Abstract
Resistance change under mechanical stimuli arouses mass operational heat, damaging the performance, lifetime, and reliability of stretchable electronic devices, therefore rapid thermal heat ...dissipating is necessary. Here we report a stretchable strain sensor with outstanding thermal management. Besides a high stretchability and sensitivity testified by human motion monitoring, as well as long-term durability, an enhanced thermal conductivity from the casted thermoplastic polyurethane-boron nitride nanosheets layer helps rapid heat transmission to the environments, while the porous electrospun fibrous thermoplastic polyurethane membrane leads to thermal insulation. A 32% drop of the real time saturated temperature is achieved. For the first time we in-situ investigated the dynamic operational temperature fluctuation of stretchable electronics under repeating stretching-releasing processes. Finally, cytotoxicity test confirms that the nanofillers are tightly restricted in the nanocomposites, making it harmless to human health. All the results prove it an excellent candidate for the next-generation of wearable devices.
The electrochemical N2 fixation, which is far from practical application in aqueous solution under ambient conditions, is extremely challenging and requires a rational design of electrocatalytic ...centers. We observed that bismuth (Bi) might be a promising candidate for this task because of its weak binding with H adatoms, which increases the selectivity and production rate. Furthermore, we successfully synthesized defect‐rich Bi nanoplates as an efficient noble‐metal‐free N2 reduction electrocatalyst via a low‐temperature plasma bombardment approach. When exclusively using 1H NMR measurements with N2 gas as a quantitative testing method, the defect‐rich Bi(110) nanoplates achieved a 15NH3 production rate of 5.453 μg mgBi−1 h−1 and a Faradaic efficiency of 11.68 % at −0.6 V vs. RHE in aqueous solution at ambient conditions.
Beneficial defects: Defect‐rich bismuth nanoplates achieve a 15NH3 production rate of 5.453 μg mgBi−1 h−1 and a Faradaic efficiency of 11.68 % at −0.6 V vs. RHE in aqueous solutions at ambient conditions because of their poor binding with H adatoms, which increases the selectivity and production rate. Also, 1H NMR measurements with N2 gas ware used as a quantitative test method in aqueous electrolytes.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Ether based electrolytes have surfaced as alternatives to conventional carbonates allowing for enhanced electrochemical performance of sodium-ion batteries; however, the primary source of the ...improvement remains poorly understood. Here we show that coupling titanium dioxide and other anode materials with diglyme does enable higher efficiency and reversible capacity than those for the combination involving ester electrolytes. Importantly, the electrolyte dependent performance is revealed to be the result of the different structural evolution induced by a varied sodiation depth. A suit of characterizations show that the energy barrier to charge transfer at the interface between electrolyte and electrode is the factor that dominates the interfacial electrochemical characteristics and therefore the energy storage properties. Our study proposes a reliable parameter to assess the intricate sodiation dynamics in sodium-ion batteries and could guide the design of aprotic electrolytes for next generation rechargeable batteries.
There is a pressing need to shorten the development period for new materials possessing desired properties. For example, bulk metallic glasses (BMGs) are a unique class of alloy materials utilized in ...a wide variety of applications due to their attractive physical properties. However, the lack of predictive tools for uncovering the relationships between BMGs' alloy composition and desired properties limits the further application of these materials. In this study, a machine-learning (ML) approach was developed, based on a dataset of 6471 alloys, to enable the construction of a predictive ML model to describe the glass-forming ability and elastic moduli of BMGs. The model's predictions of unseen data were found to be in good agreement with most experimental values. Consequently, we determined that an alloy with a large critical-casting diameter would likely have a high mixing entropy, a high thermal conductivity, and a mixing enthalpy of approximately −28 kJ/mol, and that a BMG with a small average atomic volume would likely have a high elastic modulus. The efficacy of ML was demonstrated in furnishing a mechanistic understanding and enabling the prediction of metallic-glass properties.
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•The machine learning based models for predicting properties of metallic glasses are developed.•Key features are selected, and expressions based on those features are generated.•The machine learning based models and expressions show good predictive and generalization ability.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Electrocatalytic hydrogen evolution in alkaline and neutral media offers the possibility of adopting platinum‐free electrocatalysts for large‐scale electrochemical production of pure hydrogen fuel, ...but most state‐of‐the‐art electrocatalytic materials based on nonprecious transition metals operate at high overpotentials. Here, a monolithic nanoporous multielemental CuAlNiMoFe electrode with electroactive high‐entropy CuNiMoFe surface is reported to hold great promise as cost‐effective electrocatalyst for hydrogen evolution reaction (HER) in alkaline and neutral media. By virtue of a surface high‐entropy alloy composed of dissimilar Cu, Ni, Mo, and Fe metals offering bifunctional electrocatalytic sites with enhanced kinetics for water dissociation and adsorption/desorption of reactive hydrogen intermediates, and hierarchical nanoporous Cu scaffold facilitating electron transfer/mass transport, the nanoporous CuAlNiMoFe electrode exhibits superior nonacidic HER electrocatalysis. It only takes overpotentials as low as ≈240 and ≈183 mV to reach current densities of ≈1840 and ≈100 mA cm−2 in 1 m KOH and pH 7 buffer electrolytes, respectively; ≈46‐ and ≈14‐fold higher than those of ternary CuAlNi electrode with bimetallic Cu–Ni surface alloy. The outstanding electrocatalytic properties make nonprecious multielemental alloys attractive candidates as high‐performance nonacidic HER electrocatalytic electrodes in water electrolysis.
Nonprecious nanoporous multielemental alloy electrodes composed of electroactive surface high‐entropy CuNiMoFe alloy hold great promise as cost‐effective electrocatalysts for hydrogen evolution reaction (HER) in nonacidic media. Associated with hierarchical nanoporous architecture to facilitate electron transfer and offer abundant high‐entropy CuNiMoFe active sites, the nanoporous CuAlNiMoFe hybrid electrode exhibits remarkably enhanced HER activity and durability.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Here an efficient copper‐catalyzed cascade cyclization of azide‐ynamides via α‐imino copper carbene intermediates is reported, representing the first generation of α‐imino copper carbenes from ...alkynes. This protocol enables the practical and divergent synthesis of an array of polycyclic N‐heterocycles in generally good to excellent yields with broad substrate scope and excellent diastereoselectivities. Moreover, an asymmetric azide–ynamide cyclization has been achieved with high enantioselectivities (up to 98:2 e.r.) by employing BOX‐Cu complexes as chiral catalysts. Thus, this protocol constitutes the first example of an asymmetric azide–alkyne cyclization. The proposed mechanistic rationale for this cascade cyclization is further supported by theoretical calculations.
An efficient copper‐catalyzed cascade cyclization of azide‐ynamides via α‐imino copper carbene intermediates is disclosed, enabling divergent synthesis of polycyclic N‐heterocycles in generally good to excellent yields with broad substrate scope and excellent diastereoselectivities, representing the first generation of α‐imino copper carbenes directly from alkynes. The asymmetric azide–ynamide cyclization has been achieved with high enantioselectivities.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Carbonaceous acid catalyst with high acid content was synthesized by metal (Zr) ion chelation and sulfonation of sodium carboxymethylcellulose. It catalyzed the esterification of oleic acid, with ...biodiesel yield > 99% at 40–90 °C (activation energy of 24.7 KJ/mol).
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•Carbon-based catalyst with various acid sites was synthesized.•Catalyst had high total acid content (8.45 mmol/g) for esterification of oleic acid.•High biodiesel yield (> 99%) was achieved at 40–90 °C with Ea of 24.7 KJ/mol.•10 cycles with biodiesel yield > 87% at 90 °C for 2 h reaction.•3 cycles with biodiesel yield > 93% at 50 °C for 6 h reaction.
Carbonaceous catalyst, with a variety of acid sites, was successfully synthesized by a two-step method at low temperature. The synthesized catalyst (Zr-SO3H@CMC) was characterized by elemental analysis, XRD, ICP-OES, FT-IR, BET, SEM-EDX and Boehm titration. It had high total acid content (8.45 mmol/g) for oleic acid esterification with high biodiesel yields (> 99%) at 40–90 °C. Under conditions of 90 °C, 20/1 methanol/oleic acid molar ratio, 5 wt% catalyst and 2 h reaction time, biodiesel yield of > 87% was achieved after 10 cycles of use. At lower temperatures (40, 50 and 60 °C), biodiesel yield still reached 82%, 93% and 90% after 3 cycles, respectively. The high activity of Zr-SO3H@CMC benefited from low activation energy of 24.7 KJ/mol. It shows potential application for esterification of free fatty acids or pretreatment of oils with high acid value for the green production of biodiesel.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
With reduced background and high sensitivity, photoelectrochemistry (PEC) may be applied as an intracellular nanotool and open a new technological direction of single‐cell study. Nevertheless, the ...present palette of single‐cell tools lacks such a PEC‐oriented solution. Here a dual‐functional photocathodic single‐cell nanotool capable of direct electroosmotic intracellular drug delivery and evaluation of oxidative stress is devised by engineering a target‐specific organic molecule/NiO/Ni film at the tip of a nanopipette. Specifically, the organic molecule probe serves simultaneously as the biorecognition element and sensitizer to synergize with p‐type NiO. Upon intracellular delivery at picoliter level, the oxidative stress effect will cause structural change of the organic probe, switching its optical absorption and altering the cathodic response. This work has revealed the potential of PEC single‐cell nanotool and extended the boundary of current single‐cell electroanalysis.
An integrated photocathodic nanotool was fabricated for dual‐functional intracellular drug delivery and evaluation of cellular oxidative stress in single live cell.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Discovering knowledge from data is a quantum jump from quantity to quality, which is the characteristic and the spirit of the development of science. Symbolic regression (SR) is playing a greater ...role in the discovery of knowledge from data, specifically in this era of exponential data growth, because SRs are able to discover mathematical formulas from data. These formulas may provide scientifically meaningful models, especially when combined with domain knowledge. This article provides an overview of SR applications in the field of materials science and engineering. Integrating domain knowledge with SR is the key and a crucial approach, which allows gaining knowledge from data quickly, accurately, and scientifically. In the data-driven paradigm, SR allows for uncovering the underlying mechanisms of materials behavior, properties, and functions, in a wide range of areas from basic academic research to industrial applications, including experiments and computations, by providing explicit interpretable models from data, in comparison with other machine-learning “black-box” models. SR will be a powerful tool for rational and automatic materials development.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ