Hydrogel-based plant bioelectronics are emerging as promising platforms for real-time monitoring and modulation of plant physiology, stress responses, environmental interactions, and growth. Compared with rigid electrodes and conventional polymer films, hydrogels provide a soft, hydrated, conductive, and tunable interface that reduces mechanical mismatch with growing plant tissues while enabling electrochemical, electrophysiological, optical, and multimodal sensing. This review examines recent advances in hydrogel materials for plant bioelectronics, focusing on how network structure, design requirements, materials strategies including crosslinking chemistry, porosity, swelling, adhesion, conductivity, transparency, gas permeability, and biocompatibility affect plant-device performance. Applications in monitoring plant physiology, hormones, pH, moisture, glucose, and overall plant health are highlighted. Reported hydrogel systems exhibit Young’s moduli from ∼ 1 kPa to several MPa and ionic conductivities of 10−3-10−1 S cm−1. Several plant-interfacing devices sustain strains above 300 %, maintain stable electrical performance over 10,000 loading cycles, and support continuous growth monitoring for up to 14 days. Despite these advances, standardised evaluation under realistic agricultural conditions remains limited. Future research should prioritise standardised testing, biodegradable biomass-derived materials, multimodal sensing integration, and closed-loop bioelectronic systems to advance precision agriculture and bio-regenerative life-support applications.
We develop parallel and batch-cutting variants of the norm-minimization-based outer approximation algorithm for convex vector optimization. The standard algorithm solves Nk independent subproblems at each iteration k to evaluate all vertices of the current polyhedral approximation, but processes only the single best cut. We propose two improvements. First, we parallelize the subproblem evaluations across W workers, reducing per-iteration wall-clock time. Second, we introduce a batch-cutting strategy that adds up to K supporting halfspaces per iteration, using information from all solved subproblems rather than discarding it. We prove that the batch-cutting variant inherits the convergence rate O(k2/(1−q)) of the standard algorithm, where k is the number of outer iterations and q is the number of objectives. Computational experiments on eight test problems with q ∈ {2, 3, 4, 5} show that parallelism on 8 cores increases the speed by a factor of 1.1 to 4.2, and batch cutting consistently reduces the iteration count by 62–80%. However, the wall-clock benefit of batch cutting is problem-dependent: the additional cuts per iteration accelerate vertex count growth, so batch cutting is most effective when per-vertex subproblem cost dominates.
Hydrocarbon-nitrous oxide (HyNO(x)) and related N2O-based propellant systems are promising next-generation green propulsion alternatives owing to their high performance, low toxicity, and being self-pressurizing oxidizers. Among these, N2O-ethanol (NOEL) mixtures represent a promising bipropellant combination offering clean combustion and compatibility with sustainable fuel production. This paper presents the first time-resolved measurements of CO and CO2 formation during the high-temperature reaction of ethanol with N2O behind reflected shock waves, offering insights into the kinetics of this promising green propellant system. Experiments were performed in a stainless-steel shock tube over 1300-2000 K at similar to 1.3 atm and equivalence ratios of 0.5-2.0. Mid-infrared laser absorption diagnostics using quantum-cascade lasers enabled measurement of the time-resolved species. The results revealed rapid CO buildup, followed by oxidation to CO2, characteristic of fast O/OH radical generation from N2O decomposition and its reaction with H-atom. Comparisons with recent kinetic mechanisms showed that, although the models captured overall temperature and mixture dependence, they systematically misrepresented CO yields and peak timing, particularly at low to intermediate temperatures. CO time histories exhibited two-stage behavior, with an initial steep rise attributed to rapid ethanol decomposition, followed by a nearly plateau region governed by oxidation of the remaining products. Sensitivity analysis showed that inclusion of the N2O + CO -> N-2 + CO2 reaction is critical for accurate CO prediction. An updated kinetic mechanism was therefore discussed, yielding superior CO predictions. Overall, this work provide time-resolved datasets that can be used as validation benchmarks for improving the kinetic modeling of hydrocarbon-N2O combustion and HyNO(x)-based propellant systems.
This study explores the techno-economic feasibility of generating hydrogen via co-gasification of heavy fuel oil (HFO) and polyethylene, which is a widely available plastic. Two process models are developed in this study to compare hydrogen production from HFO alone (Case 1) versus a 1:1 blend of HFO and polyethylene (PE) (Case 2). The process models are then evaluated at different operational conditions involving gasification temperature, pressure, and oxygen-to-feedstock ratio. A techno-economic analysis was conducted to assess the performance and profitability of both cases. The results presented that the blended feedstock case (Case 2) achieved significant improvements compared to the HFO-only case (Case 1). Case 2 demonstrated a 76 % Increased H2/CO ratio, a 75 % increase in the stoichiometric number, and a 30 % reduction in CO2 specific emissions. These enhancements translated to a 19 % increase in hydrogen yield per feedstock and a 16 % improvement in overall process efficiency. Additionally, the life cycle assessment (LCA) analysis showed a 7.0 % reduction in global warming potential (GWP) when using Case 2 due to the lower CO2 footprint of plastic waste. Technoeconomic analysis revealed that Case 2 had a 15 % reduced capital cost per unit of hydrogen generated 7.81 % lower levelized hydrogen production cost amounting to $1.92/kg, compared to $2.29/kg for Case 1. The analysis revealed that Case 2 surpassed Case 1 in multiple technical, environmental and financial indicators. To the best of our knowledge, no prior studies have investigated the co-gasification of HFO with plastic waste for hydrogen production. This work provides a novel integrated framework combining thermodynamic modeling with techno-economic and environmental evaluation to quantify the synergistic benefits of this underexplored feedstock combination.
This study investigates the behavioural and operational dynamics of a night-time demand-responsive transport (DRT) system in the alpine resort of Hakuba, Japan, within a digitally mediated service environment, with particular attention to the temporal and spatial patterns of seasonal tourism. Using a dataset of 5,232 completed trips, the analysis identifies the key drivers of demand intensity in a low-density mountainous context. The findings reveal a dispersed hub configuration, defined here as notable demand concentration at a limited number of functional stops combined with low geographic clustering and scattered local hotspots. This pattern reflects the role of DRT in connecting physically fragmented activity nodes. Moreover, the results highlight a distinct seasonal tourism rhythm: demand is shaped by month-to-month variation within the winter season and short-term temporal persistence, rather than conventional weekday–weekend cycles. The scale of passenger-carrying service delivery provides a baseline for understanding the operational demands of night-time DRT in rural, seasonally intensive tourism settings. The findings from the study suggest that DRT planning in such contexts should move beyond rigid, calendar-based approaches towards seasonally adaptive strategies that account for temporal persistence and spatial fragmentation in tourism-driven mobility systems.
