Two-photon polymerization (2PP) is becoming increasingly established as additive manufacturing technology for microfabrication due to its high-resolution and the feasibility of generating complex parts. Until now, the high resolution of 2PP is also its bottleneck, as it limited throughput and therefore restricted the application to the production of microparts. Thus, mechanical properties of 2PP materials can only be characterized using nonstandardized specialized microtesting methods. Due to recent advances in 2PP technology, it is now possible to produce parts in the size of several millimeters to even centimeters, finally permitting the fabrication of macrosized testing specimens. Besides suitable hardware systems, 2PP materials exhibiting favorable mechanical properties that allow printing of up-scaled parts are strongly demanded. In this work, the up-scalability of three different photopolymers is investigated using a high-throughput 2PP system and low numerical aperture optics. Testing specimens in the cm-range are produced and tested with common or even standardized material testing methods available in conventionally equipped polymer testing labs. Examples of the characterization of mechanical, thermo-mechanical, and fracture properties of 2PP processed materials are shown. Additionally, aspects such as postprocessing and aging are investigated. This lays a foundation for future expansion of the 2PP technology to broader industrial application.

Simultaneous achievement of lightweight, high strength, large fracture strain, and high damping capability has been challenging because some of these mechanical properties are mutually exclusive. Here, we have utilized self-assembled polymeric carbon precursor materials in combination with scalable nanoimprinting lithography to produce nanoporous carbon nanopillars. The produced amorphous carbon nanopillars exhibited ultrahigh strength similar to or even higher than one tenth of their Young's modulus (E), the most widely used approximation of a fundamental upper limit of material breaking strength. Remarkably, nanoporosity induced via sacrificial template significantly reduced the mass density of amorphous carbon to 0.66~0.82g/cm3 while the strength of E/10 is still maintained. Moreover, these nanopillars displayed both elastic and plastic behavior with large fracture strain. A reversible part of the sp2-to-sp3 transition produces large elastic strain and a high loss factor (up to 0.033) comparable to Ni-Ti shape memory alloys. The irreversible part of the sp2-to-sp3 transition enables plastic deformation, leading to a large fracture strain of up to 35%. These findings have been substantiated using simulation studies. None of the existing structural materials exhibit a comparable combination of density, strength, deformability, and damping capability. Hence, the results of this study illustrate the potential of both dense and nanoporous amorphous carbon materials as superior structural nanomaterials.

A thorough knowledge and understanding of the structure–property relationship between thermal conductivity and C-fiber morphology is important to estimate the behavior of carbon fiber components, especially under thermal loading. In this paper, the thermal conductivities of different carbon fibers with varying tensile modulus were analyzed perpendicular and parallel to the fiber direction. Besides the measurement of carbon fiber reinforced polymers, we also measured the thermal conductivity of single carbon fibers directly. The measurements clearly proved that the thermal conductivity increased with the tensile modulus both in fiber and perpendicular direction. The increase is most pronounced in fiber direction. We ascribed the increase in tensile modules and thermal conductivity to increasing anisotropy resulting from the orientation of graphitic domains and microvoids. Graphical abstract

Plastic Upcycling One innovative way to cool structures is by transforming plastic waste into passive cooling foils. This process is called upcycling, and it helps reduce plastic waste, promotes a more sustainable future, and contributes to a cyclic polymer economy. In article 2300444, Qimeng Song, Markus Retsch, and co-workers demonstrate how chip bags can be repurposed, showcasing the potential of upcycling.

Studies on mechanical size effects in nanosized metals unanimously highlight both intrinsic microstructures and extrinsic dimensions for understanding size-dependent properties, commonly focusing on strengths of uniform microstructures, e.g., single-crystalline/nanocrystalline and nanoporous, as a function of pillar diameters, D. We developed a hydrogel infusion-based additive manufacturing (AM) technique using two-photon lithography to produce metals in prescribed 3D-shapes with ∼100 nm feature resolution. We demonstrate hierarchical microstructures of as-AM-fabricated Ni nanopillars (D ∼ 130-330 nm) to be nanoporous and nanocrystalline, with d ∼ 30-50 nm nanograins subtending each ligament in bamboo-like arrangements and pores with critical dimensions comparable to d. In situ nanocompression experiments unveil their yield strengths, σ, to be ∼1-3 GPa, above single-crystalline/nanocrystalline counterparts in the D range, a weak size dependence, σ ∝ D-0.2, and localized-to-homogenized transition in deformation modes mediated by nanoporosity, uncovered by molecular dynamics simulations. This work highlights hierarchical microstructures on mechanical response in nanosized metals and suggests small-scale engineering opportunities through AM-enabled microstructures.

Plastic pollution has caused numerous environmental issues in recent decades. As one of the most commonly used packaging materials, aluminum-plastic laminates (APL) are particularly challenging for recycling purposes due to their sophisticated materials components. This work reveals a new strategy to upcycle such post-consumer APL packaging waste, e.g., chips-bags, for passive daytime cooling applications. This opens an attractive route to reuse APLs while at the same time reducing global energy consumption and carbon emissions. The mirror-like appearance of the APLs possesses a strong solar reflection, up to 86%. By coating, this reflective layer of the APL waste with a high emissive polydimethylsiloxane layer, a simple but effective passive daytime cooling foil is constructed, which shows promising passive cooling performance theoretically and practically. More importantly, the passive cooling foil based on APL waste is flexible and can be applied to any target object, protecting it from harsh sunlight. The low-cost APL waste-based passive cooling foil proposed in this work will significantly contribute to both energy and environmental issues that humans face today.

Materials with an extremely low thermal and high electrical conductivity that are easy to process, foldable, and nonflammable are required for sustainable applications, notably in energy converters, miniaturized electronics, and high-temperature fuel cells. Given the inherent correlation between high thermal and high electrical conductivity, innovative design concepts that decouple phonon and electron transport are necessary. We achieved this unique combination of thermal conductivity 19.8 ± 7.8 mW/m/K (cross-plane) and 31.8 ± 11.8 mW/m/K (in-plane); electrical conductivity 4.2 S/cm in-plane in electrospun nonwovens comprising carbon as the matrix and silicon-based ceramics as nano-sized inclusions with a sea-island nanostructure. The carbon phase modulates electronic transport for high electrical conductivity, and the ceramic phase induces phonon scattering for low thermal conductivity by excessive boundary scattering. Our strategy can be used to fabricate the unique nonwoven materials for real-world applications and will inspire the design of materials made from carbon and ceramic.

Smart, responsive materials are required in various advanced applications ranging from anti-counterfeiting to autonomous sensing. Colloidal crystals are a versatile material class for optically based sensing applications owing to their photonic stopband. A careful combination of materials synthesis and colloidal mesostructure rendered such systems helpful in responding to stimuli such as gases, humidity, or temperature. Here, an approach is demonstrated to simultaneously and independently measure the time and temperature solely based on the inherent material properties of complex colloidal crystal mixtures. An array of colloidal crystals, each featuring unique film formation kinetics, is fabricated. Combined with machine learning-enabled image analysis, the colloidal crystal arrays can autonomously record isothermal heating events - readout proceeds by acquiring photographs of the applied sensor using a standard smartphone camera. The concept shows how the progressing use of machine learning in materials science has the potential to allow non-classical forms of data acquisition and evaluation. This can provide novel insights into multiparameter systems and simplify applications of novel materials.

With ongoing miniaturization and weight reduction of portable electronic devices, effective heat dissipation is essential to inhibit malfunctions and premature failure. The application of fillers in a polymer matrix enhances the thermal conductivity of lightweight materials but impedes recyclability. All-polyethylene (all-PE) materials represent a sustainable and easy-to-recycle single-material alternative, whereby high and tunable thermal conductivity is provided by process-induced hierarchical PE nanostructures. Essential for this type of composite-free high-performance material is the specific PE composition containing high amounts of ultra-high molecular weight PE that form ultrastrong extended-chain nanostructures induced by shear and elongational flow during processing. This results in self-reinforcing fibre-like shish-kebab nanostructures with a high thermal conductivity parallel to the extended PE chains. Extrusion-based 3D printing enables tuning the orientation of the PE nanostructure to tailor the orientation and magnitude of the thermal conductivity. Thus, this material class highlights the possibility of combining digitally programmable heat management in 3D printed materials with sustainable material concepts. An all-polyethylene (PE) material that crystallizes into a hierarchical shish-kebab structure is introduced. The microstructure translates into a high and anisotropic thermal conductivity that can be controlled by fused-filament fabrication.

District energy systems (DES) can reduce CO2 emissions associated with buildings while meeting the energy needs of a group of buildings with fossil fuel or renewable energy resources that are located on-site. One of the present challenges of DES is optimizing the operation of energy components, as different optimization methods are available. These optimization methods can have various requirements for implementation, distinct needs for engineering labor, and may rely on freely accessible software or proprietary software. Most importantly, different methods may result in dissimilar operation planning for a given DES, which makes the selection of optimization method a key consideration for decision-makers. In this study, two optimization methods, a mixed-integer linear programming (MILP) solver as a classical method and a non-dominated sorting genetic algorithm II (NSGA-II) as a metaheuristic method, are used to optimize the early-stage operation planning of a hypothetical DES for a university campus in a cool and dry climate. The objective is to minimize the operating cost and CO2 emissions when considering uncertainties in energy demands, solar irradiance, wind speed, and annualized electricity-related emissions. Both methods present similar operation of energy components, operating cost, and operating CO2 emissions. The MILP solver and NSGA-II algorithm vary in computation time to perform the optimization, initial knowledge to run the simulation, accessibility (free/open-source status), and satisfaction of constraints. This work compares the characteristics of a MILP solver and NSGA-II algorithm to help future researchers select the suitable optimization method related to their case study. The software underlying this work is open-source and publicly available to be reused and customized for early-stage operation planning of their specific DES. This work is novel by optimizing the operation planning of a mixed-used DES to minimize the cost and CO2 emissions while considering uncertainties in weather parameters, energy demands, and annualized electricity-related emissions.

Ultralight highly porous sponges are attractive for electronic devices due to superelasticity, outstanding resilience, and thermal insulation. However, fabricating an ultralight conductive sponge with low thermal conductivity, mechanical flexibility, and piezoresistivity, as well as adjustable heating behavior, is still a challenge. Here, an ultralight carbon nanofibrous sponge fabricated by pyrolyzing a graphene oxide coated polyimide sponge is reported. The resulting carbon sponge demonstrates a high electrical conductivity of 0.03-4.72 S m-1 and a low thermal conductivity of 0.027-0.038 W m-1 K-1 (20 °C, in ambient air), as well as a low density to ∼6 mg cm-3. Additionally, the sponge exhibits mechanical flexibility, stability, excellent piezoresistivity, and an adjustable heating behavior. Hence, it could be utilized as a sensing device, including thermal management, making them promising for use in smart sportswear, human-machine interfaces, and wearable healthcare devices.

Many advanced materials consist of fibers. They are used as nonwovens, fabrics, or in composite materials. Characterization of individual fibers allows us to predict resulting material properties. We present a measurement setup and analysis software to characterize individual, micrometer-sized fibers fast and reliably. The setup is based on the lock-in thermography principle. Thermal diffusivity values of seven reference samples agree very well with previously reported values. We use our setup to investigate critical measurement parameters like excitation frequency, excitation power, pixel size, and fiber orientation. Our results show that fibers with subpixel diameters can be measured even if they are not aligned. However, special care has to be taken to choose an adequate excitation power. Measurements at high intensities can underestimate thermal diffusivity even though the raw data looks reasonable. By automatically measuring at different excitation powers, our setup solves this issue.

Passive daytime cooling materials can lower global energy consumption owing to their autonomous cooling capability. Although a significant number of passive cooling materials have been developed recently, their performance characterization is still challenging. Field tests experience high variability due to uncontrollable changes in environmental conditions. Here, we design an indoor setup to characterize the performance of passive cooling materials reproducibly and independently of weather and season. Outdoor measurement conditions are approximated using a liquid-nitrogen-cooled aluminum dome, a solar simulator, and a wavelength-selective inverse sky-window filter. In contrast to outdoor measurements, the results of various reference materials show remarkable precision and repeatability. Additionally, the impact of solar light intensity and temperature on the passive cooling performance can be experimentally investigated. Our setup is a first step in the development of a standardized test method to bring accuracy, reproducibility, and comparability to the emerging field of passive cooling materials.

Gonococcal infections represent an urgent public-health threat as >50% of cases caused by Neisseria gonorrhoeae strains display reduced susceptibility to at least one antimicrobial agent. We evaluated the pharmacodynamics of a number of antimicrobials against N. gonorrhoeae in order to assess the likelihood of mutant selection by these agents. The mutant prevention concentration (MPC) and mutant selection window (MSW) were determined for azithromycin, ceftriaxone, doxycycline, ertapenem, gentamicin, ciprofloxacin, levofloxacin and moxifloxacin against a wild-type strain of N. gonorrhoeae (ATCC 49226) and a gyrA mutant of ATCC 49226. Pharmacokinetic parameters, including peak concentration (Cmax), half-life (t1/2) and area under the plasma concentration-time curve over 24 h (AUC), associated with each agent were used to calculate the time within the MSW (TMSW, percentage of the dosing interval that antimicrobial concentrations fall within the MSW), Cmax/MPC ratio and AUC/MPC ratio for each antimicrobial agent. Concentrations of ceftriaxone (500 mg), ertapenem, ciprofloxacin, levofloxacin and moxifloxacin surpass the MPC for both strains. Results of pharmacodynamic analyses suggest that ertapenem, ciprofloxacin, levofloxacin and moxifloxacin may be most likely to prevent mutant selection in N. gonorrhoeae. Use of ceftriaxone, azithromycin, doxycycline or gentamicin for gonorrhoea is expected to lead to the ongoing emergence of resistance to these agents. There is a clear need to develop novel treatment regimens for gonococcal infections in order to limit the dissemination of resistance in N. gonorrhoeae.

Manipulation-free and autonomous recording of temperature states for extended periods of time is of increasing importance for food spoilage and battery safety assessment. An optical readout is preferred for low-tech visual inspection. Here, a concept for time-temperature integrators based on colloidal crystals is introduced. Two unique features in this class of advanced materials are combined: 1) the film-formation kinetics can be controlled by orders of magnitude based on mixtures of particles with distinct glass transition temperatures. 2) A gradual variation of the particle mixture along a linear gradient of the colloidal crystal enables local readout. Tailor-made latex particles of identical size but different glass transition temperatures provide a homogenous photonic stopband. The disappearance of this opalescence is directly related to the local particle ratio and the exposure to a time and temperature combination. This sensing material can be adjusted to report extended intermediate and short excessive temperature events, which makes it specifically suitable for long-term tracing and threshold applications.

In this study, a framework is developed to perform two-stage stochastic programming in a district energy system. This framework optimizes the sizing of energy components to minimize the total cost and operating CO2 emissions. Uncertainties in electricity demand, solar irradiance, wind speed, and electricity emissions are considered. A group of buildings at University of Utah is used as the case study to test the optimization framework. This study is novel by forming an open-source framework, considering electricity emissions with more details compared to previous studies in the literature, and performing the optimization for a campus in the U.S. This study’s results show the trade-off between cost and emissions when different energy configurations are used for three electricity purchasing cases. This framework can help facility managers to evaluate the optimum sizing of their district energy system to minimize the cost and emissions.

Stochastic optimization of a district energy system (DES) is investigated with renewable energy systems integration and uncertainty analysis to meet all three major types of energy consumption: electricity, heating, and cooling. A district of buildings on the campus of the University of Utah is used as a case study for the analysis. The proposed DES incorporates solar photovoltaics (PV) and wind turbines for power generation along with using the existing electrical grid. A combined heat and power (CHP) system provides the DES with power generation and thermal energy for heating. Natural gas boilers supply the remaining heating demand and electricity is used to run all of the cooling equipment. A Monte Carlo study is used to analyze the stochastic power generation from the renewable energy resources in the DES. The optimization of the DES is performed with the Particle Swarm Optimization (PSO) algorithm based on a day-ahead model. The objective of the optimization is to minimize the operating cost of the DES. The results of the study suggest that the proposed DES can achieve operating cost reductions (approximately 10% reduction with respect to the current system). The uncertainty of energy loads and power generation from renewable energy resources heavily affects the operating cost. The statistical approach shows the potential to identify probable operating costs at different time periods, which can be useful for facility managers to evaluate the operating costs of their DES.

This paper investigates residential rooftop photovoltaic (PV) systems for long-term thermoeconomic benefits from PV homeowners' perspectives and for impacts on the electrical distribution network from grid operators' perspectives. The costs of generating electricity from grid-connected PV systems are studied with and without energy storage at the PV homeowners' sites. Three selling scenarios for excess PV energy conversion are considered: net metering, wholesale pricing, and no payback. PV systems in Utah are utilized as case studies in this analysis. The presence of PV systems gives homeowners economic benefits such as reduced annual electricity bills. However, the levelized costs of electricity are considerably higher than the weighted electricity price in Utah. Currently, the addition of energy storage only benefits customers in Utah under the no payback policy. The impacts of PV systems toward electrical distribution networks are then studied on a distribution test system. Excess PV generation from residential PV systems causes voltage rise in the electrical distribution network. The results from this paper can educate consumers about the lifetime benefit of integrating solar energy into their homes. For grid operators, residential PV systems with energy storage can reduce the negative impacts on the grid compared with high PV penetration alone.

Assessing system costs for power generation is essential for evaluating the economical aspect of energy resources. This paper examines traditional and renewable energy resources under uncertainty and variability of input variables. The levelized cost of electricity (LCOE) of each technology is computed using a global sensitivity analysis. A Monte Carlo approach is utilized to study the thermoeconomics of a variety of power generation methods in the United States: fossil fuel-based, nuclear, developed renewable, and emerging renewable energy resources. The results of this study demonstrate how uncertainties in input data can significantly influence the LCOE values. Power generation from well-developed energy technologies exhibit less variability in LCOE due to established capital costs, operating and maintenance costs, and power generation. On the contrary, emerging renewable energy technologies are subject to high uncertainties in both technical and economic performance, as expected for technologies in early stages of development. A scenario with carbon pricing in power generation is also carried out in the paper. The presence of carbon pricing significantly increases the LCOEs of fossil fuel technologies, and LCOEs of other technologies also experience significant changes when life-cycle carbon assessments are considered. Several cost reduction opportunities are discussed to guide the development of future energy conversion, especially from emerging renewable energy resources.