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Industrial process heat decarbonization through electrification could contribute significantly to climate change mitigation efforts. In the US industry, thermal processes accounted for more than two-thirds of the total final energy demand in 2021. Cross-cutting electrification technologies like industrial heat pumps are suitable for the process heat supply to several industrial unit operations in a sustainable way while also improving overall energy efficiency. This study employs a bottom-up approach to investigate the techno-enviro-economic potentials of deploying high-temperature and steam-generating heat pumps in US textile, pulp and paper, and automotive sectors in different timeframes. The results show that the annual technical potential energy and CO2 savings by electrifying heat supply are 310 PJ (or 36% of the projected energy demand) and 28 MtCO2 (or 71% of the projected CO2 emissions) in 2050 respectively, however, these incur additional costs in each sector (ranging between 5 and 18 $/GJ). The required heating capacity of industrial heat pumps is estimated at 15 GW, which translates roughly into a market of over 6000 heat pump units and an investment volume of $7 billion in the studied processes. Although there may be individual cost-effective opportunities for electrifying heat supply in specific industrial sites, the overall costs are estimated to be high in the three industrial sectors due to the large disparity between electricity and natural gas prices and low heat source temperatures. To overcome the identified techno-economic barriers, comprehensive action plans for different stakeholders are also given. This study provides novel insights that should inform policymakers’ and executives’ decisions about the electrification of the current and future US industrial heat supply in relevant industrial sectors.

The development of highly specific biomimetic recognition material is a challenge for rapid detection of harmful residues in foodstuff. In this study, a paper-based boronate affinity metal-organic framework/molecularly imprinted polymer microfluidic chip (FZS-BA@MIP) was constructed based on the in situ construction strategy, which was also designed as a highly specific biomimetic recognition module. Here, the homogeneous zeolitic imidazole framework-8 (ZIF-8) membrane served as a great scaffold and enrichment layer. Besides, the recognition layer of MIP was prepared based on a highly oriented boronate affinity surface imprinting strategy. With the aid of the liquid flow channel, the highly specific enrichment and visual detection for antibiotic residues like kanamycin in actual products were achieved on the paper chip module of an integrated lateral flow platform. The whole analysis process could be accomplished within 30 min. In brief, this study offered a new integrated biomimetic recognition platform for visually detecting harmful veterinary residues containing cis-diols, which demonstrated promising commercial value in point-of-care testing of foodborne hazardous compounds.

Loop-mediated isothermal amplification (LAMP) has been widely used to detect many infectious diseases. However, minor inconveniences during the steps of adding reaction ingredients and lack of simple color results hinder point-of-care detection. We therefore invented a fluorometric paper-based LAMP by incorporating LAMP reagents, including a biotinylated primer, onto a cellulose membrane paper, with a simple DNA fluorescent dye incubation that demonstrated rapid and accurate results parallel to quantitative polymerase chain reaction (qPCR) methods. This technology allows for instant paper strip detection of methicillin-resistant Staphylococcus aureus (MRSA) in the laboratory and clinical samples. MRSA represents a major public health problem as it can cause infections in different parts of the human body and yet is resistant to commonly used antibiotics. In this study, we optimized LAMP reaction ingredients and incubation conditions following a central composite design (CCD) that yielded the shortest reaction time with high sensitivity. These CCD components and conditions were used to construct the paper-based LAMP reaction by immobilizing the biotinylated primer and the rest of the LAMP reagents to produce the ready-to-use MRSA diagnostic device. Our paper-based LAMP device could detect as low as 10 ag (equivalent to 1 copy) of the MRSA gene mecA within 36-43 min, was evaluated using both laboratory (individual cultures of MRSA and non-MRSA bacteria) and clinical blood samples to be 100% specific and sensitive compared to qPCR results, and had 35 day stability under 25 °C storage. Furthermore, the color readout allows for quantitation of MRSA copies. Hence, this device is applicable for point-of-care MRSA detection.

In the present article we report a new hybrid microfluidic device (hyFlow) comprising a disposable paper electrode and a three-dimensional (3D) printed plastic chip for the electrochemical detection of a magnetic bead-silver nanoparticle (MB-AgNP) bioconjugate. This hybrid device evolved due to the difficulty of incorporating micron-scale MBs into paper-only fluidic devices. Specifically, paper fluidic devices can entrap MB-containing conjugates within their cellulose or nitrocellulose fiber matrix. The hyFlow system was designed to minimize such issues and transport MB conjugates more efficiently to the electrochemical detection zone of the device. The hyFlow system retains the benefit of fluid transport by pressure-driven flow, however, no pump is required for its operation. The hyFlow device is capable of detecting either pre-formed MB-AgNP conjugates or conjugates formed in situ. The detection limit of AgNPs using this device is 12 pM, which represents just 22 AgNPs per MB.

Paper-based microfluidic devices are an attractive platform for developing low-cost, point-of-care diagnostic tools. As paper-based devices' detection chemistries become more complex, more complicated devices are required, often entailing the sequential delivery of different liquids or reagents to reaction zones. Most research into flow control has been focused on introducing delays. However, delaying the flow can be problematic due to increased evaporation leading to sample loss. We report the use of a CO2 laser to uniformly etch the surface of the paper to modify wicking speeds in paper-based microfluidic devices. This technique can produce both wicking speed increases of up to 1.1× faster and decreases of up to 0.9× slower. Wicking speeds can be further enhanced by etching both sides of the paper, resulting in wicking 1.3× faster than unetched channels. Channels with lengthwise laser-etched grooves were also compared to uniformly etched channels, with the most heavily grooved channels wicking 1.9× faster than the fastest double-sided etched channels. Furthermore, sealing both sides of the channel in packing tape results in the most heavily etched channels, single-sided, double-sided, and grooved, wicking over 13× faster than unetched channels. By selectively etching individual channels, different combinations of sequential fluid delivery can be obtained without altering any channel geometry. Laser etching is a simple process that can be integrated into the patterning of the device and requires no additional materials or chemicals, enabling greater flow control for paper-based microfluidic devices.

This paper introduces a novel software framework to support automated development of paper-based microuidic devices. Compared to existing lab-on-a-chip technologies, paper-based microuidics difiers in terms of substrate technologies and point-of-care usage across a wide variety environmental conditions. This paper addresses the contexts in which the software can address these challenges and presents several initial case studies that demonstrate the capabilities of the framework to produce workable and usable paper microuidic devices.

Paper microfluidic devices (including lateral flow assays) offer an excellent combination of utility and low cost. Many paper microfluidic devices are fabricated using the Xerox ColorQube line of commercial wax-based color printers; the wax ink serves as a hydrophobic barrier to fluid flow. These printers are capable of depositing four different colors of ink, cyan (C), magenta (M), yellow (Y), and black (K), plus 11 combinations of these colors (CM, CY, CK, MY, MK, YK, CMY, CMK, CYK, MYK, and CMYK), although most researchers use only black ink to print paper microfluidic devices. Recently, as part of a project to develop a computer-aided design framework for use with paper microfluidics devices, we unexpectedly observed that different colors of wax ink behave differently in paper microfluidics. We found that among the single colors of ink, black ink actually had the most barrier failures, and magenta ink had the fewest barrier failures. In addition, some combinations of colors performed even better than magenta: the combinations CY, MK, YK, CMY, CYK and MYK had no barrier failures in our study. We also found that the printer delivers significantly different amounts of ink to the paper for the different color combinations, and in general, the color combinations that formed the strongest barriers to fluid flow were the ones that had the most ink delivered to the paper. This suggests that by simply weighing paper samples printed with all 15 combinations of colors, one can easily find the color combinations most likely to form a strong barrier for a given printer. Finally, to show that deliberate choices of ink colors can actually be used to create new functions in paper microfluidics, we designed and tested a new color-based "antifuse" structure that protects paper microfluidic devices from a typical operator error (addition of too much fluid to the device). Our results provide a set of color choice guidelines that designers can use to control the behavior of their paper microfluidics.

In this paper, we present an experimental study on measurement of the thermal conductivity and heat pipe effect in both hydrophilic and hydrophobic (Toray TGP-H60) carbon papers (around 200 μm thickness) with/out liquid water. An experimental setup is developed for measuring thermal conductance at different liquid water contents and temperatures without dissembling the testing device for water addition. Theoretical analysis is also performed to evaluate the apparent conductance of heat pipe effect. We found that liquid water presence inside these materials increases the overall thermal conductivity. At high temperature around 80 °C, the heat pipe effect is evident for the hydrophilic paper; while for the hydrophobic one, the heat pipe effect is found to be smaller. The distinction is likely due to the different patterns of the capillary liquid flow in the two media. For the hydrophobic paper, liquid water flows back to the evaporation side when the breakthrough pressure is reached and flow is through preferred routes of small flow resistance. As a result, heat pipe effect is active only in part of the medium, therefore smaller than that in the hydrophilic one. The results are important for understanding the heat transfer phenomena occurring in porous media and effects of material surface property. © 2012 Elsevier Ltd. All rights reserved.

In this paper, we present an experimental study on measurement of the thermal conductivity and heat pipe effect in both hydrophilic and hydrophobic (Toray TGP-H60) carbon papers (around 200 μm thickness) with/out liquid water. An experimental setup is developed for measuring thermal conductance at different liquid water contents and temperatures without dissembling the testing device for water addition. Theoretical analysis is also performed to evaluate the apparent conductance of heat pipe effect. We found that liquid water presence inside these materials increases the overall thermal conductivity. At high temperature around 80 °C, the heat pipe effect is evident for the hydrophilic paper; while for the hydrophobic one, the heat pipe effect is found to be smaller. The distinction is likely due to the different patterns of the capillary liquid flow in the two media. For the hydrophobic paper, liquid water flows back to the evaporation side when the breakthrough pressure is reached and flow is through preferred routes of small flow resistance. As a result, heat pipe effect is active only in part of the medium, therefore smaller than that in the hydrophilic one. The results are important for understanding the heat transfer phenomena occurring in porous media and effects of material surface property. © 2012 Elsevier Ltd. All rights reserved.

Electrospinning, a process that converts a solution or melt droplet into an ejected jet under a high electric field, is a well-established technique to produce one-dimensional (1D) fibers or two-dimensional (2D) randomly arranged fibrous meshes. Nevertheless, the direct electrospinning of fibers into controllable three-dimensional (3D) architectures is still a nascent technology. Here, we apply near-field electrospinning (NFES) to directly write arbitrarily shaped 3D structures through consistent and spatially controlled fiber-by-fiber stacking of polyvinylidene fluoride (PVDF) fibers. An element central to the success of this 3D electrospinning is the use of a printing paper placed on the grounded conductive plate and acting as a fiber collector. Once deposited on the paper, residual solvents from near-field electrospun fibers can infiltrate the paper substrate, enhancing the charge transfer between the deposited fibers and the ground plate via the fibrous network within the paper. Such charge transfer grounds the deposited fibers and turns them into locally fabricated electrical poles, which attract subsequent in-flight fibers to deposit in a self-aligned manner on top of each other. This process enables the design and controlled fabrication of electrospun 3D structures such as grids, walls, hollow cylinders, and other 3D logos. As such, this technique has the potential to advance the existing electrospinning technologies in constructing 3D structures for biomedical, microelectronics, and MEMS/NMES applications.

Sub-1 nm, extremely long nickel molybdate nanowires are synthesized based on a good/poor solvent system. The ultrathin nanowires can be hierarchically assembled into flexible, free-standing films with good mechanical properties. Compared with the large-size counterpart, nickel molybdate ultrathin nanowires display promising oxygen evolution reaction catalytic performance derived from the ultrathin feature.

Heart attack is mainly caused by the rupture of a vulnerable plaque. IVUS-OCT is a novel medical imaging modality that provides opportunities for accurate assessment of vulnerable plaques in vivo in patients. IVUS provides deep penetration to image the whole necrotic core while OCT enables accurate measurement of the fibrous cap of a plaque owing to its high resolution. In this paper, the authors describe the fundamentals, the technical designs and the applications of IVUS-OCT technology. Results from cadaver specimens are summarized, which indicated the complementary nature of OCT and IVUS for assessment of vulnerable plaques, plaque composition, and stent-tissue interactions. Furthermore, previously reported in vivo animal experiments are reviewed to assess the clinical adaptability of IVUS-OCT. Future directions for this technology are also discussed in this review.

This comment addresses the work of Wang and Li (DOI: 10.1007/s12293-009-0012-0, hereafter referred to as WL) published online on September 01, 2009 in a (special) issue of Memetic Computing. We respectfully beg to differ in opinion with WL and argue that the concept of multi-strategy evolutionary search used by WL is not particularly novel. Similar ideas have been presented elsewhere, and these publications date back to at least early 2007. For instance, in a series of papers published in PNAS (2007), SSSAJ (2008), WRR (2008), IEEE-TEVC (2009), JH (2010), Vrugt and coworkers have introduced AMALGAM, a multi-method (or ensemble) search approach to solve emerging single and multiple objective search and optimization problems. In the past few years, MATLAB, C++, Python, and R implementations of AMALGAM have been distributed extensively among researchers and practitioners in various fields of study.

A partnership of affluent nations is proposed to pursue the objective of universal electrification in the world with a challenging interim goal of bringing efficient and sustainable[2] 2 For the purposes of this paper sustainable electric systems are those that emit low or no net greenhouse gases (GHGs). This requirement is generally consistent with the goal expressed in the National Research Council report Our Common Journey: a Transition Toward Sustainability [National Research Council, 1999]: "... that the primary goals of transition toward sustainability should be to meet the needs of a much larger but stabilizing population, to sustain the life support systems of the planet and to sustainably reduce hunger and poverty." The authors believe that the electric systems targeted in the universal electrification proposal of this paper can contribute to the goals of the transition explored by the academy. The partners must assure this contribution, but for simplicity the authors use the criterion of low or no net greenhouse gas emissions from electricity generation to qualify a system as sustainable. electric services within 20 years to one billion people. Four plausible partners are the United States, the European Union, Japan with Australia and New Zealand, and, perhaps, the OPEC countries. The partners would provide part of the capital needed for electrification. This "concessionary" contribution should stimulate private investors and/or indigenous governments to supply the remainder of the capital needed, and to organize the management of each electrification project. The concessionary contribution would be designed for two objectives: (1) to help alleviate poverty, grow opportunities, and increase the quality of life in the developing world by providing electric services to all, and (2) to reduce future greenhouse gas emissions by supporting a low-carbon development path toward universal electrification. Paying for the difference in cost between high-efficiency end-use equipment and least-first-cost equipment would pursue the first objective. This concession would significantly lower the cost to the consumer of electricity services. The second objective would be pursued by paying the added cost (up to $ 1000/kW) of low-GHG or climate-friendly electricity generation (i.e., low or no-net carbon-emitting systems) over and above least-first-cost generation technology. This electrification of one billion people would require about 50 GWe of new electric generating capacity assuming 50% capacity factor and 15% line losses. The concessionary investment needed would be up to $50 billion for low-GHG generation plus about $30 billion for efficient end-use equipment. An additional 25% of the concessionary contribution would go for training, program management and evaluation. The total is $ 100 billion (or $ 100/person) spread over 20 years, or about $ 1.25 billion per partner per year assuming an equal share for each partner. The remainder of the capital required is estimated to be about $ 170/person for the electrical system including hook-up, plus another $ 140/person for efficient end-use equipment purchased at the cost of least-first-cost equipment. The consumer would pay back these non-concessionary investments through the price of electricity and through a lease-purchase charge for end-use equipment. Provision of basic electricity services for newly electrified communities is estimated to require about 0.025 kW/person on average for all electric uses including domestic, commercial, agricultural and industrial uses, compared with current electricity services of about 1.8 kW/person in the United States and 0.3 kW/person globally. Thus, electric power per capita in these poor, mostly rural areas would be very small initially, just sufficient to meet basic necessities, and consistent with customers' ability to pay. Not every community would choose or be able to buy even this much, but to have the opportunity for electric services at whatever level will make a huge difference in people's lives. Experience over many decades has shown that electrification can help alleviate poverty, improve health, reduce drudgery, and increase literacy, all contributing to the UN's Millennium Development Goals to which all countries are committed. © 2005 International Energy Initiative, Inc.

Moss et al.(2005) describe, in a recent paper, a filter that they use to detect lines. We noticed that the wavelet on which this filter is based is a difference of uniform filters. This filter is an approximation to the second derivative operator, which is commonly implemented as the Laplace of Gaussian (or Marr-Hildreth) operator (Marr & Hildreth, 1980; Jahne, 2002), Figure 1. We have compared Moss' filter with 1) the Laplace of Gaussian operator, 2) an approximation of the Laplace of Gaussian using uniform filters, and 3) a few common noise reduction filters. The Laplace-like operators detect lines by suppressing image features both larger and smaller than the filter size. The noise reduction filters only suppress image features smaller than the filter size. By estimating the signal to noise ratio (SNR) and mean square difference (MSD) of the filtered results, we found that the filter proposed by Moss et al. does not outperform the Laplace of Gaussian operator. We also found that for images with extreme noise content, line detection filters perform better than the noise reduction filters when trying to enhance line structures. In less extreme cases of noise, the standard noise reduction filters perform significantly better than both the Laplace of Gaussian and Moss' filter.

Abstract: The identification of top quark decays where the top quark has a large momentum transverse to the beam axis, known as top tagging, is a crucial component in many measurements of Standard Model processes and searches for beyond the Standard Model physics at the Large Hadron Collider.Machine learning techniques have improved the performance of top tagging algorithms, but the size of the systematic uncertainties for all proposed algorithms has not been systematically studied.This paper presents the performance of several machine learning based top tagging algorithms on a dataset constructed from simulated proton-proton collision events measured with the ATLAS detector at √ s = 13 TeV.The systematic uncertainties associated with these algorithms are estimated through an approximate procedure that is not meant to be used in a physics analysis, but is appropriate for the level of precision required for this study.The most performant algorithms are found to have the largest uncertainties, motivating the development of methods to reduce these uncertainties without compromising performance.To enable such efforts in the wider scientific community, the datasets used in this paper are made publicly available.

Predictive high-fidelity modeling of wind turbines with computational fluid dynamics, wherein turbine geometry is resolved in an atmospheric boundary layer, is important to understanding complex flow accounting for design strategies and operational phenomena such as blade erosion, pitch-control, stall/vortex-induced vibrations, and aftermarket add-ons. The biggest challenge with high-fidelity modeling is the realization of numerical algorithms that can capture the relevant physics in detail through effective use of high-performance computing. For modern supercomputers, that means relying on GPUs for acceleration. In this paper, we present ExaWind, a GPU-enabled open-source incompressible-flow hybrid-computational fluid dynamics framework, comprising the near-body unstructured grid solver Nalu-Wind, and the off-body block-structured-grid solver AMR-Wind, which are coupled using the Topology Independent Overset Grid Assembler. Turbine simulations employ either a pure Reynolds-averaged Navier–Stokes turbulence model or hybrid turbulence modeling wherein Reynolds-averaged Navier–Stokes is used for near-body flow and large eddy simulation is used for off-body flow. Being two-way coupled through overset grids, the two solvers enable simulation of flows across a huge range of length scales, for example, 10 orders of magnitude going from O(μm) boundary layers along the blades to O(10 km) across a wind farm. In this paper, we describe the numerical algorithms for geometry-resolved turbine simulations in atmospheric boundary layers using ExaWind. We present verification studies using canonical flow problems. Validation studies are presented using megawatt-scale turbines established in literature. Additionally presented are demonstration simulations of a small wind farm under atmospheric inflow with different stability states.

Correction to: Transport in Porous Media (2024) 151:55–84https://doi.org/10.1007/s11242-023-02033-0. There are three numbers in Table 2 of the original paper that were incorrect. Specfically, the value of the density of hydrogen (H2) for the DB model and the values of density and viscosity of H2 for the TOUGH2 model listed in Table 2 of the original paper were incorrect. (Table presented.) Properties of the H2-water upconing system for comparison against the DB model. Property DB model Used for TOUGH2 Gas cap thickness, total reservoir thickness, and radial extent (outer radius) of the reservoir Infinite, infinite, infinite 50 m, 100 m (with open boundary at bottom), 100 m (open boundary condition) Porosity (ϕ) 0.10 0.10 Permeability (kH) 1.0 × 10−12 m2 1.0 × 10−12 m2 Permeability (kV) 1.0 × 10−12 m2 1.0 × 10−12 m2 Relative permeability (krel) Not applicable Linear with Slr = 0.99 Distance from well to H2-water interface (d) 10 m 10 m Extraction rate of rate of H2 (Qm) − 5.5 kg s−1 − 5.5 kg s−1 Density of water 996 kg m−3 996 kg m−3 Density of H2 7.32 kg m−3 7.87 kg m−3 Viscosity of water 6.54 × 10−4 Pa s 5.11 × 10−4 Pa s Viscosity of H2 9.31 × 10−6 Pa s 9.53 × 10−6 Pa s A corrected Table 2 is shown below. The erroneous values in Table 2 were not used in any of the modeling and simulation. Accurate values for density and viscosity in the modeling and simulation come from CoolProp for the DB model and from EOS7CH for the TOUGH2 simulations.

This paper presents the electron and photon energy calibration obtained with the ATLAS detector using 140 fb−1 of LHC proton-proton collision data recorded at √s = 13 TeV between 2015 and 2018. Methods for the measurement of electron and photon energies are outlined, along with the current knowledge of the passive material in front of the ATLAS electromagnetic calorimeter. The energy calibration steps are discussed in detail, with emphasis on the improvements introduced in this paper. The absolute energy scale is set using a large sample of Z-boson decays into electron-positron pairs, and its residual dependence on the electron energy is used for the first time to further constrain systematic uncertainties. The achieved calibration uncertainties are typically 0.05% for electrons from resonant Z-boson decays, 0.4% at ET ∼ 10 GeV, and 0.3% at ET ∼ 1 TeV; for photons at ET ∼ 60 GeV, they are 0.2% on average. This is more than twice as precise as the previous calibration. The new energy calibration is validated using J/ → ee and radiative Z-boson decays.

This paper investigates the maximization of data harvested by an uncrewed aerial vehicle (UAV) that supports Internet of Things (IoT) deployment scenarios. The novelty of the paper is that we study the feasibility of battery-free UAV and IoT device deployment where the UAV is powered by a ground laser source, and the IoT devices are powered by a power beacon via bistatic backscattering. We aim to optimize the UAV trajectory while minimizing the laser energy consumption throughout the entire flight by tuning the laser power and the power beacon radiated temporal power profiles. Upon considering an unspecified flying time, we adopt path discretization and resort to the single-block successive convex approximation (SCA) to solve the data collection maximization problem. In addition to considering the UAV dynamics and power budget, two novel SCA-compatible bounds are introduced for the product of positive mixed convex/concave functions. Finally, the simulation results show that the proposed algorithm increases the data collected under different operation conditions by approximately 90%.

Synchrophasor data contain a trove of information on the power system and its dynamics. These measurements have a high potential to unlock our ability to cope with changing system conditions and challenges posed by distributed and intermittent energy sources. While Phasor Measurement Units (PMUs) have seen a large deployment in the grid, their applications are limited by the software platforms that are deployed in control centers to monitor the grid. In this paper, we present an open source toolkit that enables fast prototyping of PMU applications. The toolkit is akin to a software development kit (SDK) for synchrophasor applications, providing a number of functionalities that enable high-level PMU application development within the LabVIEW environment. This Smart-grid Synchrophasor SDK (S3DK) proposes a paradigm based on the concept of distributed applications, which allows development and deployment to be independent of the existing software stack deployed in control centers and to leverage PMU data at any level of a synchrophasor system hierarchy. This paper serves to introduce the S3DK, which is released as open source software to facilitate broader and fast prototyping of synchrophasor applications.

Although gold nanorods have been the subject of much research, the pathways for controlling their shape and thereby their optical properties remain largely heuristically understood. Although it is apparent that the simultaneous presence of and interaction between various reagents during synthesis control these properties, computational and experimental approaches for exploring the synthesis space can be either intractable or too time-consuming in practice. This motivates an alternative approach leveraging the wealth of synthesis information already embedded in the body of scientific literature by developing tools to extract relevant structured data in an automated, high-throughput manner. To that end, we present an approach using the powerful GPT-3 language model to extract structured multi-step seed-mediated growth procedures and outcomes for gold nanorods from unstructured scientific text. GPT-3 prompt completions are fine-tuned to predict synthesis templates in the form of JSON documents from unstructured text input with an overall accuracy of 86% aggregated by entities and 76% aggregated by papers. The performance is notable, considering the model is performing simultaneous entity recognition and relation extraction. We present a dataset of 11 644 entities extracted from 1137 papers, resulting in 268 papers with at least one complete seed-mediated gold nanorod growth procedure and outcome for a total of 332 complete procedures.

Defossilization of the current energy system is a major requirement to decelerate anthropogenic climate change. However, a defossilized energy system is vastly more complex than current fossil-based energy systems: The integration of distributed energy resources and sector-coupling increases connectivity, demands interdisciplinary workflows, and creates a need for more sophisticated design processes. Inspired by the semiconductor and automotive industries, digitalization of the design process using platform-based design (PBD), coupled with the energy hub concept, can improve cost-effective energy systems design and accelerate the industry's contributions to achieving net-zero emissions. PBD is an efficient and effective methodology to manage and de-risk the complexity of integrated energy system design, leading to affordable and reliable solutions due to the inherent techno-economic analysis underlying the decision-making process. Combining the PBD framework with the energy hub concepts establishes a powerful design workflow for developing holistic energy systems from a single building up to the district and city scales. The fundamental tenets of this workflow, as discussed in this paper, are (1) the separation of functions from architectures, (2) the identification of abstraction levels at which systems can be analyzed and optimized, and (3) the ability to repurpose components at all levels of abstraction to aid design reuse and allow performance feedback at every stage of the process. We argue that PBD can become the next frontier in energy system design. PBD, as presented in this paper, is not limited to the energy sector, and it can also be a sub-process of an even more holistic infrastructure design. Spatial planning, architecture, and civil engineering can all be further integrated with the PBD concept, allowing societies to reach ambitious sustainability goals faster, at lower cost, and with greater resilience.

This paper examines building energy flexibility at an aggregated level and addresses the main barriers and research gaps for the development of this resource across three design and development phases: market and policy, early planning and design, and operation. We review methodologies and tools and discuss barriers, challenges, and opportunities, incorporating policy, economic, technical, professional, and social perspectives. Although various legal and regulatory frameworks exist to foster the development of energy flexibility for small buildings, financing mechanisms are limited with a significant number of perceived risks undermining private investment. For the early planning and design phase, planners and designers lack appropriate tools and face interoperability challenges, which often results in insufficient consideration of demand response programs. The review of the operational phase highlighted the socio-technical challenges related to both the complexity of deployment and communication, as well as privacy and acceptability issues. Finally, the paper proposes a number of targeted research directions to address challenges and promote greater energy flexibility deployments, including capturing building demand side dynamics, improving baseline estimations and developing seamless connectivity between buildings and districts.

Correction to: Nature Energy. Published online 25 May 2023. This paper was originally published under a standard Springer Nature license (© The Author(s), under exclusive licence to Springer Nature Limited). It is now available as an open-access paper under a Creative Commons Attribution 4.0 International license, © The Author(s). The error has been corrected in the online version of the article.

K-12 schools are the largest energy consumers in the public sector, with their HVAC energy consumption representing the largest portion of their total energy use. While transitioning these schools to grid-interactive HVAC system operation through advanced controls offers significant financial and environmental benefits, and model predictive control (MPC) has been identified as a promising solution to achieve that, very few MPCs are affordable and have been deployed in K-12 schools. This situation raises concerns about the unclear real-world benefits of MPC technology among facility managers and industries. To address this gap, this paper presents a low-cost MPC solution that requires minimal control infrastructure costs and a unique field demonstration at a K-12 school, conducted for both cooling and heating seasons. This work adopted a previously developed MPC and extended it for use in the school application. The MPC aims to coordinate multiple packaged units to eliminate unnecessary peaks and shift cooling or heating loads in response to grid signals based on load conditions, while maintaining thermostat temperatures within school-defined bounds. Throughout the field tests, the MPC achieved a 24% reduction in peak demand during the cooling season and shifted cooling or heating loads by up to 16% in response to the school's utility tariff, considering load conditions, while also allowing end-users to override thermostat setpoints. The paper also discusses the limitations of this study and future research directions for better performance of the MPC at K-12 schools.

This paper introduces the District Optimization Testing (DOPTEST) concept, which naturally extends from the Building Optimization Testing (BOPTEST) framework, for simulation-based testing of advanced control strategies in districts. While the focus of the BOPTEST framework is on individual building control, DOPTEST is meant to assess system integration strategies at a district level. This paper lays down the design requirements and modeling methodology for district emulators in DOPTEST and shows a simulation example of its first test case prototype.

As part of the Snowmass'21 community planning excercise, the Advanced Accelerator Concepts (AAC) community proposed future linear colliders with center-of-mass energies up to 15 TeV and luminosities up to 50 × 1034 cm-2 s-1 in a compact footprint. In addition to being compact, these machines must also be energy efficient. We identify two challenges that must be addressed in the design of these machines. First, the Beam Delivery System (BDS) must not add significant length to the accelerator complex. Second, beam parameters must be chosen to mitigate beamstrahlung effects and maximize the luminosity-per-power of the machine. In this paper, we review advances in plasma lens technology that will help to reduce the length of the BDS system and we detail new Particle-in-Cell simulation studies that will provide insight into beamstrahlung mitigation techniques. We apply our analysis to both e + e - and γγ colliders. The challenges and solutions described in this paper are considered independently. A unified, self-consistent concept for a BDS system for a 15 TeV linear collider will be the subject of future work.

In this paper, a state-space method for double-beam systems with variable cross sections is developed, making it possible to calculate the transverse vibration of the double-beams accurately and effectively. Due to the variability in the double-beam cross sections with the viscoelastic interlayer in between, the governing equations of vibration for the systems become highly coupled partial differential equations, making the problem difficult to solve. A basic double-beam system is introduced to modify the original governing equations to two inhomogeneous differential equations. Given the separation of variables, several mode-shape coefficients and a state variable are defined to construct the state-space equations. The coupling terms and variables are transferred into the constant coefficient matrix of the state-space equations, decoupling them. Numerical procedures are presented to solve the state-space equations to obtain homogenous and inhomogeneous solutions, including the natural frequencies and mode shapes in free vibration and the dynamic responses in forced vibration, respectively. The method has substantial advantages in decoupling high-order partial differential equations and can be further extended to solve complex structural systems. Numerical results also demonstrate that the method is accurate and efficient. Finally, an engineering application with a rail-bridge with a floating slab track is discussed in detail with the method.

The ozone oxidation, or ozonation, of thiosulfate is an important reaction for wastewater processing, where it is used for remediation of mining effluents, and for studying aerosol chemistry, where its fast reaction rate makes it an excellent model reaction. Although thiosulfate ozonation has been studied since the 1950s, challenges remain in developing a realistic reaction mechanism that can satisfactorily account for all observed products with a sequence of elementary reaction steps. Here, we present novel measurements using trapped microdroplets to study the pH-dependent thiosulfate ozonation kinetics. We detect known products and intermediates, including SO32-, SO42-, S3O62-, and S4O62-, establishing agreement with the literature. However, we identify S2O42- as a new reaction intermediate and find that the currently accepted mechanism does not directly explain observed pH effects. Thus, we develop a new mechanism, which incorporates S2O42- as an intermediate and uses elementary steps to explain the pH dependence of thiosulfate ozonation. The proposed mechanism is tested using a kinetic model benchmarked to the experiments presented here, then compared to literature data. We demonstrate good agreement between the proposed thiosulfate ozonation mechanism and experiments, suggesting that the insights in this paper can be leveraged in wastewater treatment and in understanding potential climate impacts.

This paper presents a machine learning-based airfoil shape optimization framework designed to increase flutter resistance and reduce drag. Using the National Advisory Committee for Aeronautics airfoil as the base design and a Hicks–Henne bump function, we employ multi-objective Bayesian optimization and harmonic balance-based flutter prediction. The optimization process yields a Pareto front revealing trade-off relationships between the flutter speed index and drag coefficient. The optimized airfoils, resembling those of evolved marine animals, outperform the base design in terms of flutter resistance and drag. These results demonstrate the framework's potential to enhance aircraft performance and safety by addressing aeroelastic factors.

Abstract: Pixel sensors in 3D technology equip the outer ends of the staves of the Insertable B Layer (IBL), the innermost layer of the ATLAS Pixel Detector, which was installed before the start of LHC Run 2 in 2015. 3D pixel sensors are expected to exhibit more tolerance to radiation damage and are the technology of choice for the innermost layer in the ATLAS tracker upgrade for the HL-LHC programme. While the LHC has delivered an integrated luminosity of  ≃ 235 fb-1 since the start of Run 2, the 3D sensors have received a non-ionising energy deposition corresponding to a fluence of ≃ 8.5 × 1014 1 MeV neutron-equivalent cm-2 averaged over the sensor area. This paper presents results of measurements of the 3D pixel sensors' response during Run 2 and the first two years of Run 3, with predictions of its evolution until the end of Run 3 in 2025. Data are compared with radiation damage simulations, based on detailed maps of the electric field in the Si substrate, at various fluence levels and bias voltage values. These results illustrate the potential of 3D technology for pixel applications in high-radiation environments.

Examining coupled renewable-battery power plants (“hybrids”) in congested areas provides insights into a future of increased wind and solar penetration. Our study focuses on two types of congested regions, Variable Renewable Energy (VRE)-rich Areas and Load Centers, and explores likely plant configuration choices for developers and transmission network planners. This paper examines how hybrid value, comprising energy and capacity value, varies by plant configuration and congested region type considering factors such as storage duration, battery degradation, and ability to charge from the grid. We select plant locations from across the seven main U.S. independent system operators (ISOs). Hybrid value for each configuration is computed based on profit-maximizing plant operation given perfect foresight, according to observed wholesale power market real time prices from 2018 to 2021. In VRE-rich Areas, the median increase in energy value from extending storage duration from one to 4 h is 29.4 % for solar and 26.8 % for wind, assuming low battery degradation costs and storage sized to 100 % of the plant's nameplate generation capacity. Increasing storage duration beyond 4 h does not substantially increase its value from energy markets, even in VRE-rich Areas. We find that solar hybrids reach a 90 % capacity credit with 4 h of storage, while wind hybrids require 8 h of storage, based on the capacity factor of each hybrid during the top 100 net load hours.

In materials science, chiral perovskites stand out due to their exceptional optoelectronic properties and the versatility in their structure and composition, positioning them as crucial in the advances of technologies in spintronics and chiroptical systems. This review underlines the critical role of synthesizing and growing these materials, a process integral to leveraging their complex interplay between structural chirality and distinctive optoelectronic properties, including chiral-induced spin selectivity and chiroptical activity. The paper offers a comprehensive summary and discussion of the methods used in the synthesis and growth of chiral perovskites, delving into extensive growth techniques, fundamental mechanisms, and strategic approaches for the engineering of low-dimensional perovskites, alongside the creation of novel chiral ligands. The necessity of developing new synthetic approaches and maintaining precise control during the growth of chiral perovskites is emphasized, aiming to enhance their structural chirality and boost their efficiency in spin and chiroptical selectivity.

Akin to the impact that digital microelectronics had on electronic devices for information technology, digital microfluidics (DMF) was anticipated to transform fluidic devices for lab-on-a-chip (LoC) applications. However, despite a wealth of research and publications, electrowetting-on-dielectric (EWOD) DMF has not achieved the anticipated wide adoption, and commercialization has been painfully slow. By identifying the technological and resource hurdles in developing DMF chip and control systems as the culprit, we envision democratizing DMF by building a standardized design and manufacturing platform. To achieve this vision, we introduce a proof-of-concept cloud platform that empowers any user to design, obtain, and operate DMF chips (https://edroplets.org). For chip design, we establish a web-based EWOD chip design platform with layout rules and automated wire routing. For chip manufacturing, we build a web-based EWOD chip manufacturing platform and fabricate four types of EWOD chips (i.e., glass, paper, PCB, and TFT) to demonstrate the foundry service workflow. For chip control, we introduce a compact EWOD control system along with web-based operating software. Although industrial fabrication services are beyond the scope of this work, we hope this perspective will inspire academic and commercial stakeholders to join the initiative toward a DMF ecosystem for the masses.

This paper details the design and preliminary demonstrations for a compact climbing robot named EEWOC (Extended-reach Enhanced Wheeled Orb for Climbing). This novel platform utilizes the EEMMMa limb (Elastic Extending Mechanism for Mobility and Manipulation), detailed in previous work. This highly extendable and bendable robotic limb utilizes a unique tape spring structure for long reach in a small, lightweight package. EEWOC combines this limb with additional degrees of freedom and two magnetic grippers to allow it to ascend vertical metal surfaces by consecutively extending and gripping. It is also equipped with wheels for horizontal mobility. A key advantage of this system is EEWOC's ability to bend to place its grippers around obstacles or corners and above ledges. The prototype is small and lightweight, with a profile of 25x30x30 cm and weight of 1.8 kg, while able to extend its limb up to 1.2 m away. With versatile movement options, EEWOC has the potential to fully traverse large metal structures such as ships or buildings for use in inspection tasks. This paper presents a detailed view of the overall system and mechanism design, and successful climbing demonstrations are shown on steel structures. The paper concludes by detailing EEWOC's future capabilities and additional tests and theories needed to refine its maneuvers and control.

Building and microgrid designs with highly-distributed electrical storage have potential advantages over today's conventional topologies with centralized storage. This paper studies the capital cost benefits of several residential behind-the-meter distributed-storage topologies, including AC and DC versions of systems with load-packaged batteries and resilient sub-networks. The study begins by defining the block configuration of each topology. This work then develops a model for the cost of the power electronics necessary to interface with the storage elements. Finally, the analysis develops a model for the total cost of each storage topology, incorporating the installation and soft costs. The results suggest that while the cost of power electronics is lower in centralized topologies, the total cost is lower for distributed storage due to the avoided costs of installation and permitting. This paper also explores the benefits of load-packaged batteries for savings in electrical infrastructure.

This paper investigates the energy performance and sizing criteria of 5th generation district heating and cooling (5GDHC) networks as a function of the heat distribution temperature in the building systems connected to the district network. An energy simulation model of a 5GDHC network was developed in Modelica for a case study located in Denmark. Calculations were carried out for four different building heating systems. Simulation results show that reducing the heat distribution temperatures from 70 °C to 23 °C leads to around 40% annual electric energy savings (from 10.4 kWh/m2 to 6.2 kWh/m2) for the operation of the heat pumps. Heat distribution temperatures of 23 °C cause higher water mass flow rates through the network, leading to annual electric energy consumption for the circulation pumps that are almost doubled (from 0.16 kWh/m2 to 0.3 kWh/m2) compared to the reference case at 70 °C. Furthermore, the paper discusses how the results obtained from the Danish case study can be generalized and applied to other cases using a simplified mathematical approach. It is found that about 1.5% of electric energy savings can be achieved for each temperature degree reduction in the heat distribution system.

With the wide adoption of building automation system, and the advancement of data, sensing, and machine learning techniques, data-driven fault detection and diagnostics (FDD) for building heating, ventilation, and air conditioning systems has gained increasing attention. In this paper, data-driven FDD is defined as those that are built or trained from data via machine learning or multivariate statistical analysis methods. Following this definition, this paper reviews and summarizes the literature on data-driven FDD from three aspects: process, systems studied (including the systems being investigated, the faults being identified, and the associated data sources), and evaluation metrics. A data-driven FDD process is further divided into the following steps: data collection, data cleansing, data preprocessing, baseline establishment, fault detection, fault diagnostics, and potential fault prognostics. Literature reported data-driven methods used in each step of an FDD process are firstly discussed. Applications of data-driven FDD in various HVAC systems/components and commonly used data source for FDD development are reviewed secondly, followed by a summary of typical metrics for evaluating FDD methods. Finally, this literature review concludes that despite the promising performance reported in the literature, data-driven FDD methods still face many challenges, such as real-building deployment, performance evaluation and benchmarking, scalability and transferability, interpretability, cyber security and data privacy, user experience, etc. Addressing these challenges is critical for a broad real-building adoption of data-driven FDD.

ObjectiveWe performed a scoping review of algorithms using electronic health record (EHR) data to identify patients with Alzheimer's disease and related dementias (ADRD), to advance their use in research and clinical care.Materials and methodsStarting with a previous scoping review of EHR phenotypes, we performed a cumulative update (April 2020 through March 1, 2023) using Pubmed, PheKB, and expert review with exclusive focus on ADRD identification. We included algorithms using EHR data alone or in combination with non-EHR data and characterized whether they identified patients at high risk of or with a current diagnosis of ADRD.ResultsFor our cumulative focused update, we reviewed 271 titles meeting our search criteria, 49 abstracts, and 26 full text papers. We identified 8 articles from the original systematic review, 8 from our new search, and 4 recommended by an expert. We identified 20 papers describing 19 unique EHR phenotypes for ADRD: 7 algorithms identifying patients with diagnosed dementia and 12 algorithms identifying patients at high risk of dementia that prioritize sensitivity over specificity. Reference standards range from only using other EHR data to in-person cognitive screening.ConclusionA variety of EHR-based phenotypes are available for use in identifying populations with or at high-risk of developing ADRD. This review provides comparative detail to aid in choosing the best algorithm for research, clinical care, and population health projects based on the use case and available data. Future research may further improve the design and use of algorithms by considering EHR data provenance.

Axially loaded piles in liquefiable soils can undergo severe settlement due to an earthquake event. During shaking, the settlement is caused by the decreased shaft and tip capacity from excess pore pressures (ue) generated around the pile. Post shaking, soil settlement from the reconsolidation of liquefied soil surrounding the pile results in the development of additional load (known as drag load), causing downdrag settlement of the pile. Estimating the axial load distribution and pile settlement is essential for designing and evaluating the performance of axially loaded piles in liquefiable soils. In practice, a simplified neutral plane solution method is used, where the liquefied soils are modeled as a consolidating layer without considering the effect of ue generation/dissipation. A TzQzLiq analysis models the load and settlement response of axially loaded piles in liquefiable soils by accounting for the effect of excess pore pressure (ue) generation/dissipation on the shaft and tip capacity. This paper presents the deficiencies of the simplified neutral plane method in predicting the drag load as well as the downdrag settlement by comparing it with the TzQzLiq analysis validated with hypergravity model tests. The results show that the drag load and the downdrag settlement predicted by the neutral plane method might be over- or under-estimated depending on the pile load, the rate of ue dissipation, and the soil settlement. For the cases studied, it was found that most of the pile settlement occurs during shaking due to the decrease in the pile's tip resistance from the development of ue in the soil surrounding it. While large drag loads develop during reconsolidation, the resulting downdrag settlement is small. While the neutral plane method generally predicted a downdrag settlement comparable to that of the TzQzLiq analysis, it overpredicted drag load and could not predict co-seismic settlement. Finally, the study advocates for the development and use of a displacement-based procedure (accounting for all the mechanisms occurring during and after an earthquake event) such as based on TzQzLiq analysis in accurately evaluating the performance of the pile (i.e., the pile settlement and the maximum load), thus providing an overall safe, efficient, and optimized design.