End-user Applications Research Articles

Preparation, physicochemical analyses, and comparative evaluation studies of deep eutectic solvents (DES) from non-conventional precursor materials applied as catalytic curing agents for epoxy resin

Curing agents for epoxy resins play a significant role in initiating crosslinking reactions resulting in the hardened and durable material with enhanced properties as per end-user applications. The industrial world still lacks efficient, environmentally friendly, and cheap curing agents for epoxy resins. Here, we report the development of deep eutectic solvents (DESs) based on N, N’-Bis(2-aminoethyl)ethane-1,2-diamine (commonly known as triethylenetetramine, TETA) as hydrogen bond donor (HBD), and salicylic acid (SA) and choline chloride (CC) as hydrogen bond acceptor (HBA). The formulated DESs are suggested to be relatively safe, affordable, and eco-friendly and the active groups in the DESs are capable of initiating crosslinking reactions. The physical stability of the formulated deep eutectic solvents was examined by polarizing optical microscopic (POM) analysis, which confirmed the absence of any crystals or residues. The purity was ensured by 13C nmr spectroscopy. Additionally, supportive evidence for the hydrogen bond formation between the constituents was gained from the outcomes of FTIR, 1H nmr, and mass spectrometry. Deep depression in melting points was confirmed from DSC studies, and TGA suggested better thermal stability for DESs than its constituents. Density functional theory (DFT) at B3LYP6-31G* of Gaussian computational framework was applied to energetically optimize the isolated structure and determine the bond lengths in angstroms. The structural characteristics and nonbonding interactions of DESs were investigated by the same. In addition, the density and rheological characteristics were assessed for the ideal utilization of the formulated DESs as epoxy resin curing agents. The detailed comparative studies demonstrated the advantages of deep eutectic solvents over conventional curing agents. The efficacy of the formulations as a curing agent was primarily confirmed from FTIR analysis and the curing behavior was investigated by DSC studies.

Uranium-zirconium hydride nuclear fuel performance in the NaK-cooled MARVEL microreactor

The Microreactor Applications Research Validation and EvaLuation (MARVEL) Project is producing a high temperature NaK-cooled nuclear reactor test bed at the Idaho National Laboratory to ultimately improve integration of microreactors to end-user applications. This ambitious effort seeks to design, authorize, construct, test, and operate the reactor within five years. The computational software package chosen for high fidelity fuel performance modeling, BISON, has been upgraded to model the performance of the MARVEL reactor's selected fuel system: 304 stainless steel-clad U-ZrHx. In this manuscript, we begin by describing the MARVEL fuel element. The known properties and relationships of the fuel element constituents are recapitulated from data available in the literature. These historical relationships assume a particular U-ZrH microstructure which previously was not well-understood; therefore, the fuel's microstructure is experimentally confirmed using electron microscopy and X-ray diffraction. This information is then used to analyze the thermomechanical performance of the MARVEL fuel element during a hypothetical extreme accident scenario (a loss of flow accident during a loss of coolant pressure accident) using the nuclear fuel performance code BISON. The analysis shows that the primary phenomenon which could compromise the structural integrity of the MARVEL fuel element is the same as for TRIGA reactors: high temperature internal gas overpressurization-induced rupture of the cladding arising from excessive hoop stresses. Despite modeling the most severe MARVEL accident scenario, the highest anticipated temperature in the fuel, about 718 °C, results in hoop stresses of less than 10 MPa. Based on this analysis, a conservative steady state fuel meat temperature limit of 900 °C precludes cladding damage. The newly incorporated fuel performance simulation capabilities developed in this work may also be extended to model this fuel system under other conditions or reactors.

Techno-economic and life cycle analysis of synthetic natural gas production from low-carbon H2 and point-source or atmospheric CO2 in the United States

Synthetic natural gas (SNG) is of great interest in reducing fossil energy consumption while maintaining compatibility with existing NG infrastructure and end-use applications equipment. SNG can be produced using clean H2 generated from renewable or nuclear energy and CO2 captured from stationary sources or the atmosphere. In this study, we develop an engineering process model of SNG production using Aspen Plus® and production scales reported by the industry. We examine the levelized cost and life cycle greenhouse gas (GHG) emissions of SNG production under various CO2 supply scenarios. Considering the higher cost of H2 transportation compared with CO2 transportation, we assume that CO2 feedstock is transported via pipeline to the H2 production location, which is collocated with the SNG plant. We also evaluate the cost of CO2 captured from the atmosphere, assuming the direct air capture process can occur near the SNG facility. Depending on the CO2 supply chain, the levelized cost of SNG is estimated to be in the range of $45–76 per million British thermal units (MMBtu) on a higher heating value (HHV) basis. The SNG production cost may be reduced to $27–57/MMBtu-HHV by applying a tax credit available in the United States for low-carbon H2 production (45 V). With a lower electricity price of 3ȼ/kWh for water electrolysis and accounting for a 45 V tax credit, the SNG cost reaches parity with the cost of fossil NG. Depending on the CO2 supply chain, SNG can reduce life cycle GHG emissions by 52–88 % compared with fossil NG.

High-performance cementitious composites containing nanostructured carbon additives made from charred coal fines.

Carbon-based nanomaterials, such as carbon nanoplatelets, graphene oxide, and carbon quantum dots, have many possible end-use applications due to their ability to impart unique mechanical, electrical, thermal, and optical properties to cement composites. Despite this potential, these materials are rarely used in the construction industry due to high material costs and limited data on performance and durability. In this study, domestic coal is used to fabricate low-cost carbon nanomaterials that can be used economically in cement formulations. A range of chemical and physical processing approaches are employed to control the size, morphology, and chemical functionalization of the carbon nanomaterial, which improves its miscibility with cement formulations and its impact on mechanical properties and durability. At loadings of 0.01 to 0.07wt.% of coal-derived carbon nanomaterial, the compressive and flexural strength of cement samples are enhanced by 24% and 23%, respectively, in comparison to neat cement. At loadings of 0.02 to 0.06wt.%, the compressive and flexural strength of concrete composites increases by 28% and 21%, respectively, in comparison to neat samples. Additionally, the carbon nanomaterial additives studied in this work reduce cement porosity by 36%, permeability by 86%, and chloride penetration depth by 60%. These results illustrate that low-loadings of coal-derived carbon nanomaterial additives can improve the mechanical properties, durability, and corrosion resistance of cement composites.

OPTORER: A Dynamic Routing and Touring Service for Indoors and Outdoor Tours.

This paper introduces a new routing and touring service both for outdoor and indoor places of touristic and cultural interest designed to be used in the wider area of Attica, Greece. This service is the result of the work performed in OPTORER (OPTORER: OPtimal rouTing and explOration of touRistic and cultural arEas of interest within Attica given personalized adaptive preferences, promoted underlying purpose, and interactive experience), project, and it aspires to offer a range of innovative and thematic routes to several specified points of interest in the selected area of Attica, encouraging the combination of indoor and outdoor routes in a single tour. The aim is to optimize the user experience while promoting specific, user-centric features, with safety and social welfare being a priority for every designed tour, resulting in enhancing the touristic experience in the area. Using a common smartphone device, as well as common wearable devices (i.e., smartwatches), the OPTORER service will provide an end-to-end solution by developing the algorithms and end-user applications, together with an orchestration platform responsible for managing, operating, and executing the service that produces and presents to the end user results derived from solving dynamically complex optimization problems.

Finishing of additively manufactured stainless steel features by the eco-friendly electrolyte: Plasma and surface interaction science

Many industries, including the chemical, food, medical, and aerospace sectors, use complex-shaped components fabricated by additive manufacturing. The usage of additively manufactured components in end-user applications is challenging due to their higher surface roughness. Finishing processes are necessary to reduce the surface roughness. Electrolytic hot plasma polishing is a newly developed finishing process. For this process, an eco-friendly electrolytic solution is prepared and used to finish any shape of the component that is to be conductive. Salts and distilled water are used while preparing the eco-friendly electrolytic solution, which is used to finish stainless steel. Initially, the prepared solution was used to finish cast stainless steel components, and subsequently, it was employed for finishing additively manufactured stainless steel components. Voltage–current characteristics of the cast and additively manufactured components resulted in a similar pattern. As a result, the range of the input process parameters for cast components was chosen for the input of central composite rotatable design to finish the additively manufactured stainless steel components. The surface roughness of additively manufactured stainless steel features was reduced to 95.6 % of the original surface roughness of 12.45 μm. Scanning electron microscopy was used to examine surface morphology and material removal mechanisms.

Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings.

Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or a sequence of images to recognize content, has been used extensively across industries in recent years. However, in the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV has the potential to improve patient monitoring, and system efficiencies, while reducing workload. In contrast to previous reviews, we focus on the end-user applications of CV. First, we briefly review and categorize CV applications in other industries (job enhancement, surveillance and monitoring, automation, and augmented reality). We then review the developments of CV in the hospital setting, outpatient, and community settings. The recent advances in monitoring delirium, pain and sedation, patient deterioration, mechanical ventilation, mobility, patient safety, surgical applications, quantification of workload in the hospital, and monitoring for patient events outside the hospital are highlighted. To identify opportunities for future applications, we also completed journey mapping at different system levels. Lastly, we discuss the privacy, safety, and ethical considerations associated with CV and outline processes in algorithm development and testing that limit CV expansion in healthcare. This comprehensive review highlights CV applications and ideas for its expanded use in healthcare.

Strain shift measured from stress-controlled oscillatory shear: Evidence for a continuous yielding transition and new techniques to determine recovery rheology measures

Understanding the yielding of complex fluids is an important rheological challenge that affects our ability to engineer and process materials for a wide variety of applications. Common theoretical understandings of yield stress fluids follow the Oldroyd–Prager formalism in which the material behavior below the yield stress is treated as solidlike, and above the yield stress as liquidlike, with an instantaneous transition between the two states. This formalism was built on a quasi-static approach to the yield stress, while most applications, ranging from material processing to end user applications, involve a transient approach to yielding over a finite timescale. Using stress-controlled oscillatory shear experiments, we show that yield stress fluids flow below their yield stresses. This is quantified through measuring the strain shift, which is the value about which the strain oscillates during a stress-controlled test and is a function of only the unrecoverable strain. Measurements of the strain shift are, therefore, measurements of flow having taken place. These experimental results are compared to the Herschel–Bulkley form of the Saramito model, which utilizes the Oldroyd–Prager formalism, and the recently published Kamani–Donley–Rogers (KDR) model, in which one constitutive equation represents the entire range of material responses. Scaling relationships are derived, which allow us to show why yield stress fluids will flow across all stresses, above and below their yield stress. Finally, derivations are presented that show strain shift can be used to determine average metrics previously attainable only through recovery rheology, and these are experimentally verified.

Carbon abatement cost evolution in the forthcoming hydrogen valleys by following different hydrogen pathways

The need to develop a forthcoming hydrogen economy emphasises the emerging concept of ‘hydrogen valleys', where hydrogen production and consumption are being developed. This study assesses the Levelized Cost of Hydrogen (LCOH) and Carbon Abatement Cost (CAC) for various hydrogen end-use applications within the context of a hydrogen valley located in the southern Italy over different time horizons (Today 2023, Mid-Term 2030, Long-Term 2050). Examined applications include blending into the gas grid, reconversion into electricity for grid balancing by means of fuel cells, hydrogen refuelling stations for fuel cell electric vehicles (FCEVs), synthetic methane production, and electro-fuel synthesis. Employing a parametric approach, the study compares LCOH and decarbonisation costs across different pathways. Results indicate current LCOH production values of 3.66–4.90 €/kgH2, projected to decrease to 1.41–1.94 €/kgH2 by 2050. Decarbonisation cost analysis identifies blending and FCEV scenarios as the most cost-effective, contrasting with Power-to-Power scenarios, particularly in the mid and long terms.

Assessing the Emergence and Evolution of Artificial Intelligence and Machine Learning Research in Neuroradiology.

Interest in artificial intelligence (AI) and machine learning (ML) has been growing in neuroradiology, but there is limited knowledge on how this interest has manifested into research and specifically, its qualities and characteristics. This study aims to characterize the emergence and evolution of AI/ML articles within neuroradiology and provide a comprehensive overview of the trends, challenges, and future directions of the field. We performed a bibliometric analysis of the American Journal of Neuroradiology; the journal was queried for original research articles published since inception (January 1, 1980) to December 3, 2022 that contained any of the following key terms: "machine learning," "artificial intelligence," "radiomics," "deep learning," "neural network," "generative adversarial network," "object detection," or "natural language processing." Articles were screened by 2 independent reviewers, and categorized into statistical modeling (type 1), AI/ML development (type 2), both representing developmental research work but without a direct clinical integration, or end-user application (type 3), which is the closest surrogate of potential AI/ML integration into day-to-day practice. To better understand the limiting factors to type 3 articles being published, we analyzed type 2 articles as they should represent the precursor work leading to type 3. A total of 182 articles were identified with 79% being nonintegration focused (type 1 n = 53, type 2 n = 90) and 21% (n = 39) being type 3. The total number of articles published grew roughly 5-fold in the last 5 years, with the nonintegration focused articles mainly driving this growth. Additionally, a minority of type 2 articles addressed bias (22%) and explainability (16%). These articles were primarily led by radiologists (63%), with most (60%) having additional postgraduate degrees. AI/ML publications have been rapidly increasing in neuroradiology with only a minority of this growth being attributable to end-user application. Areas identified for improvement include enhancing the quality of type 2 articles, namely external validation, and addressing both bias and explainability. These results ultimately provide authors, editors, clinicians, and policymakers important insights to promote a shift toward integrating practical AI/ML solutions in neuroradiology.

Hemp cultivation opportunities for marginal lands development.

Agricultural diversification and high-quality products deriving from sustainable crops such as hemp can represent a solution to revitalize marginal areas and reverse land abandonment. This study aimed at comparing four different hemp cultivars (Carmagnola Selezionata, "CS"; Futura 75, "FUT"; Felina 32, "FEL"; Secuieni Jubileu, "JUB") to provide information to select the best suited cultivar for cultivation in mountain marginal areas and for specific end-use applications. Hemp cultivars were cultivated in a single experimental field to compare their ecological and agronomic behavior (duration of life cycle phases, plant size and biomass allocation, and plant resource-use strategies). Through metabolomic analysis of both vegetative and reproductive parts of the plants we tested the presence of substances of nutraceutical interest and traced seed nutritional profile. The four cultivars had different ecological and agronomic behavior, and nutritional profile. We found several compounds with potential pharmaceutical and nutraceutical values in all parts of the plant (leaves, inflorescences, and stems). JUB resulted the most suitable for seed production while CS showed the highest content of bioactive compounds in flowers and leaves. FUT, showed the best suitability for multi-purpose cultivation, while FEL seemed to be not appropriate for the cultivation in mountain area. The multi-disciplinary approach we adopted was effective in distinguish across hemp cultivars and provided information to farmers for the selection of the best hemp cultivar to select. Hemp had a high potential for cultivation in marginal lands, demonstrating to be an economic resource due to its multi-purpose use and to the possibility to generate high-added values products. Our results could serve as a stimulus for the reintroduction of this culture in the study area and in other similar environments.

Review of Cell-Balancing Schemes for Electric Vehicle Battery Management Systems

The battery pack is at the heart of electric vehicles, and lithium-ion cells are preferred because of their high power density, long life, high energy density, and viability for usage in relatively high and low temperatures. Lithium-ion batteries are negatively affected by overvoltage, undervoltage, thermal runaway, and cell voltage imbalance. The minimisation of cell imbalance is particularly important because it causes uneven power dissipation by each cell and, hence, temperature distribution that adversely impacts the battery lifetime. Several papers in the literature proposed advanced cell-balancing techniques to increase the effectiveness of basic cell-balancing approaches, reduce power losses, and reduce the number of components in balancing circuits. The new developments and optimisations over the last few years have been particularly intense due to the increased interest in battery technologies for several end-use applications. This paper reviews and discusses recent cell-balancing techniques or methods, covering their operating principles and the optimised utilisation of electrical components.

Injury prevention for women and girls playing Australian Football: programme cocreation, dissemination and early adopter coach feedback

BackgroundAdherence to injury prevention programmes may improve with greater end-user involvement and application of implementation frameworks during development. We describe the cocreation, initial dissemination and feedback from programme early adopters...

A review of IoT security and privacy using the Blockchain to Boost Data Integrity and Privacy

The Internet of Things (IoT) is an emerging technology. It enables users to exchange their data and resources by linking devices across the IoT network, resulting in a more relaxed and connected lifestyle. It allows real-time data to be delivered to end-user applications and systems via sensing, actuation, communication, computing, networking, and storage. It can transfer data across a network without requiring human intervention or computer-to-computer interaction. It makes it possible to detect the various sensors around the world through low-priced sensor devices. IoT Networking defines communication or inter-connection between Peer-to-Peer (P2P) either through a connected medium (Wired) or unconnected medium (Wireless) to procure a secure transmission of data. Blockchain is a peer-to-peer network where peers can communicate and do transactions without the need for centralized authority. It allows data to be stored and transferred in a safe and interconnected method. This section includes a literature review on the centralized IOT system model, highlighting the limitations of the centralized IoT system and blockchain security in IoT. This research study's systematic evaluation is processed based on the reviews of the literature available with recent advancements and inventions through patents, articles, conference papers, and journals. Also, the related work with IoT systems for data privacy and security issues and applying Blockchain technology to IOT are discussed.

Energy harvesting from plants using hybrid microbial fuel cells; potential applications and future exploitation.

Microbial Fuel Cells (MFC) can be fuelled using biomass derived from dead plant material and can operate on plant produced chemicals such as sugars, carbohydrates, polysaccharides and cellulose, as well as being "fed" on a regular diet of primary biomass from plants or algae. An even closer relationship can exist if algae (e.g., prokaryotic microalgae or eukaryotic and unicellular algae) can colonise the open to air cathode chambers of MFCs driving photosynthesis, producing a high redox gradient due to the oxygenic phase of collective algal cells. The hybrid system is symbiotic; the conditions within the cathodic chamber favour the growth of microalgae whilst the increased redox and production of oxygen by the algae, favour a more powerful cathode giving a higher maximum voltage and power to the photo-microbial fuel cell, which can ultimately be harvested for a range of end-user applications. MFCs can utilise a wide range of plant derived materials including detritus, plant composts, rhizodeposits, root exudates, dead or dying macro- or microalgae, via Soil-based Microbial Fuel Cells, Sediment Microbial Fuel Cells, Plant-based microbial fuel cells, floating artificial islands and constructed artificial wetlands. This review provides a perspective on this aspect of the technology as yet another attribute of the benevolent Bioelectrochemical Systems.

A review of IoT security and privacy using the Blockchain to Boost Data Integrity and Privacy

The Internet of Things (IoT) is an emerging technology. It enables users to exchange their data and resources by linking devices across the IoT network, resulting in a more relaxed and connected lifestyle. It allows real-time data to be delivered to end-user applications and systems via sensing, actuation, communication, computing, networking, and storage. It can transfer data across a network without requiring human intervention or computer-to-computer interaction. It makes it possible to detect the various sensors around the world through low-priced sensor devices. IoT Networking defines communication or inter-connection between Peer-to-Peer (P2P) either through a connected medium (Wired) or unconnected medium (Wireless) to procure a secure transmission of data. Blockchain is a peer-to-peer network where peers can communicate and do transactions without the need for centralized authority. It allows data to be stored and transferred in a safe and interconnected method. This section includes a literature review on the centralized IOT system model, highlighting the limitations of the centralized IoT system and blockchain security in IoT. This research study's systematic evaluation is processed based on the reviews of the literature available with recent advancements and inventions through patents, articles, conference papers, and journals. Also, the related work with IoT systems for data privacy and security issues and applying Blockchain technology to IOT are discussed.

Multi-scale assessment of global warming mitigation potential of anaerobic digestion for food waste management in the United States: A comparison of Life Cycle Assessment approaches

This study provides a holistic Life Cycle Assessment (LCA) for evaluating the decarbonization potential of using Anaerobic Digestion (AD) in food waste treatment. The LCA is based on the development of a new flexible tool that allows the integration of the LCA at the process level with geospatial data and therefore includes a wide range of factors such as multiple feedstocks, technologies for end-use applications, and facility locations. This tool estimates a potential for reducing 21.8 MMtons CO2eq/y (345 gCO2eq/kgwaste) and generating 161 TBTU/y of renewable natural gas for the whole United States by managing food waste based on waste-hierarchy. This study also identifies those states with a more efficient AD implementation potential regarding CO2eq avoidance. It highlights the importance of including region-specific conditions since feedstock diversion depends on the state and is the main contributor to the decarbonization potential, up to 60 % of the avoided emissions.

Flashback propensity due to hydrogen blending in natural gas: Sensitivity to operating and geometrical parameters

Hydrogen has emerged as a promising option for promoting decarbonization in various sectors by serving as a replacement for natural gas while retaining the combustion-based conversion system. However, its higher reactivity compared to natural gas introduces a significant risk of flashback. This study investigates the impact of operating and geometry parameters on flashback phenomena in multi-slit burners fed with hydrogen-methane-air mixtures. For this purpose, transient numerical simulations, which take into account conjugate heat transfer between the fluid and the solid walls, are coupled with stochastic sensitivity analysis based on Generalized Polynomial Chaos. This allows deriving comprehensive maps of flashback velocities and burner temperatures within the parameter space of hydrogen content, equivalence ratio, and slit width, using a limited number of numerical simulations. Moreover, we assess the influence of different parameters and their interactions on flashback propensity. The ranges we investigate encompass highly H2-enriched lean mixtures, ranging from 80% to 100% H2 by volume, with equivalence ratios ranging from 0.5 to 1.0. We also consider slit widths that are typically encountered in burners for end-user devices, ranging from 0.5mm to 1.2mm. The study highlights the dominant role of preferential diffusion in affecting flashback physics and propensity as parameters vary, including significant enrichment close to the burner plate due to the Soret effect. These findings hold promise for driving the design and optimization of perforated burners, enabling their safe and efficient operation in practical end-user applications.

Investigating the influence of oriented external electric fields on modulating spin-transition temperatures in Fe(II) SCO complexes: a theoretical perspective.

Spin-crossover complexes, valued for their bistability, are extensively studied due to their numerous potential applications. A primary challenge in this molecular class is to identify effective methods to adjust the spin-transition temperature, which frequently falls outside the desired temperature range. This typically necessitates intricate chemical design and synthesis or the use of stimuli such as light or pressure, each introducing its own set of challenges for integrating these molecules into end-user applications. In this work, we aim to address this challenge using an oriented external electric field (OEEF) as one stimulus to modulate the spin-transition temperatures. For this purpose, we have employed both periodic and non-periodic calculations on three well-characterised Fe(II) SCO complexes, namely [Fe(phen)2(NCS)2] (1, phen = 1,10-phenanthroline), [Fe(bt)2(NCS)2] (2, bt = 2,2'-bi-2-thiazoline) and [Fe(py)2phen(NCS)2] (3, py = pyridine) possessing a similar structural motif of {FeN4N'2}. To begin with, DFT calculations employing the TPSSh functional were performed on complexes 1 to 3, and the estimated low-spin (LS) and high-spin (HS) gaps are 24.6, 15.3 and 15.4 kJ mol-1, and these are in the range expected for Fe(II) SCO complexes. In the next step, an OEEF was applied in the molecule along the pseudo-C2 axis that bisects two coordinated -NCS groups. Application of an OEEF was found to increase the Fe-ligand bond length and found to affect the spin-transition at the particular applied OEEF. While the HS state of 1 becomes the ground state at an applied field of 0.514 V Å-1, the LS state lies at a higher energy of 1.3 kJ mol-1. Similarly, complexes 2 and 3 also show the HS ground state at an applied field of 0.514 V Å-1, where the LS state stays at higher energies of 6.13 and 11.62 kJ mol-1, respectively. It is found that the overall change in enthalpy (ΔHHL) and entropy (ΔSHL) for the spin transition in the presence of OEEFs decreases upon increasing the strength of the applied field. The computed spin-transition temperature (T1/2) using DFT was found to be in close agreement with the experimentally reported values. It is estimated that on increasing the strength of the applied electric field, the T1/2 increases significantly. While the DFT computed T1/2 values for the optimised geometry of 1, 2 and 3 were found to be 134.6 K, 159.9 K and 111.4 K respectively, at the applied field of 0.6425 V Å-1T1/2 increases up to 187.3 K, 211.0 K and 184.4 K respectively, unveiling an hitherto unknown strategy to tune the T1/2 values. A limited benchmarking was performed with five additional exchange-correlation functionals: PBE, BLYP, B3LYP*, B3LYP, and PBE0. These functionals were found to be unsuitable for predicting the correct SCO behaviour for complex 2, and their behaviour under various electric fields did not improve. This emphasises the importance of choosing the correct functional at zero OEEF prior to testing them under various electric fields. Furthermore, calculations were performed with complex 1 adsorbed on the Au(111) surface. The formation of an Au-S bond during adsorption significantly stabilises the low-spin (LS) state, hindering the observation of spin-crossover (SCO) behaviour. Nonetheless, the application of an OEEF reduces this gap and brings the T1/2 value closer to the desired temperature. This offers a novel post-fabrication strategy for attaining SCO properties at the interface.

DITTO: A Visual Digital Twin for Interventions and Temporal Treatment Outcomes in Head and Neck Cancer.

Digital twin models are of high interest to Head and Neck Cancer (HNC) oncologists, who have to navigate a series of complex treatment decisions that weigh the efficacy of tumor control against toxicity and mortality risks. Evaluating individual risk profiles necessitates a deeper understanding of the interplay between different factors such as patient health, spatial tumor location and spread, and risk of subsequent toxicities that can not be adequately captured through simple heuristics. To support clinicians in better understanding tradeoffs when deciding on treatment courses, we developed DITTO, a digital-twin and visual computing system that allows clinicians to analyze detailed risk profiles for each patient, and decide on a treatment plan. DITTO relies on a sequential Deep Reinforcement Learning digital twin (DT) to deliver personalized risk of both long-term and short-term disease outcome and toxicity risk for HNC patients. Based on a participatory collaborative design alongside oncologists, we also implement several visual explainability methods to promote clinical trust and encourage healthy skepticism when using our system. We evaluate the efficacy of DITTO through quantitative evaluation of performance and case studies with qualitative feedback. Finally, we discuss design lessons for developing clinical visual XAI applications for clinical end users.