Grand Challenge Research Articles

Introduction to the Special Issue on the Role of Place in Sustainability: Key Trends and Agenda for Future Research

This editorial reviews key themes, trends, and assumptions of organizational research on place and sustainability and introduces the special issue on the “Role of Place in Sustainability.” While recent theorizing has often emphasized global issues such as grand challenges, planetary boundaries, and climate change, this special issue revisits the local by focusing on the role of place in sustainability. We discuss trends and subdomains of research on place and sustainability and identify key assumptions at the interplay between global and local perspectives. Instead of advocating for universal solutions or exclusively context-specific approaches, we highlight the concept of “senses of place,” emphasizing the connections among diverse notions of place and intrinsic links to these locations. We demonstrate how the four insightful articles featured in this special issue provide a broader dialogue on place and sustainability. Finally, we outline a research agenda that identifies underexplored themes in place-based sustainability studies.

Modulating local coordination structure over single Pt atom to optimize adsorption behavior for high-efficiency photocatalytic H2 production

The development of single-atom photocatalysts featuring precisely defined coordination structure is of great significance for elucidating the correlations between catalytic performances and coordination structures, yet it remains a grand challenge. Herein, the asymmetric/symmetric Pt-Nx moieties in carbon nitride (denoted as Pt-Nx@MCT, x equals to a coordination number of 3 or 4) are elaborately constructed by a coordination structure regulation strategy. The hydrogen production performances of Pt-N3@MCT and Pt-N4@MCT photocatalysts display a relationship with the Pt-N coordination numbers, with Pt-N3@MCT showing a photocatalytic activity (195 μmol h−1) 3 times that of Pt-N4@MCT (65 μmol h−1) and an apparent quantum yield of 34.1 % at 420 nm. The asymmetric Pt-N3 moiety in Pt-N3@MCT acts as electron collector to promote the separation and transfer of charge carriers and to create the higher occupied Pt d orbital, weakening the bond strength between hydrogen intermediates and Pt atomic sites and accelerating hydrogen production reaction kinetics in Pt-N3@MCT.

A Photoelectrochemical Retinomorphic Synapse.

Reproducing human visual functions with artificial devices is a long-standing goal of the neuromorphic domain. However, emulating the chemical language communication of the visual system in fluids remains a grand challenge. Here, a "multi-color" hydrogel-based photoelectrochemical retinomorphic synapse is reported with unique chemical-ionic-electrical signaling in an aqueous electrolyte that enables, e.g., color perception and biomolecule-mediated synaptic plasticity. Based on the specific enzyme-catalyzed chromogenic reactions, three multifunctional colored hydrogels are developed, which can not only synergize with the Bi2S3 photogate to recognize the primary colors but also synergize with a given polymeric channel to promote the long-term memory of the system. A synaptic array is further constructed for sensing color images and biomolecule-coded information communication. Taking advantage of the versatile biochemistry, the biochemical-driven reversible photoelectric response of the cone cell is further mimicked. This work introduces rich chemical designs into retinomorphic devices, providing a perspective for replicating the human visual system in fluids.

Dynamically Precise Constructing Dual-Atom Pd2 Catalyst:A Monodisperse Catalyst With High Stability for Semi-Hydrogenation of Alkyne.

Dual-atom catalysts (DACs) originate unprecedented reactivity and maximize resource efficiency. The fundamental difficulty lies in the high complexity and instability of DACs, making the rational design and targeted performance optimization a grand challenge. Here, an atomically dispersed Pd2 DAC with an in situ generated Pd─Pd bond is constructed by a dynamic strategy, which achieves high activity and selectivity for semi-hydrogenation of alkynes and functional internal acetylene, twice higher than commercial Lindlar catalyst. Density functional theory calculations and systematic experiments confirms the ultrahigh properties of Pd2 DAC originates from the synergistic effect of the dynamically generated Pd─Pd bonds. This discovery highlights the potential for dynamic strategies and opens unprecedented possibilities for the preparation of robust DACs on an industrial scale.

Moment-Based Reinforcement Learning for Ensemble Control.

Problems involving controlling the collective behavior of a population of structurally similar dynamical systems, the so-called ensemble control, arise in diverse emerging applications and pose a grand challenge in systems science and control engineering. Owing to the severely under-actuated nature and the difficulty of placing large-scale sensor networks, ensemble systems are limited to being actuated and monitored at the population level. Moreover, mathematical models describing the dynamics of ensemble systems are often elusive. Therefore, it is essential to design broadcast controls that excite the entire population in such a way that the heterogeneity in system dynamics is robustly compensated. In this article, we propose a reinforcement learning (RL)-based data-driven control framework incorporating population-level aggregated measurement data to learn a global control signal for steering a dynamic population in the desired manner. In particular, we introduce the notion of ensemble moments induced by aggregated measurements and derive the associated moment system to the original ensemble system. Then, using the moment system, we learn an approximation of optimal value functions and the associated policies in terms of ensemble moments through RL. We illustrate the feasibility and scalability of the proposed moment-based approach via numerical experiments using a population of linear, bilinear, and nonlinear dynamic ensemble systems. We report that the proposed method achieves the desired control objectives of various ensemble control tasks and obtains significantly better averaged-reward when compared with three existing methods.

Thriving through trials and tribulations: the impact of the ‘Grand Challenge’ on engineering student resilience skills

ABSTRACT Resilience is considered of significant importance for student attainment and work-readiness. This study investigated the impact of Grand Challenge (GC) – an industry-based learning assessment for 1st and 2nd year Engineering students – on student resilience. Resilience scores and psychological distress were measured before and after the GC through questionnaires presented to students. Focus groups were conducted to assess GC’s effect on student resilience qualitatively. Resilience scores in the questionnaires decreased slightly after GC, possibly due to less students completing the post-GC questionnaire. Interestingly, when discussing GC in the focus groups, students did feel GC helped them develop their resilience and understood the impact of resilience on their future career. Overall, the findings help establish GC as a challenge-based learning (CBL) intervention and the extent to which such CBL assessments may affect student resilience skills. The study provides the groundwork for future resilience training and assessment in higher engineering education through CBL events.

Responsible development of digital livestock technologies for agricultural challenges: Purpose, practicality and effects are key considerations

AbstractDigital livestock technologies (DLTs) are presented as solutions to grand challenges in post‐Brexit British agricultural policy, such as climate change and food security. Evidence suggests technological solutions to agricultural challenges will be more effective with stakeholder and public engagement, yet there is little known about stakeholder views on these emerging technologies. We drew on responsible research and innovation, to analyse stakeholder perspectives on three case studies of DLT development through anticipatory focus groups with expert stakeholders in British animal agriculture. We found that stakeholders from broadly agroecological approaches to farming are at risk of exclusion from DLT development and policy, with negative implications for the ability of DLTs to resolve grand challenges in animal agriculture. We propose a heuristic framework of purpose, practicality and effects as key considerations for inclusive and responsible DLT development that contributes to ensuring effective solutions to grand challenges while avoiding wasted investments.

Collaborative consensus on assistive technology impacts and outcomes: the GAATO Grand Challenges

ABSTRACT Capturing the outcomes of assistive technology (AT) is essential to know what works and what is needed. People across the globe are working to close the gap of unmet need for AT, and to enable access to quality AT products and services. Inconsistent terminology, measures and a lack of coordinated systems prevent the sharing of knowledge, and the lack of articulated global priorities limit the usefulness of data for policymakers and resource allocation decisions. This paper provides methods of a global “Grand Challenge” project, run by the Global Alliance of Assistive Technology Organizations, and describes the resulting challenges. A multiphase, mixed method approach founded on participatory principles sought to capture multi-country and multi-stakeholder perspectives “from the ground up.” A collaborative consensus method was adapted to document and distil AT challenge data from global regions, to refine this data with global stakeholder organizations, and to publish a shortlist of AT Grand Challenges. The 2022 Grand Challenges in Outcomes and Impact are 1) measuring need, 2) documenting inputs, 3) measuring outcomes, 4) measure impacts, 5) sharing data, and 6) informing policy. The challenge now remains to work together to resolve them.

EXPRESS: Researchers convening dialogue to address grand challenges: Affordances, tensions, and the shift to deep dialogue

EXPRESS: Researchers convening dialogue to address grand challenges: Affordances, tensions, and the shift to deep dialogue

Crime-First Labels and Public Attitudes Toward Adolescent Girls in the Juvenile Legal System

Purpose: This study delves into the effects of labels, specifically crime-first language, on public attitudes toward adolescent girls within the juvenile legal system. Examining fictional vignettes depicting family conflict, the research explores whether the use of language influences the endorsement of punitive responses and if this effect varies across racial and ethnic lines. Method: A large online, statewide sample of adults ( n = 1,791) was randomized to one of six vignettes. Results: Findings reveal that crime-first language, “accused of domestic battery,” is significantly associated with more punitive attitudes. We found no interaction effects between race and label. Although effect sizes were small, the study emphasizes the need to reconsider language choices to avoid perpetuating negative stereotypes and stigmatization. Discussion: Identifying and disseminating person-first language may be an important form of social work practice that can enhance current efforts on the Social Work Grand Challenge of promoting smart decarceration.

Arginine-loaded globular BSAMA/fibrous GelMA biohybrid cryogels with multifunctional features and enhanced healing for soft gingival tissue regeneration

Mucogingival surgery has been widely used in soft gingival tissue augmentation in which autografts are predominantly employed. However, the autografts face grand challenges, such as scarcity of palatal donor tissue and postoperative discomfort. Therefore, development of alternative soft tissue substitutes has been an imperative need. Here, we engineered an interconnected porous bovine serum albumin methacryloyl (BSAMA: B, as a drug carrier and antioxidant)/gelatin methacryloyl (GelMA: G, as a biocompatible collagen-like component)-based cryogel with L-Arginine (Arg) loaded as an angiogenic molecule, which could serve as a promising gingival tissue biohybrid scaffold. BG@Arg cryogels featured macroporous architecture, biodegradation, sponge-like properties, suturability, and sustained Arg release. Moreover, BG@Arg cryogels promoted vessel formation and collagen deposition which play an important role in tissue regeneration. Most interestingly, BG@Arg cryogels were found to enhance antioxidant effects. Finally, the therapeutic effect of BG@Arg on promoting tissue regeneration was confirmed in rat full-thickness skin and oral gingival defect models. In vivo results revealed that BG@Arg2 could promote better angiogenesis, more collagen production, and better modulation of inflammation, as compared to a commercial collagen membrane. These advantages might render BG@Arg cryogels a promising alternative to commercial collagen membrane products and possibly autografts for soft gingival tissue regeneration.

Hetero phase nanocomposite based posture sensor with stretchable connector-sensor interface

During recent years, stretchable and flexible sensors for wearable application have gained a lot of interest, but one of the grand challenges that yet to be addressed is the interface between the soft and stretchable sensor and the hard and rigid circuits. The mechanical mismatch between soft sensors and hard connectors to external circuits makes the interface prone to quick failure under mechanical strain and deformation.Here, we report a soft piezoresistive posture sensor made of a hetero-phase nanocomposite with the stretchable connector-sensor interface for the robust sensing of the posture. The hetero-phase nanocomposite is made of Gallium-indium (GaIn) in the liquid phase, carbon nano tube (CNT) and polydimethylsiloxane (PDMS) in the solid phase. The sensor with the Young’s modulus of 158 kPa shows comparable mechanical softness to the skin, which is essential for accurate sensing. Further, we developed liquid metal (LM) based stretchable connections between the soft sensor and the connectors to the external circuit to enable the stable operation of the sensor under deformation. The developed posture sensor has a gauge factor (GF) of 0.815 under 20 % and 0.32 between 20 % and 60 % of applied tensile strain. The sensor was successfully used for sensing of the posture with the sensitivity of 1 % change in its electrical resistance per 10 degrees of neck tilting and strain sensitivity of 2 % of applied strain. The fabrication of the CNT-LM-PDMS posture sensor employed a scalable and cost-effective process. The presented stretchable connection is a solution that can be applied to many soft and stretchable wearable sensors and circuit interfaces with various physiological sensing applications.

Deep Reconstruction of Ruthenate Perovskite Oxide Enables Efficient pH-Universal Hydrogen Evolution Reaction.

Designing highly efficient, stable, and pH-universal perovskites for hydrogen evolution reaction (HER) is urgently needed yet remains a grand challenge. Herein, a titanium-containing strontium ruthenate (SrTi0.5Ru0.5O3, STRO) is developed as an excellent HER electrocatalyst in a wide pH range. The introduction of Ti into SrRuO3 significantly reduces the size of STRO, endowing with a high reactivity that facilitates a deep surface-reconstruction during HER. Furthermore, Sr2+ leaching triggered reconstruction leads to STRO breaking into tiny nanoparticles accompanied by high-valence ruthenium (Ru) species reducing to metallic Ru. The generated active species, increased accessible sites, and improved electrical conductivity greatly boost HER. The reconstructed STRO displays remarkable HER activities with exceptional low overpotentials of 18, 24, and 55mV at 10mA cm-2 in 1m KOH, 0.5m H2SO4, and 1m PBS, respectively, surpassing most perovskites reported previously and comparable to or even outperforming that of commercial Pt/C. Moreover, the STRO exhibits excellent stabilities over 200h in alkaline and acidic media, superior to that of Pt/C. This work not only provides insights into structure reconstruction of perovskites during HER, but also opens new perspectives for developing high-efficiency and pH-universal electrocatalysts for future energy applications.

Interface construction of crosslinked hyperbranched polyamidoamine between silver layer and poly(meta-phenylene isophthalamide) fiber to enhance conductivity and its resilience to adverse conditions

High-performance silvered poly(meta-phenylene isophthalamide) (PMIA) fiber can be applied in antistatic, conductive, electromagnetic interference shielding and other fields, whereas achieving good interface bonding of silver layer to PMIA matrix remains a grand challenge. This paper studies a method of constructing multi-structure interface between silver layer and PMIA fiber. First, crosslinked hyperbranched polyamidoamine (HPAMAM), referred to as xHP-Qy, was introduced onto PMIA by means of a dip-coating method. Then, the modified fiber was surface metallized by silver ammonia solution and glucose solution to prepare a silver-coated fiber. The structure, morphology and properties of as-prepared materials were characterized systematically. The results show that the final fiber exhibits a low electrical resistivity (5.95 × 10−5 Ω·cm) without sacrificing the thermal stability and mechanical performance of the PMIA itself, which is attributed to the distinct antenna-shaped structure of xHP-Qy between PMIA matrix and Ag layer. In addition, the functionalized composite fiber preserves low resistivity even after subjecting harsh environment and mechanical deformation. The strategy paves a promising avenue for the fabrication of high-performance fiber with enormous potential application for electromagnetic interference shielding, electrical heating and flexible sensor materials.

Adaptive edge prior-based deep attention residual network for low-dose CT image denoising

Improving the diagnostic quality of low-dose CT (LDCT) images relies on effective noise removal. Recent advancements have highlighted the widespread use of deep residual networks for LDCT image denoising. These networks possess properties that aid in preserving image integrity and optimizing model performance. However, the denoising process faces challenges due to the complex patterns and intensity similarities between edge details and lesion regions. To address this issue, this paper introduces a novel approach called the cross-scale attentional residual network (RCANet), which utilizes an adaptive edge prior for LDCT image denoising. The adaptive edge prior enhances the denoising network’s ability to retain image boundary features and uniqueness. To distinguish subtle differences between LDCT image edge details and lesion areas, a cross-scale mapping dual-element module (CMDM) is designed to preserve rich edge texture information during model training. To prevent over-smoothing of denoised results, a compound loss function is proposed, combining MSE loss and multi-scale attention residual perception loss. To validate the effectiveness of the method, experiments were conducted on the AAPM-Mayo Clinic LDCT Grand Challenge dataset. The results demonstrate that RCANet surpasses state-of-the-art residual structure-based network models and performs comparably to leading denoising algorithms.

Oriented construction of lignin-derived carbon hybrid electrocatalyst with superwettable surface and Co-based heterostructure for overall water splitting

Converting lignin waste into carbon hybrid catalysts for water electrolysis engenders a beneficial scenario for both the valorization of waste carbon resources and the reduction of hydrogen production costs, which remains a grand challenge. Hence, we report a novel lignin-derived carbon hybrid electrocatalyst as a low-cost and highly efficient bifunctional electrocatalyst for overall water splitting, prepared by a feasible strategy involving NaCl-assisted pyrolysis and subsequent oxidation-phosphorization. Notably, this strategy can not only regulate the composition of CoP/CoOx nano-heterostructures but also improve the structural properties of porous carbon matrix. Profiting from superwettable surface, ultrafine Co-based heterojunctions and large active area, the optimal CoP/CoOx-HPC catalyst exhibits exceptional bifunctional catalytic activity in 1 M KOH electrolyte, requiring only small overpotentials of 396 and 522 mV for HER and OER, respectively, to achieve a large current density of 400 mA cm−2. Meanwhile, the assembled alkali-electrolyzer using CoP/CoOx-HPC as both cathode and anode needs a relatively low voltage of 2.31 V at 400 mA cm−2, significantly outperforming the commercial couple of Pt/C || RuO2. This work can be of value in the design of the low-cost and highly active lignin-derived electrocatalysts and also serve as guidance for the high-value utilization of lignin or even other biomass wastes.

Intraoral Ultrasound Imaging Using a Rotational Transducer with Periodontal Feature Identification by Machine Learning.

Innovative intraoral ultrasound devices with smart artificial intelligence-based identification for dento-anatomy could provide crucial information for oral health diagnosis and treatment and shed light on real-time detection of developmental dentistry. However, the grand challenge is that the current ultrasound technologies are meant for external use due to their bulkiness and low frequency. We report a compact versatile ultrasound intraoral device that consists of a rotational probe head robustly pivoted around a hand-held and portable handle for real-time imaging of intraoral anatomy using high-frequency ultrasonography (up to 25 MHz). The intraoral ultrasound device that could be adjusted for various orientations of the imaging planes by rotating the head provides real-time, high-resolution ultrasonograms of intraoral structures, including dento-periodontium of most tooth types and maxillary palate. Machine learning-based algorithms are integrated to automate the identification of important structures, including alveolar bone and cementum-enamel junction. The intraoral ultrasound device smartened with artificial intelligence could innovate oral health diagnosis and treatment plans toward precision health and patient care.

Innovative advances for neglected tropical disease (NTD): A global perspective from intellectual property

This study examines innovative advances related to neglected tropical diseases (NTDs), which have severe health, social, and economic consequences that affect more than one billion people worldwide. It addresses the 2030 Agenda for the Sustainable Development Goals in tackling some of the “Grand Challenges,” such as dengue fever (DF), one of the primary and re-emerging diseases that have advanced worldwide in recent years. One of the challenges is the requirement that high investments in innovation can be rationalized by the potential returns sustained by the intellectual property protection system. This study answers the question: “How have dengue-related innovations been approached globally from an intellectual property perspective?” Seeking to shed light on innovative advances in NTD, this exploratory study adopted a (i) longitudinal approach, assessing the global impact of DF, and (ii) historical analysis from the intellectual property perspective, seeking the effectiveness of commercially viable solutions against the disease. An extensive search was conducted in international patent databases using Questel® Orbit Intelligence software to address the proposed objective. This study selected 2307 invention patent families (FamPat) from 1993 to 2023, of which nearly half have expired or been revoked. China is the largest jurisdiction, with 415 FamPat filings. However, many regions with a high global burden of DF lack intellectual protection for DF-related innovations. Although the number of Fampat is relatively high for DF, and the interaction between universities, government, and private companies shows the potential development of an innovation ecosystem, few technological solutions are available. This study concludes that the capacity to absorb knowledge influences the effectiveness of making a technology available as a commercial product.

Construction of Highly Porous and Robust Hydrogen‐Bonded Organic Framework for High‐Capacity Clean Energy Gas Storage

Development of highly porous and robust HOFs for high‐pressure methane and hydrogen storage remains a grand challenge due to the fragile nature of hydrogen bonds. Herein, we report a strategy of constructing double‐walled framework to target highly porous and robust HOF (ZJU‐HOF‐5a) for extraordinary CH4 and H2 storage. ZJU‐HOF‐5a features a minimized twofold interpenetration with double‐walled structure, in which multiple supramolecular interactions are existed between the interpenetrated walls. This structural configuration can notably enhance the framework robustness while maintaining its high porosity, affording one of the highest gravimetric and volumetric surface areas of 3102 m2 g−1 and 1976 m2 cm−3 among the reported HOFs so far. ZJU‐HOF‐5a exhibits an extremely high volumetric H2 uptake of 43.6 g L−1 at 77 K/100 bar and working capacity of 41.3 g L−1 under combined swing conditions, and also impressive methane storage performance with a 5−100 bar working capacity of 187 (or 159) cm3 cm−3 at 270 K (or 296 K). SCXRD studies on CH4‐loaded ZJU‐HOF‐5a reveal that abundant supramolecular binding sites combined with ultrahigh porosities account for its high CH4 storage capacities. Combined with high stability, super‐hydrophobicity, and easy‐recovery, ZJU‐HOF‐5a is placed among the most promising materials for H2 and CH4 storage applications.

Ionic Liquid-Accelerated Growth of Covalent Organic Frameworks with Tunable Layer-Stacking.

Layer-stacking behaviors are crucial for two-dimensional covalent organic frameworks (2D COFs) to define their pore structure, physicochemical properties, and functional output. So far, fine control over the stacking mode without complex procedures remains a grand challenge. Herein, we proposed a "key-cylinder lock mimic" strategy to synthesize 2D COFs with a tunable layer-stacking mode by taking advantage of ionic liquids (ILs). The staggered (AB) stacking (unlocked) COFs were exclusively obtained by incorporating ILs of symmetric polarity and matching molecular size; otherwise, commonly reported eclipsed (AA) stacking (locked) COFs were observed instead. Mechanistic study revealed that AB stacking was induced by a confined interlocking effect (CIE) brought by anions and bulky cations of the ILs inside pores ("key" and "cylinder", respectively). Excitingly, this strategy can speed up production rate of crystalline powders (e.g., COF-TAPT-Tf@BmimTf2N in merely 30 minutes) under mild reaction conditions. This work highlights the enabling role of ILs to tailor the layer stacking of 2D COFs and promotes further exploration of their stacking mode-dependant applications.