm amplitude in DB was in our study well correlated with clinical features: positive correlation with FVC, DLCO, 6MWT, and negative correlation with dyspnea. Previous studies found also that amplitude was strongly and positively correlated with FVC and DLCO, which are strong predictive factors of mortality in IPF and ILDs , 28, 31.Finally, like the other works studying diaphragm in IPF or ILD , 28, 31,Regarding lung density assessed by CT, we found a negative correlation between the proportion of voxels greater than -600HU (as a surrogate of lung fibrosis) and the diaphragmatic amplitude in DB. This highlights the relationship between thoraco-pulmonary compliance and a mechanical constraint on the diaphragm in IPF patients. Kismet et al.  also anaThis study has several strengths. Our cohort of IPF patients was compared to a large cohort of healthy controls used to define the normal value of diaphragm measurements by ultrasound . For tecSeveral limitations should nevertheless be noted. This is a monocentric study including a small number of IPF patients. Due to the retrospective nature of the study, data to quantify the patient's peripheral muscle mass were not available to assess the extent of the global muscle weakness. However, the cohort of IPF patients in this study had normal blood albumin levels, BMI around 25, and was not strongly deconditioned (median 6MWT at 524 m). We, thus, may hypothesize that patients had no strong sarcopenia. And last, we had no access to quality-of-life data, an essential element to characterize the impact of the disease in IPF patients –3.Our study shows that diaphragmatic amplitudes in QB and DB are altered in IPF compared to controls probably because of the change of the thoraco-pulmonary compliance responsible for a mechanical constraint on the diaphragm. Moreover, predictors of mortality such as FVC and DLCO, clinical outcomes such as 6MWT and dyspnea, and lung density are well correlated with the diaphragmatic amplitude in DB. Further studies are needed to know if the diaphragmatic amplitude could be a prognostic factor in IPF and is associated with exacerbations, hospitalizations, or mortality.


Arabidopsis thaliana (Arabidopsis), Gossypium raimondii (cotton), Zea mays (maize), Brassica napus (oilseed rape) and Oryza sativa Japonica Group (rice). In total, we found 100 126 introns that host functional elements in these five species. Specifically, we found that among all species, the number of introns with functional elements on the positive and negative strands is similar, with a length mostly smaller than 1500 bp, and the Adenine/Thymine (A/T) content is much higher than that of Guanine/Cytosine (G/C). In addition, the distribution of functional elements in introns varies among different species. All the above data can be downloaded for free in this database. This database provides a concise, comprehensive and user-friendly web interface, allowing users to easily retrieve target data based on their needs, using relevant organizational options. The database operation is simple and convenient, aiming to provide strong data support for researchers in related fields to study plant introns that host functional elements, including circular RNAs, lncRNAs, etc.Although more and more attention has been focused on introns and the important role of plant introns in plant growth and development has been discovered, there is still a lack of an open and comprehensive database on plant introns with functional elements in current research. In order to make full use of large-scale sequencing data and help researchers in related fields to achieve high-throughput functional verification of identified plant introns with functional elements, we designed a database containing five plant species, PlantIntronDB and systematically analyzed 358, 59, 185, 210 and 141 RNA-seq samples from Database URL:  http://deepbiology.cn/PlantIntronDB/ Introns are non-coding nucleotide sequences that do not appear in mature mRNAs . CentralXenopus oocytes  for related RNA-seq samples on 8 July 2022. We used advanced search terms, including ‘RNA-seq’ and ‘Illumina’, and downloaded RNA-seq data for each species. Then, we used the SRAToolkit v3.0.2, Hisat2 v2.2.1 (http://deepbiology.cn/PlantIntronDB/).We selected five plant species, including 2 v2.2.1 , and othArabidopsis thaliana (Arabidopsis) (TAIR10), Gossypium raimondii (cotton) (Graimondii2_0_v6), Zea mays (maize) (Zm-B73-REFERENCE-NAM-5.0), Brassica napus (oilseed rape) (AST_PRJEB5043_V1) and Oryza sativa Japonica Group (rice) (IRGSP-1.0) by using Hisat2. After alignment, we used Samtools v1.9 . We used SRAToolkit, Hisat2 and other software packages to analyze the above RNA-seq samples. We removed files that did not generate valid sequence data during the analysis pipeline through data cleaning and only retained files that met the standards for analysis  gene ID. This ID integrates the higher-level ID to which the feature is subordinate, which helps to associate introns hosting functional elements with genes, and it enables users to compare and search different species of introns; (ii) intron-specific information for each species, including intron number, which refers to the position of the intron, chromosome, start position, end position, positive and negative strand, sequence length and sample number. The sample number is how many samples have been analyzed from all samples to contain introns with functional elements. At present, this database mainly supports the following functions: (i) intron information browsing: users can browse the specific attributes of all data in the database to understand its basic information, providing convenience for subsequent research; (ii) intron search: users can quickly locate an intron of interest by entering information such as gene ID, intron number, chromosome, start, end, width, strand and species; (iii) intron download: users can obtain complete information about all introns through the download function.The web interface of PlantIntronDB consists of seven pages: Home, User Guide, Browse, Search, Download, Publication and Team . Home inArabidopsis, cotton, maize, oilseed rape and rice. It has detailed information about introns hosting functional elements, including gene ID, chromosome number, intron number, strand, start and end position and sample number. In addition, the platform also provides a search function to facilitate users to find the search target quickly. Finally, it also provides a free download function, which allows users to download by clicking the links of corresponding species. By using this database, users can not only grasp the rich details of plant introns hosting functional elements but also track these introns’ resources and reveal their novel functions and regulatory roles. We believe that this database will help the community to have a deeper understanding of plant introns with functional elements.PlantIntronDB provides a complete and comprehensive platform for plant introns, including


The modified filler with 5% OMMT/PA6 at 328 K still had the strongest barrier performance. When the pressure increased, the permeability coefficient of the modified material first decreased and then increased. In addition to this, the effect of the fractional free volume on the barrier properties of the materials was also investigated. This study provides a basis and reference for the selection and preparation of polymer linings for high-barrier hydrogen storage cylinders.In order to investigate the type IV hydrogen storage bottle with better hydrogen storage capacity, the polymer lining of the hydrogen storage bottle was further developed. In this paper, the molecular dynamics method was used to simulate the helium adsorption and diffusion processes within a modified montmorillonite (OMMT)-filled polyamide 6 (PA6) system. The effects of the barrier properties of the composites were investigated at different filler contents , different temperatures (288 K and 328 K) and different pressures  for certain contents. It was found that when the filler content was 5%, the permeability coefficient of the material was lower than 2 × 10 Nowadays, the production and application of hydrogen have quite mature technology, but the storage of hydrogen is the main difficulty that restricts the application and promotion of hydrogen energy ,2,3. HigHumpenoder  studied With the rise of nanocomposites, scholars have attempted to prepare nanocomposites by incorporating inorganic nano-fillers into polymer matrices . The incIn this paper, a molecular model of nanocomposites with polyamide 6 as the polymer substrate and modified montmorillonite as the nano-filler was constructed by means of a molecular dynamics simulation. Due to the high difficulty factor of hydrogen experiments and tests, simulations with helium instead of hydrogen can more simply reflect the gas barrier properties of the materials. By studying the diffusion coefficient of helium molecules in the polymer composite model and investigating the effect of different filler ratios on the gas permeation of the nanocomposites under different temperature and pressure conditions, better barrier performance materials were obtained.+. Since MMT is hydrophilic, the surface modification of the six interlayer cations of MMT was replaced with octadecyl trimethylammonium chloride for better compatibility with the polyamide materials. The obtained model is shown in Montmorillonite is an aluminosilicate mineral with two silica-oxygen tetrahedral layers interspersed with an aluminum-oxygen octahedral layer, which has a strong ion-exchange capacity . MMT celOne hundred repeating units were established to form random single chains of PA6 molecules , and the4. Next, the composite system was cyclically annealed using Anneal. For a more rational molecular structure, the annealing cycle was set to 10 times with an initial temperature of 300 K, and the mid-point of each annealing cycle was 500 K . Molecular dynamics (MD) was then used to extract the optimized model with the minimum energy for kinetic simulation. The thermodynamic ensemble of NPT was selected under the UFF force field, and the kinetic optimization was performed for 1000 ps at 298 K and 0.1 MPa. The Andersen heat bath method was selected for temperature control and the Berenson pressure bath method for pressure control.In order to achieve the most stable initial state of the established model, the model was first structurally optimized and energy-minimized by the geometric optimization method with a maximum number of iterative steps of 5 × 10The solubility coefficients of helium were obtained using adsorption isotherms at pressure variations ranging from 0.01 kPa to 10,000 kPa. The working condition of a type IV hydrogen storage tank is at 52 MPa with a safety pressure of the storage tank of 60 MPa. The operating pressure of the IV hydrogen storage tank is generally 41.6 MPa, which is 0.8 times the working pressure. Therefore, four different pressures  were simulated by using sorption. In order to obtain the permeation coefficient, helium permeation was performed using fixed pressure on the configuration. Then, an NPT dynamics simulation of 500 ps under NPT for a system containing helium molecules was conducted, and a schematic diagram of the unit cell model is shown in The Universal force field (UFF)  was usedb is the Langmuir parameter, P is the pressure and C is the adsorption of gas molecules.The solubility coefficients of gas molecules can be obtained by fitting isothermal adsorption curves to obtain adsorption equations and then performing concentration conversion :(1)C=kDPD is the gas diffusion coefficient, N is the number of helium atoms, i helium atom at moment t and moment 0, respectively, and The diffusion coefficient of a gas molecule is an equation relating velocity and time, and the helium diffusion coefficient can be calculated using the following equation ,38:(3)D=P is the permeability coefficient and D is the gas diffusion coefficient.The permeability of gas molecules in the composite model can be characterized by calculating the solubility and diffusion coefficients of gas molecules:In order to obtain the fractional free volume (FFV) of the system, the system model was analyzed using the Atom Volumes and Surface tool. The Connolly radius was set to 1.40 Å based on the known van der Waals radius of the helium atom, and the Connolly surface of the system was calculated to obtain the FFV of the system.The results obtained from the simulations are shown in t) is shown in t = 220 ps, and the diffusion reaches a stable state.From the time ,40. The −13 cm3∙cm/(cm2∙s∙Pa), indicating that this system has the best gas barrier. Compared with the material with 3% OMMT content, the 5% OMMT content has approximately 60% lower solubility, 15% lower diffusion coefficient and 60% lower permeability coefficient. When raising the OMMT content to 6%, both solubility and diffusion coefficients increased slightly, and the permeability coefficient increased by about 30% compared to the system containing 5% OMMT. The solubility and diffusion coefficients continued to increase when the addition of OMMT reached 7%. The reason for these results may be that the addition of a small amount of OMMT breaks the channels for the diffusion of gas molecules in the PA6 system and increases the degree of complexity of the molecular diffusion channels. Due to the irregular spatial distribution of the PA6 molecular chains and montmorillonite molecules in the model, the increased channel complexity impedes the diffusive movement of the gas, resulting in a lack of sufficient energy for further movement of the gas within the system; therefore, the penetration of the gas molecules is limited. However, the addition of too much OMMT filler leads to more molecular diffusion channels in the system, so the permeation coefficient of the composite increases slightly. From this, it can be found that the addition of 5% OMMT content to PA6 is the most suitable in order to obtain the best barrier material.The MSD curve was fitted to find the diffusion coefficient from Equation (3). The solubility coefficients, diffusion coefficients and permeability coefficients of the model calculated from the equations are shown in t) is shown in t = 200 ps, and the diffusion reaches a stable state. The increase in temperature leads to a decrease in the time of the normal diffusion.−12 cm3∙cm/(cm2∙s∙Pa) at 328 K compared to 288 K. The permeability coefficient of 4% OMMT/PA6 increased approximately 1.7 times, while the permeability coefficients of 5% OMMT/PA and 6% OMMT/PA increased by a factor of approximately 1.5. The permeability coefficient of 7% presented the highest increase in the system. It can be seen that temperature has a great influence on the permeability of the material, and there have been many studies that have demonstrated this phenomenon [−12 cm3∙cm/(cm2∙s∙Pa) except for the addition of 3% OMMT, which again indicates that the barrier properties of the materials were greatly improved by the addition of a certain content of OMMT. The diffusion and permeability coefficients of OMMT/PA6 increase with an increase in temperature because the thermal motion of helium molecules increases and the intermolecular collisions become stronger due to the increase in temperature.The solubility and diffusion coefficients at different temperatures are shown in enomenon ,42. FurtAs can be seen in 2 increases with increasing pressure, while the permeability of slightly soluble gases such as He decreases with increasing pressure. This has been demonstrated by the polymer hydrogen permeation experiments performed by Kanesugi [Kanesugi . The incThe internal spatial structure of polymer materials consists mainly of molecular chains and free volumes. The free volume is dispersed in the material in the form of “pores”, whose size and distribution affect the ability of gas molecules to diffuse through the material. The study of the free volume allows the analysis and interpretation of the barrier properties of the material to gases . The freThis indicates that the increase in temperature accelerates the movement of the molecules within the material and the gas, increasing the volume and the number of “pores” in the system and increasing the fractional free volume (FFV), thus increasing the diffusion coefficient and decreasing the barrier performance.To further analyze the effect of the free volume, the FFV calculated from the simulation results is shown in −13 cm3∙cm/(cm2∙s∙Pa). When more OMMT was added, the permeability coefficient increased instead. This indicates that 5% OMMT content in PA6 is most suitable for the best barrier performance. Temperature also has an effect on the barrier properties of the material. The increase in temperature caused a decrease in the adsorption capacity of the material, a significant increase in diffusion capacity and a deterioration in the barrier properties. However, the permeability of the system with 5% OMMT content added remained the best for the temperature change. In addition, the barrier performance of the system with 5% OMMT content at different pressures was also investigated. It was found that the permeability coefficient decreased and the barrier properties increased when pressure reached 41.6 MPa. With the further increase in pressure, the gas diffusion coefficient increased and the barrier performance of the material decreased. Additionally, this study also found that the FFV affects the diffusion coefficient of the material. The larger the FFV, the larger the diffusion coefficient of the material.In this paper, the barrier properties of OMMT/PA6 composites at different filler contents, different temperatures and different pressures were investigated using molecular dynamics methods. The results show that the increase in the OMMT content in PA6 can effectively limit the diffusion movement of the gas molecules within the material, thus further improving the barrier properties. When the content of OMMT added reached 5%, the permeability coefficient of the material was lower than 2 × 10


Langmuir adsorption capacity was calculated as 3.92 mg g−1 with R2 = 0.99. The kinetic data brought to light that pseudosecond-order kinetic model was well described the experimental data (R2 = 0.99), and according to thermodynamic parameters, boron adsorption has spontaneous and endothermic nature (ΔH°=15.47 kJ mol−1).Graphene oxide, which has a great application in industry, is one of the promising carbonous materials. Modified Hummers method was applied to synthesize graphene oxide. Characterization techniques showed that pure graphene oxide was successfully obtained. Its adsorptive properties were investigated by boron adsorption. The results were demonstrated that boron adsorption on graphene oxide was a pH-dependent process and maximum adsorption was achieved at pH 6 (0.98 mg g However, its concentrations were restricted due to harmful effects. Boron concentration is restricted as 0.5 mg LRemoval of boron from wastewater has been studied using coagulation and coprecipitation process , 3, adso3O4) nanoparticles with the maximum adsorption capacity as 4.57 mmol g−1 at pH 7 [−1 [Adsorption is the most useful technique among boron removal techniques due to being applicable at low concentrations with high adsorption capacities. Boron adsorption was studied using various adsorbents. Chen et al. examined boron adsorption using magnetic magnetite Fe3O nanopartpH 7 [−1 . Boron rpH 7 [−1 , 16.Graphene is a new carbon-based material with high surface area and excellent mechanical properties . GraphenIn this study, raw graphene oxide was used for boron adsorption to establish its usability and adsorption capacity for removal of boron. Modified Hummers method was used to synthesize graphene oxide in our research laboratory and characterization was performed with various methods. Synthesized graphene oxide was used as an adsorbent for boron adsorption to investigate the effects of adsorbent amount, solution pH, initial boron concentration, contact time, and temperature. Obtained data was used to determine the isotherm, kinetic, and thermodynamic parameters.Graphite powder was purchased from Grafen Chemical Industries (Grafen Co.). Potassium persulfate (99.99%), phosphorus pentoxide (98.0%), potassium permanganate (99.0%), hydrogen peroxide (30%), ascorbic acid (99.0%), ammonium acetate (99.99%), acetic acid (100%), boric acid (99.5%), and ethylenediaminetetraacetic acid disodium salt (99.0%) were obtained from Merck, Germany. Azomethine-H reagent (97.0%) was purchased from Acros Organics, Belgium. Thyoglycolic acid (80.0%) was obtained from Sigma-Aldrich, USA. All chemicals used in the experiments were in analytical grade.2SO4 was added slowly onto a certain amount of preoxidated graphite. To solve the mixture, 30 g of KMnO4 was put into the solution while keeping the temperature constant at 10 °C. The temperature was then raised to 35 °C and the mixture was mixed for 4 h. Next, 500 mL of distilled water was added drop by drop into the solution and agitated for 1 h. Subsequently, 40 mL of 35% H2O2 was added into the remaining solution. The solution was aged for 1 day and filtered. The mixture was washed with 10% HCl solution and acetone until neutral pH was reached. Synthesized graphite oxide was dried in an oven at 60 °C. Finally, graphite oxide was ultrasonicated for 2 h, and after centrifugation, graphene oxide was synthesized and dried in an oven at 60 °C.Modified Hummers method was used to synthesize graphene oxide from graphite . First, The materials were thoroughly characterized through the utilization of advanced analytical techniques. Specifically, X-ray diffraction analysis was conducted utilizing the state-of-the-art PANanlytical X’Pert PRO diffractometer, providing high precision and accuracy in crystal structure determination. Fourier transform infrared spectrometry was employed using the Perkin Elmer Spectrum One instrument to identify and analyze functional groups present in the material. Additionally, scanning electron microscopy using the cutting-edge FEI QUANTA FEG 250 was employed to obtain high-resolution images and information regarding the surface morphology of the material. This comprehensive characterization approach allowed for a thorough understanding of the properties and characteristics of the materials studied.−1) on a mechanical shaker. Determined amount of graphene oxide was used to examine the pH effect on boron adsorption by changing solution pH between 2 and 12. The pH of the solutions was adjusted with 0.1 M HNO3 and 0.1 M NaOH solutions. The effect of initial boron concentration was studied by shaking a series of 50 mL boron solution whose concentrations were changed between 2 and 36 mg L−1. Kinetic experiments were conducted at different time intervals (1–36 h) by shaking 50 mL of 4 mg L−1 boron solution with 0.2 g graphene oxide. Thermodynamic experiments were studied at 298, 308, and 318 K to investigate the thermodynamic parameters.Boron removal was studied in batch system and various parameters such as adsorbent amount, solution pH, initial solution concentration, contact time and temperature were examined. To investigate the effect of adsorbent dosage, various amounts of graphene oxide (0.05–0.4 g) was mixed continuously with 50 mL boron solution . The adsorption capacity of graphene oxide was calculated by the following equation;qe represents the adsorption capacity at equilibrium (mg g−1), Co shows the initial boron concentration, and Ce is the equilibrium concentration of boron solution (mg L−1), m is the amount of graphene oxide (g), and V is the solution volume (L).where d-spacing of graphite and graphene oxide was 0.328 nm and 0.759 nm, respectively. Increasing d-spacing between the layers can be attributed to the bounding of oxygen groups into the structure while synthesizing the graphene oxide from graphite. XRD pattern of graphene oxide after boron adsorption was similar to raw graphene oxide, which might be attributed to the low boron concentration used in the experiments.X-ray diffraction patterns of graphite, graphene oxide before adsorption, and graphene oxide after adsorption are shown in −1, indicating vibration band of OH group of water bounded on the structure [−1 and 1620 cm−1 could be attributed to vibration band of C=O group of carboxylic acid and C=C groups in graphene oxide structure [−1 assigned to vibration band of C-O belongs to epoxy and alkoxy groups. Stretching vibration of S-O group that appeared at 831 cm−1 belongs to sulfur in graphene oxide structure. FTIR spectra after boron adsorption did not show any significant change with the raw graphene oxide.FTIR spectra of graphite and graphene oxide are given in tructure . The absSEM images and EDX analysis of all samples are given in −1 of initial boron concentration. The influence of adsorption dosage was investigated by changing graphene oxide amount between 0.05 and 0.4 g using 4 mg LSolution pH is an important factor that describes the surface properties of the adsorbent and also molecular structure of the adsorbate. Therefore, solution pH was adjusted from pH 2 to 12, and the adsorption capacities are given in −1), a slight decrease was observed until pH 10 (0.96 mg g−1), and a sharp decrease was seen at pH 12 (0.37 mg g−1). Boric acid forms borate anion in water with pKa value 9.2. According to the solubility reaction below pH 8, boric acid (H3BO3) is the dominant species in water, while borate anion (B(OH)4−) is the dominant species above pH 10. Between pH 8 and 10, occurrence of H3BO3 species diminishes slowly and B(OH)4− species rises in water medium. Besides, hydroxyl and carboxylic acid groups on graphene oxide structure are protonated in acidic medium and the surface charge becomes positive. As the pH increases, the surface loses its protons and the surface becomes negatively charged. According to ionization of boric acid and protonation of graphene oxide surface, hydrogen bonding between oxygen and hydrogen molecules is the significant reason for the adsorption of boric acid in acidic medium. Boric acid slowly decreases because the borate anion begins to form in the solution as the pH increases, which causes a higher adsorption of boron. After pH 10, a significant decrease was observed due to electrostatic repulsive forces between the B(OH)4− and negatively charged surface of graphene oxide. Besides, the competition between hydroxyl molecules (OH−) and B(OH)4− anions had a negative effect on boron adsorption; therefore, a sharp decrease was observed after pH 10.As seen from −1, and experiments were conducted using 0.2 g of graphene oxide at room temperature during 24 h. As shown in −1) and it decreased to 40% for 36 mg L−1 boron concentration. Certain amount of active adsorption sites was occupied with boron molecules, and after a point, no further adsorption occurred on these sites. Therefore, it was easy to adsorb boron at low concentrations due to the number of free adsorption sites. However, increasing boron concentration resulted in a decrease in adsorption, because all adsorption sites were covered by a certain amount of boron molecules and the rest of the boron molecules remained in the solution.One of the parameters that affect adsorption capacities of graphene oxide is initial solution concentration. Boron solution concentrations were changed between 2 and 36 mg LL2 type subgroup. L type isotherms define Langmuir isotherms, which implies monolayer adsorption on graphene oxide.According to initial solution parameters, adsorption isotherm and isotherm models were analyzed. Langmuir, Freundlich, Temkin, and Dubinin-Redushkevich isotherm models were used to analyze the isotherm data . LangmuiTo identify the conformity of isotherm models and experimental data, correlation coefficient and chi-square values were compared. To calculate the chi-square values, the equation below was used:X2 indicate that the isotherm model is in a good agreement with experimental data. According to correlation coefficients (R2) and chi-square (X2) values, Langmuir isotherm model  was best-fitted model of adsorption process, which indicates the monolayer adsorption of boron on graphene oxide. Langmuir adsorption capacity was found as 3.92 mg g−1. As shown in qe values were in a good correlation with those calculated from Langmuir model among all isotherm models. Ismonto et al. (2014) studied synthesis of activated carbon and carbon nanotubes for boron removal from wastewater. Carbon nanotubes were modified with polyvinyl alcohol (PVA) and adsorption capacity was found as 1.19 mg g−1, while it was 1.28 mg g−1 for unmodified carbon nanotubes. Besides, Langmuir isotherm model was the best-fitted model for boron adsorption [−1 adsorption capacity for boron removal [−1 adsorption capacity [The parameters calculated from the isotherm data are given in sorption . Zohdi e removal . JaouadiRL) can be calculated using Langmuir constant b (L mg−1). RL defines if the adsorption process is favorable or unfavorable. RL is calculated as:Dimensionless constant separation factor . Pseudosecond-order kinetic model was derived based on three assumptions: (i) the adsorption process is irreversible; therefore, desorption can be neglected, (ii) there is no concentration gradient of the adsorbate remaining in the solution, (iii) adsorption process is controlled by a chemical reaction [−1) demonstrated a good correlation with the experimental value (0.986 mg g−1). According to the literature comparison, pseudosecond-order kinetic model was best fitted for boron adsorption [The kinetic modeling of boron adsorption was analyzed using various kinetic models . Kineticreaction , 33. Measorption ,35.To understand the effect of temperature on boron adsorption, batch adsorption experiments were examined at 298, 308, and 318 K. It was observed that increasing temperature has a positive effect on boron adsorption on graphene oxide. Meanwhile, thermodynamic parameters were also calculated to better understand the adsorption behavior.H°) was calculated according to Van’t Hoff equation [Change in standard enthalpy  was given as ;Change in standard entropy (ΔS°) was calculated as ;K) showed a good correlation with ΔH°. Increasing temperature resulted in an increment in K values. ΔG° values were found negative suggesting that the boron adsorption process spontaneously occurred at all temperatures. Positive values of ΔS° indicated that randomness at solid/liquid interface and degree of freedom of the adsorbed boron were increased.Calculated thermodynamic parameters are given in e= 0.98 mg g−1. Experimental data was well correlated with Langmuir model (qmax= 3.92 mg g−1 and R2 = 0.99), which indicates monolayer adsorption of boron on graphene oxide surface. Electrostatic attractions and chelating ability of boron were responsible forces for boron adsorption. Kinetic data followed pseudo-second-order kinetic model (R2 = 0.99), and according to thermodynamic parameters, boron adsorption had spontaneous and endothermic nature. All the results given above indicated that graphene oxide could be used as a promising adsorbent for the adsorption of boron from wastewater.Boron adsorption was studied using graphene oxide, which was synthesized by modified Hummers method and the structure of graphene oxide was lightened. According to XRD analysis, graphene oxide was obtained successfully from graphite that gave the characteristic peak at 2θ = 11.6°. Meanwhile, FTIR analysis proved the synthesized graphene oxide C=O, C-O, and C=C bands. Boron adsorption strongly depended on the solution pH and the maximum adsorption capacity was found at pH 6 with q