Exploring the Use of the Picture Exchange Communication System (PECS) in Special Education Settings.

The Picture Exchange Communication System (PECS) is an Augmentative and Alternative Communication (AAC) system which is widely used to support children with developmental disabilities. In the present study, we surveyed individuals responsible for implementing PECS in special educational settings in the United Kingdom (N=283). We explored knowledge of and adherence to the  intervention, with a view to identifying training and support needs. Specifically, we examined participants' knowledge, implementation accuracy, training experiences, access to resources, and attitudes towards PECS. We developed hierarchical logistic regression models to explore the association between training experience and both knowledge and use of PECS. We pre-registered our methods, predictions and the analysis plan on the Open Science Framework (OSF).We found considerable variation in practitioner knowledge and implementation of PECS. Formal training predicted greater knowledge and more accurate implementation when practitioner role and the degree of setting support were accounted for. While PECS was rated by a large majority to be effective and practical, many participants identified that time and the availability of resources were barriers to implementation. We also found that the purpose of PECS was not always fully understood by practitioners, and we identified some consistent gaps in knowledge and implementation.  This study contributes new information regarding the real-world  use of PECS in educational settings and offers new insights for supporting practitioners.

Transformer Models in Healthcare: A Survey and Thematic Analysis of Potentials, Shortcomings and Risks.

Large Language Models (LLMs) such as General Pretrained Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT), which use transformer model architectures, have significantly advanced artificial intelligence and natural language processing. Recognized for their ability to capture associative relationships between words based on shared context, these models are poised to transform healthcare by improving diagnostic accuracy, tailoring treatment plans, and predicting patient outcomes. However, there are multiple risks and potentially unintended consequences associated with their use in healthcare applications. This study, conducted with 28 participants using a qualitative approach, explores the benefits, shortcomings, and risks of using transformer models in healthcare. It analyses responses to seven open-ended questions using a simplified thematic analysis. Our research reveals seven benefits, including improved operational efficiency, optimized processes and refined clinical documentation. Despite these benefits, there are significant concerns about the introduction of bias, auditability issues and privacy risks. Challenges include the need for specialized expertise, the emergence of ethical dilemmas and the potential reduction in the human element of patient care. For the medical profession, risks include the impact on employment, changes in the patient-doctor dynamic, and the need for extensive training in both system operation and data interpretation.

Correction to "Transition Metal Dissolution Mechanisms and Impacts on Electronic Conductivity in Composite LiNi0.5Mn1.5O4 Cathode Films".

[This corrects the article DOI: 10.1021/acsmaterialsau.2c00060.].

Transition Metal Dissolution Mechanisms and Impacts on Electronic Conductivity in Composite LiNi0.5Mn1.5O4 Cathode Films.

The high-voltage LiNi0.5Mn1.5O4 (LNMO) spinel cathode material offers high energy density storage capabilities without the use of costly Co that is prevalent in other Li-ion battery chemistries (e.g., LiNixMnyCozO2 (NMC)). Unfortunately, LNMO-containing batteries suffer from poor cycling performance because of the intrinsically coupled processes of electrolyte oxidation and transition metal dissolution that occurs at high voltage. In this work, we use operando electron paramagnetic resonance (EPR) and nuclear magnetic resonance (NMR) spectroscopies to demonstrate that transition metal dissolution in LNMO is tightly coupled to HF formation (and thus, electrolyte oxidation reactions as detected with operando and in situ solution NMR), indicative of an acid-driven disproportionation reaction that occurs during delithiation (i.e., battery charging). Leveraging the temporal resolution (s-min) of magnetic resonance, we find that the LNMO particles accelerate the rate of LiPF6 decomposition and subsequent Mn2+ dissolution, possibly due to the acidic nature of terminal Mn-OH groups. X-ray photoemission electron microscopy (XPEEM) provides surface-sensitive and localized X-ray absorption spectroscopy (XAS) measurements, in addition to X-ray photoelectron spectroscopy (XPS), that indicate disproportionation is enabled by surface reconstruction upon charging, which leads to surface Mn3+ sites on the LNMO particle surface that can disproportionate into Mn2+(dissolved) and Mn4+(s). During discharge of the battery, we observe high quantities of metal fluorides (in particular, MnF2) in the cathode electrolyte interphase (CEI) on LNMO as well as the conductive carbon additives in the composite. Electronic conductivity measurements indicate that the MnF2 decreases film conductivity by threefold compared to LiF, suggesting that this CEI component may impede both the ionic and electronic properties of the cathode. Ultimately, to prevent transition metal dissolution and the associated side reactions in spinel-type cathodes (particularly those that operate at high voltages like LNMO), the use of electrolytes that offer improved anodic stability and prevent acid byproducts will likely be necessary.

How Can Transformer Models Shape Future Healthcare: A Qualitative Study.

Transformer models have been successfully applied to various natural language processing and machine translation tasks in recent years, e.g. automatic language understanding. With the advent of more efficient and reliable models (e.g. GPT-3), there is a growing potential for automating time-consuming tasks that could be of particular benefit in healthcare to improve clinical outcomes. This paper aims at summarizing potential use cases of transformer models for future healthcare applications. Precisely, we conducted a survey asking experts on their ideas and reflections for future use cases. We received 28 responses, analyzed using an adapted thematic analysis. Overall, 8 use case categories were identified including documentation and clinical coding, workflow and healthcare services, decision support, knowledge management, interaction support, patient education, health management, and public health monitoring. Future research should consider developing and testing the application of transformer models for such use cases.

Assessing the Potential Risks of Digital Therapeutics (DTX): The DTX Risk Assessment Canvas.

MOTIVATION: Digital therapeutics (DTX), i.e., health interventions that are provided through digital means, are increasingly available for use; in some countries, physicians can even prescribe selected DTX following a reimbursement by health insurances. This results in an increasing need for methodologies to consider and monitor DTX's negative consequences, their risks to patient safety, and possible adverse events. However, it is completely unknown which aspects should be subject to surveillance given the missing experiences with the tools and their negative impacts. OBJECTIVE: Our aim is to develop a tool-the DTX Risk Assessment Canvas-that enables researchers, developers, and practitioners to reflect on the negative consequences of DTX in a participatory process. METHOD: Taking the well-established business model canvas as a starting point, we identified relevant aspects to be considered in a risk assessment of a DTX. The aspects or building blocks of the canvas were constructed in a two-way process: first, we defined the aspects relevant for discussing and reflecting on how a DTX might bring negative consequences and risks for its users by considering ISO/TS 82304-2, the scientific literature, and by reviewing existing DTX and their listed adverse effects. The resulting aspects were grouped into thematic blocks and the canvas was created. Second, six experts in health informatics and mental health provided feedback and tested the understandability of the initial canvas by individually applying it to a DTX of their choice. Based on their feedback, the canvas was modified. RESULTS: The DTX Risk Assessment Canvas is organized into 15 thematic blocks which are in turn grouped into three thematic groups considering the DTX itself, the users of the DTX, and the effects of the DTX. For each thematic block, questions have been formulated to guide the user of the canvas in reflecting on the single aspects. Conclusions: The DTX Risk Assessment Canvas is a tool to reflect the negative consequences and risks of a DTX by discussing different thematic blocks that together constitute a comprehensive interpretation of a DTX regarding possible risks. Applied during the DTX design and development phase, it can help in implementing countermeasures for mitigation or means for their monitoring.

A comparison of stimulus fading and stimulus shaping on perceptual category learning.

Errorless learning strategies such as stimulus fading and stimulus shaping are commonly used to teach complex skills like categorisation and concept formation. Despite widespread use, very few studies have explored the comparative effectiveness of these procedures in well-controlled analyses. The vast majority of existing studies have been undertaken with clinical populations and have involved small numbers of participants (e.g., Single-case designs). The present study sought to compare stimulus fading, stimulus shaping, and trial-and-error learning in a perceptual categorisation task. In Experiment 1, we found robust benefits of stimulus shaping when compared to stimulus fading or trial-and-error learning on measures of initial acquisition of discrimination and one measure of stimulus generalisation. These findings were replicated in a second experiment in which the dimension of fading/shaping was changed from a modification of the comparison stimuli (S-) to a modification of the target stimulus (S +). We discuss the implications of our findings for the selection of errorless learning strategies in clinical settings.

Tozorakimab (MEDI3506): an anti-IL-33 antibody that inhibits IL-33 signalling via ST2 and RAGE/EGFR to reduce inflammation and epithelial dysfunction.

Interleukin (IL)-33 is a broad-acting alarmin cytokine that can drive inflammatory responses following tissue damage or infection and is a promising target for treatment of inflammatory disease. Here, we describe the identification of tozorakimab (MEDI3506), a potent, human anti-IL-33 monoclonal antibody, which can inhibit reduced IL-33 (IL-33red) and oxidized IL-33 (IL-33ox) activities through distinct serum-stimulated 2 (ST2) and receptor for advanced glycation end products/epidermal growth factor receptor (RAGE/EGFR complex) signalling pathways. We hypothesized that a therapeutic antibody would require an affinity higher than that of ST2 for IL-33, with an association rate greater than 107 M-1 s-1, to effectively neutralize IL-33 following rapid release from damaged tissue. An innovative antibody generation campaign identified tozorakimab, an antibody with a femtomolar affinity for IL-33red and a fast association rate (8.5 × 107 M-1 s-1), which was comparable to soluble ST2. Tozorakimab potently inhibited ST2-dependent inflammatory responses driven by IL-33 in primary human cells and in a murine model of lung epithelial injury. Additionally, tozorakimab prevented the oxidation of IL-33 and its activity via the RAGE/EGFR signalling pathway, thus increasing in vitro epithelial cell migration and repair. Tozorakimab is a novel therapeutic agent with a dual mechanism of action that blocks IL-33red and IL-33ox signalling, offering potential to reduce inflammation and epithelial dysfunction in human disease.

Digital health as an enabler for hospital@home: A rising trend or just a vision?

Background: Hospital@home is a model of healthcare, where healthcare professionals actively treat patients in their homes for conditions that may otherwise require hospitalization. Similar models of care have been implemented in jurisdictions around the world over the past few years. However, there are new developments in health informatics including digital health and participatory health informatics that may have an impact on hospital@home approaches. Objectives: This study aims to identify the current state of implementation of emerging concepts into the hospital@home research and models of care; to identify strengths and weaknesses, opportunities, and threats associated with the models of care; and to suggest a research agenda. Methods: We employed two research methodologies, namely, a literature review and a SWOT (strengths, weaknesses, opportunities, and threats) analysis. The literature from the last 10 years was collected from PubMed using the search string "hospital at home" OR "care at home" OR "patient at home." Relevant information was extracted from the included articles. Results: Title and abstract review were conducted on 1,371 articles. The full-text review was conducted on 82 articles. Data were extracted from 42 articles that met our review criteria. Most of the studies originated from the United States and Spain. Several medical conditions were considered. The use of digital tools and technologies was rarely reported. In particular, innovative approaches such as wearables or sensor technologies were rarely used. The current landscape of hospital@home models of care simply delivers hospital care in the patient's home. Tools or approaches from taking a participatory health informatics design approach involving a range of stakeholders (such as patients and their caregivers) were not reported in the literature reviewed. In addition, emerging technologies supporting mobile health applications, wearable technologies, and remote monitoring were rarely discussed. Conclusion: There are multiple benefits and opportunities associated with hospital@home implementations. There are also threats and weaknesses associated with the use of this model of care. Some weaknesses could be addressed by using digital health and wearable technologies to support patient monitoring and treatment at home. Employing a participatory health informatics approach to design and implementation could help to ensure the acceptance of such care models.

Developing a Technical-Oriented Taxonomy to Define Archetypes of Conversational Agents in Health Care: Literature Review and Cluster Analysis.

BACKGROUND: The evolution of artificial intelligence and natural language processing generates new opportunities for conversational agents (CAs) that communicate and interact with individuals. In the health domain, CAs became popular as they allow for simulating the real-life experience in a health care setting, which is the conversation with a physician. However, it is still unclear which technical archetypes of health CAs can be distinguished. Such technical archetypes are required, among other things, for harmonizing evaluation metrics or describing the landscape of health CAs. OBJECTIVE: The objective of this work was to develop a technical-oriented taxonomy for health CAs and characterize archetypes of health CAs based on their technical characteristics. METHODS: We developed a taxonomy of technical characteristics for health CAs based on scientific literature and empirical data and by applying a taxonomy development framework. To demonstrate the applicability of the taxonomy, we analyzed the landscape of health CAs of the last years based on a literature review. To form technical design archetypes of health CAs, we applied a k-means clustering method. RESULTS: Our taxonomy comprises 18 unique dimensions corresponding to 4 perspectives of technical characteristics (setting, data processing, interaction, and agent appearance). Each dimension consists of 2 to 5 characteristics. The taxonomy was validated based on 173 unique health CAs that were identified out of 1671 initially retrieved publications. The 173 CAs were clustered into 4 distinctive archetypes: a text-based ad hoc supporter; a multilingual, hybrid ad hoc supporter; a hybrid, single-language temporary advisor; and, finally, an embodied temporary advisor, rule based with hybrid input and output options. CONCLUSIONS: From the cluster analysis, we learned that the time dimension is important from a technical perspective to distinguish health CA archetypes. Moreover, we were able to identify additional distinctive, dominant characteristics that are relevant when evaluating health-related CAs (eg, input and output options or the complexity of the CA personality). Our archetypes reflect the current landscape of health CAs, which is characterized by rule based, simple systems in terms of CA personality and interaction. With an increase in research interest in this field, we expect that more complex systems will arise. The archetype-building process should be repeated after some time to check whether new design archetypes emerge.

Client Views of Contingency Management in Gambling Treatment: A Thematic Analysis.

Low levels of treatment access and poor retention among those with gambling problems suggests a need to improve treatment. Contingency management (CM) is a behavioural intervention involving the identification of target behaviours and the provision of incentives when targets are met. There exists a substantial evidence base for CM increasing abstinence and attendance in substance misuse treatment, but this has not been widely extended to gambling treatment setting. This study sought to explore the views of clients about CM for the treatment of problematic and disordered gambling. We conducted semi-structured interviews with 25 gambling treatment clients who were, or had previously been, engaged in treatment in Great Britain. Participants were provided with an explanation of CM, two hypothetical scenarios, and two structured questionnaires to facilitate discussion. Thematic analysis was used to interpret findings. Some participants felt that clients could manipulate CM while in treatment to obtain money to gamble, and that mechanisms of CM could trigger recovering clients into relapse. Participants also identified potential benefits of CM to achieve treatment goals, by enhancing motivation and engagement while in treatment, and helping bring people into treatment earlier. Gambling treatment clients broadly supported the use of incentives for treatment. CM is seen as a facilitator of extended engagement in treatment, and an encouragement for clients to make progress in the treatment process.

Usability Assessment of Conversational Agents in Healthcare: A Literature Review.

Conversational agents (CA) are chatbot-based systems supporting the interaction with users through text, speech, or other modalities. They are used in an increasing number of medical use cases. Even though usability is considered a prerequisite for the success of mHealth apps using CA, there is still no standard procedure to study usability of health CA. In this paper, we report the results from a systematic literature review aiming at identifying study designs, tools, and metrics used to assess usability in health CA. We searched three bibliographic databases (PubMed, Scopus, IEEE Xplore) for papers reporting on CA in healthcare to extract information on the usability assessment of those CA. From 273 retrieved results, we included 66 papers for full text review. 34 of them reported on usability assessments. A broad range of tools is used (e.g. SUS, UEQ), but also individual questionnaires are exploited. The examined studies use scenario-based setups but assess also real-world usage. Exploratory setups are rarely reported. Due to the differences in the study designs and assessment tools, it is impossible to compare usability among CA. Thus, we recommend to develop a standardised procedure that can be always applied and which can be enriched by assessments needed for evaluating usability of CA-specific features.

Gambling treatment service providers' views about contingency management: a thematic analysis.

BACKGROUND: There is a need to improve retention and outcomes for treatment of problem gambling and gambling disorder. Contingency management (CM) is a behavioural intervention involving identification of target behaviours (such as attendance, abstinence, or steps towards recovery) and the provision of incentives (such as vouchers or credits towards the purchase of preferred items) contingent on objective evidence of these behaviours. Contingency management for abstinence and attendance in substance misuse treatment has a substantial evidence base but has not been widely adopted or extended to other addictive behaviours such as gambling. Potential barriers to the widespread adoption of CM may relate to practitioners' perceptions about this form of incentive-based treatment. The present study sought to explore United Kingdom (UK) gambling treatment providers' views of CM for treatment of problem gambling and gambling disorder. METHODS: We conducted semi-structured interviews with 30 treatment providers from across the UK working with people with gambling problems. Participants were provided with an explanation of CM, several hypothetical scenarios, and a structured questionnaire to facilitate discussion. Thematic analysis was used to interpret findings. RESULTS: Participants felt there could be a conflict between CM and their treatment philosophies, that CM was similar in some ways to gambling, and that the CM approach could be manipulated and reduce trust between client and therapist. Some participants were more supportive of implementing CM for specific treatment goals than others, such as for incentivising attendance over abstinence due to perceived difficulties in objectively verifying abstinence. Participants favoured providing credits accruing to services relevant to personal recovery rather than voucher-based incentives. CONCLUSIONS: UK gambling treatment providers are somewhat receptive to CM approaches for treatment of problem gambling and gambling disorder. Potential barriers and obstacles are readily addressable, and more research is needed on the efficacy and effectiveness of CM for gambling.

Sputum biomarkers during acute severe asthma attacks in children-a case-control study.

AIM: To study sputum mediator profiles pattern in children with acute severe asthma, compared with stable asthma and healthy controls. The mechanisms of acute severe asthma attacks, such as biomarkers cascades and immunological responses, are poorly understood. METHODS: We conducted a prospective observational case-control study of children aged 5 to 17 years, who presented to hospital with an asthma attack. Children with stable asthma were recruited during outpatient asthma clinic visits. Control children without an asthma diagnosis were recruited from surgical wards. Sputum mediator profiles were measured, and sputum leukocyte differential cell counts were generated. RESULTS: Sputum data were available in 48 children (acute asthma; n = 18, stable asthma; n = 17, healthy controls; n = 13). Acute-phase biomarkers and neutrophil attractants such as IL-6 and its receptor, IL-8 and cytokines linked with bacterial signals, including TNF-R1 and TNF-R2, were elevated in asthma attacks versus stable asthma and healthy controls. T-cell attractant cytokines, associated with viral infections, such as CCL-5, CXCL-10 and CXCL-11, and CXCL-9 (secreted from eosinophils after a viral trigger) were also raised. CONCLUSION: Mediator profiles consistent with bacterial and viral respiratory infections, and T2 inflammation markers co-exist in the sputum of children with acute severe asthma attacks.

Security, privacy, and healthcare-related conversational agents: a scoping review.

Health chatbots interview patients and collect health data. This process makes demands on data security and data privacy. To identify how and to what extent security and privacy are considered in current health chatbots. We conducted a scoping review by searching three bibliographic databases (PubMed, ACM Digital Library, IEEExplore) for papers reporting on chatbots in healthcare. We extracted which, how, and where data is stored by health chatbots and identified which external services have access to the data. Out of 1026 retrieved papers, we included 70 studies in the qualitative synthesis. Most papers report on chatbots that collect and process personal health data, usually in the context of mental health coaching applications. The majority did not provide any information regarding security or privacy aspects. We were able to determine limitations in literature and identified concrete challenges, including data access and usage of (third-party) services, data storage, data security methods, use case peculiarities and data privacy, as well as legal requirements. Data privacy and security in health chatbots are still underresearched and related information is underrepresented in scientific literature. By addressing the five key challenges in future, the transfer of theoretical solutions into practice can be facilitated.

Potassium Fluoride and Carbonate Lead to Cell Failure in Potassium-Ion Batteries.

While Li-ion is the prevailing commercial battery chemistry, the development of batteries that use earth-abundant alkali metals (e.g., Na and K) alleviates reliance on Li with potentially cheaper technologies. Electrolyte engineering has been a major thrust of Li-ion battery (LIB) research, and it is unclear if the same electrolyte design principles apply to K-ion batteries (KIBs). Fluoroethylene carbonate (FEC) is a well-known additive used in Li-ion electrolytes because the products of its sacrificial decomposition aid in forming a stable solid electrolyte interphase (SEI) on the anode surface. Here, we show that FEC addition to KIBs containing hard carbon anodes results in a dramatic decrease in capacity and cell failure in only two cycles, whereas capacity retention remains high (> 90% over 100 cycles at C/10 for both KPF6 and KFSI) for electrolytes that do not contain FEC. Using a combination of 19F solid-state nuclear magnetic resonance (SSNMR) spectroscopy, X-ray photoelectron spectroscopy (XPS), and electrochemical impedance spectroscopy (EIS), we show that FEC decomposes during galvanostatic cycling to form insoluble KF and K2CO3 on the anode surface, which correlates with increased interfacial resistance in the cell. Our results strongly suggest that KIB performance is sensitive to the accumulation of an inorganic SEI, likely due to poor K transport in these compounds. This mechanism of FEC decomposition was confirmed in two separate electrolyte formulations using KPF6 or KFSI. Interestingly, the salt anions do not decompose themselves, unlike their Li analogues. Insight from these results indicates that electrolyte decomposition pathways and favorable SEI components are significantly different in KIBs and LIBs, suggesting that entirely new approaches to KIB electrolyte engineering are needed.

Evolving contact mechanics and microstructure formation dynamics of the lithium metal-Li7La3Zr2O12 interface.

The dynamic behavior of the interface between the lithium metal electrode and a solid-state electrolyte plays a critical role in all-solid-state battery performance. The evolution of this interface throughout cycling involves multiscale mechanical and chemical heterogeneity at the micro- and nano-scale. These features are dependent on operating conditions such as current density and stack pressure. Here we report the coupling of operando acoustic transmission measurements with nuclear magnetic resonance spectroscopy and magnetic resonance imaging to correlate changes in interfacial mechanics (such as contact loss and crack formation) with the growth of lithium microstructures during cell cycling. Together, the techniques reveal the chemo-mechanical behavior that governs lithium metal and Li7La3Zr2O12 interfacial dynamics at various stack pressure regimes and with voltage polarization.

Recovery and Reuse of Composite Cathode Binder in Lithium Ion Batteries.

Here, we investigate the recovery and reuse of polyvinylidene fluoride (PVDF) binders from both homemade and commercial cathode films in Li ion batteries. We find that PVDF solubility depends on whether the polymer is an isolated powder or cast into a composite film. A mixture of tetrahydrofuran:N-methyl-2-pyrrolidone (THF : NMP, 50 : 50 v/v) at 90 °C delaminates composite cathodes from Al current collectors and yields pure PVDF as characterized by 1 H nuclear magnetic resonance (NMR), gel permeation chromatography (GPC), wide-angle X-ray scattering (WAXS), and scanning electron microscopy (SEM). PVDF recovered from Li ion cells post-cycling exhibits similar performance to pristine PVDF. These data suggest that PVDF can be extracted and reused during Li ion battery recycling while simultaneously eliminating the formation of HF etchants, providing an incentive for use in direct cathode recycling.

Developing Intelligent Interviewers to Collect the Medical History: Lessons Learned and Guidelines.

BACKGROUND: Physicians spend a lot of time in routine tasks, i.e. repetitive and time consuming tasks that are essential for the diagnostic and treatment process. One of these tasks is to collect information on the patient's medical history. OBJECTIVES: We aim at developing a prototype for an intelligent interviewer that collects the medical history of a patient before the patient-doctor encounter. From this and our previous experiences in developing similar systems, we derive recommendations for developing intelligent interviewers for concrete medical domains and tasks. METHODS: The intelligent interviewer was implemented as chatbot using IBM Watson assistant in close cooperation with a family doctor. RESULTS: AnCha is a rule-based chatbot realized as decision tree with 75 nodes. It asks a maximum of 44 questions on the medical history, current complaints and collects additional information on the patient, social details, and prevention. CONCLUSION: When developing an intelligent digital interviewer it is essential to define its concrete purpose, specify information to be collected, design the user interface, consider data security and conduct a practice-oriented evaluation.

High-performance organic pseudocapacitors via molecular contortion.

Pseudocapacitors harness unique charge-storage mechanisms to enable high-capacity, rapidly cycling devices. Here we describe an organic system composed of perylene diimide and hexaazatrinaphthylene exhibiting a specific capacitance of 689 F g-1 at a rate of 0.5 A g-1, stability over 50,000 cycles, and unprecedented performance at rates as high as 75 A g-1. We incorporate the material into two-electrode devices for a practical demonstration of its potential in next-generation energy-storage systems. We identify the source of this exceptionally high rate charge storage as surface-mediated pseudocapacitance, through a combination of spectroscopic, computational and electrochemical measurements. By underscoring the importance of molecular contortion and complementary electronic attributes in the selection of molecular components, these results provide a general strategy for the creation of organic high-performance energy-storage materials.