Light management is critical for thin-film energy devices, where interfacial optical losses can exceed 10-20% of incident photons. Despite well-established photonic concepts, implementation remains limited by the incompatibility of conventional nanopatterning with fragile energy materials, restricting the scalable performance gains. In this perspective, we argue that effective light management requires decoupling optical functionality from material constraints. We highlight laser-induced periodic surface structures generated by femtosecond-laser processing as a scalable, maskless approach to create photonic interfaces while preserving optoelectronic quality. Using CsPbI3 thin films as a model system, we demonstrate direct formation of surface gratings leading to reduced reflectivity and enhanced absorption (∼10%) through diffraction-driven light coupling and optical path-length extension. Self-organized photonic interfaces thus provide a general strategy for scalable optical optimization across a broad range of thin-film energy technologies.

n-octane is an essential hydrocarbon in fuels and petrochemicals, yet conventional production suffers from high energy demand and material losses. This study develops an integrated process design combining recycle systems, heat integration, and purge-gas utilization for n-octane production. Results show that recycle integration raises yield from 92.81% to 97.46%, heat integration achieves 36.38% energy savings, and purge-gas valorization sustains high yield (97.41%) while delivering the greatest energy reduction (62.30%). The findings demonstrate that synergistic process intensification enhances efficiency and sustainability, offering a transferable framework for hydrocarbon production optimization. Copyright © 2026 by Authors, Published by Universitas Diponegoro and BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

This proof-of-concept study explores the application of solvatochromism to resolve differences in the polarity microenvironment induced by nonpolar hydrocarbon compounds, specifically cycloalkanes. A solvatochromic probe based on Betaine 30 or Reichardt's dye was designed, and UV-vis absorption spectra were recorded for a set of structurally diverse cycloalkanes. Multivariate analysis, incorporating Principal Component Analysis (PCA) and K-means clustering, was applied to the spectral data to introduce a novel polarity classification capable of capturing subtle differences in the polarity microenvironment of cycloalkanes, compounds traditionally regarded as uniformly nonpolar. This solvatochromic approach demonstrated high sensitivity in detecting and differentiating small polarity variations among closely related isomers, revealing that molecular conformation, stereochemistry, and the number, type, and position of alkyl substituents exert measurable effects on the polarity microenvironment of hydrocarbons. These findings are particularly relevant to the development of alternative synthetic aviation fuels, where small variations in cycloalkane composition can significantly influence fuel performance, including energy density, combustion efficiency, and material compatibility (e.g., O-ring swelling). Overall, this work underscores the utility of solvatochromic probes for differentiating among structurally similar organic compounds, revealing quantitative polarity microenvironment differences across cycloalkanes and provides a preliminary polarity microenvironment classification framework for evaluating subtle intermolecular interaction differences to complement traditional methods based solely on solvatochromic parameters such as ET(30) or normalized ENT values.

The importance of conventional plastics is undeniable; however, their non-biodegradability makes them one of the biggest environmental problems. Among the alternatives to mitigate environmental damages, the production of polyhydroxyalkanoates (PHB) provides a biodegradable solution, making them accessible to a wide variety of applications. In general, biopolymers accumulate as energy and carbon storage material in microorganisms such as bacteria and microalgae. In the present study, the impact of different sources like carbon, glucose, nitrogen and sodium was tested on the production of PHB in the cultures of the microalgae Scenedesmus acutus. To evaluate the effect of the variables, a fractional Taguchi experimental design was devised and executed, thus, 16 experimental runs and 3 replicas in each treatment were considered. Results showed calculated concentrations of the biopolymer in a range from 7.5 to 24.7% w/w of dry weight. Additionally, the PHB was identified by spectroscopic and thermogravimetric analysis. Statistical analysis was performed, showing differences in biomass production, PHB concentration in g L- 1 and the percentage of PHB. Likewise, a Pareto diagram was used to consider the biomass production results, with glucose, biomass-glucose, and biomass-sodium as determining factors in the PHB production. The present research provides significant data on critical factors related with PHB production, to the best of our knowledge, thus showing S. acutus may be a promising candidate among PHB producers through a low-cost means.

Post-polymerization modification can provide access to functional polymers that are difficult to obtain through bottom-up synthesis, allowing systematic tuning of the material properties. Such functional polymers, in turn, offer a powerful platform for tailoring catalytic microenvironments, particularly in the electrochemical CO2 reduction reaction (CO2RR), where tunable CO2 adsorption and mass transport are critical for selectivity and efficiency. However, achieving precise control over these properties requires accurate inclusion of functional groups on the polymer backbone, which remains a significant challenge. Here, we report a modular post-polymerization approach to functionalize polybutadiene, leveraging the reactivity of the unsaturated bonds, through nitration and subsequent reduction to primary amines. Using tert-butyl nitrite as a simple and inexpensive NO2 radical source, we achieve tunable and regio- and stereoselective nitro incorporation onto the polymer backbone ranging from 0.8 to 18.6% (nitro per 100 carbon atoms), which could be readily reduced to the primary amine without compromising the polymer backbone integrity. Detailed polymer analysis after 15N-labeling revealed a high regioselectivity for the functionalization of the internal over terminal unsaturated bonds. When applied as an amine-functionalized layer on a Cu electrode for CO2 electroreduction, we observed a scaling relationship between the amine functionalization degree and the partial current for C2 products (ethylene and ethanol). Overall, this work highlights a robust and versatile post-polymerization strategy that advances precision control in polymer functionalization essential for engineering applications, particularly in energy materials.

Nonadiabatic (NA) molecular dynamics (MD) is the method of choice for modeling far-from-equilibrium, excited state processes in molecules and materials. Machine learning (ML) can streamline all NAMD components, enabling quantum dynamics simulations of thousand-atom systems over nanoseconds. By comparing three qualitatively different ML models to interpolate the NA Hamiltonian and testing them on a metal halide perovskite, we demonstrate that bidirectional long-short-term-memory (BiLSTM) gives the best performance, since it is efficient for smaller, sequence-dependent data sets. Transformer also provides an accurate representation, although the amount of data is not sufficiently large to take full advantage of transformer capabilities. Kernel ridge regression (KRR) is simple and inexpensive, achieving rapid and robust NA Hamiltonian interpolation, although it requires smaller steps. Even with sparse training data, the models can closely replicate ab initio results while achieving 2 orders of magnitude computational savings. The reported advances allow one to accelerate the discovery and optimization of energy and optoelectronic materials.

While greenhouse cultivation boosts food production to address population growth, its energy-intensive temperature control and irrigation systems pose significant sustainability challenges. Here, we present a thermoresponsive poly(N-isopropylacrylamide) hydrogel (NA-Li) that closes the water and thermal energy loop within greenhouses. Below the thermoresponsive temperatures, the embedded hygroscopic salt enables autonomous atmospheric water harvesting in a wide range of humid environments. Above the thermoresponsive temperatures, the poly(N-isopropylacrylamide) chains collapse, directly squeezing out the liquid water to irrigate drylands, without extra condensers. Simultaneously, the dropped transmittance of solar light efficiently decreases the interior temperatures (1.1-6.0 °C) to mitigate sunscald and reduces soil- and transpiration-driven water loss. As a proof-of-concept application, greenhouse trials confirmed the noticeable efficacy of NA-Li in promoting crop survival rates under thermal shock and doubling productivity, saving 1.02 kg m-2 water consumption. By synchronizing atmospheric water capture, on-demand irrigation and adaptive radiative shading in a single material, this study connected the interdisciplinary fields of horticulture, thermal energy management, water and materials, which could provide a feasible solution to water and temperature management in greenhouses and a technical route for its sustainable development.

The conversion of bio-waste into functional energy materials provides a robust platform for addressing both environmental and energy challenges. In this paper, discarded absorbent pads are transformed into carbon-rich frameworks, which is followed by the fabrication of composites through the incorporation of Cu4SnS4 (CSS) for dual electrochemical applications. Integrating CSS into the waste-derived carbon matrix induces strong synergistic effects, improving electrical conductivity, increasing active-site availability, and accelerating charge-transfer kinetics. Comprehensive physicochemical analyses confirmed the successful formation of a well-integrated heterostructure composite with favorable structural and surface characteristics. Electrochemical evaluations further demonstrated that CSS-modified carbon exhibits superior bifunctional performance. In a two-electrode configuration, the composite delivers an energy density of 12.08 Wh kg-1 at a power density of 250 W kg-1 along with excellent cycling stability in supercapacitor applications. As an electrocatalyst, it achieves a low overpotential of 268 mV at -10 mA cm-2 and a small Tafel slope of 75 mV dec-1, reflecting efficient reaction kinetics. The strong durability observed in both systems underscores the structural integrity and long-term operational stability of the material. Overall, this paper advances a sustainable waste-to-resource strategy for fabricating multifunctional carbon-based composites, offering a promising platform for integrated energy-storage and hydrogen-generation technologies.

Open-shell organic radicals underpin catalysis, energy materials, and spin-based technologies, yet rational design is hindered by the difficulty of tuning electronic reactivity while preserving chemically meaningful local response. Here, a descriptor-driven reinforcement learning framework is used to regulate radical reactivity in terms of conceptual density functional theory (CDFT) based reactivity indices, enabling electrophilicity index (ω) to be driven toward the experimentally motivated target ω ≈ 1.0 eV while maintaining admissible atom-condensed Fukui function behavior. Using a Twin Delayed Deep Deterministic Policy Gradient (TD3) anchored to benchmark tolerances, an 85.7% multiobjective success rate is achieved on held-out radicals, and all of the top 20 candidates ranked by a composite ω-Fukui function score satisfy both global and local criteria. Class- and motif-resolved analyses reveal a clear hierarchy in tractability: electronically flexible scaffolds, including phosphoryl, silyl, boryl, and alkyl-centered radicals, consistently converge to balanced reactivity regimes, whereas rigid π-conjugated or lone-pair-locked motifs, such as aromatic, heteroaryl, and halogen-substituted radicals, resist coordinated tuning. Complementary reward ablation further shows that robust convergence requires coupling global ω control to local Fukui function-based regularization, as single-descriptor objectives lead to unstable or incomplete optimization. Together, these results demonstrate that electronic flexibility governs the tunability of open-shell reactivity descriptors and provide a practical strategy for radical selection and descriptor-space optimization using chemically interpretable electronic metrics.

Covalent functionalization offers a versatile platform to engineer carbon nanotube properties for optoelectronics applications. We demonstrate by atomistic quantum dynamics simulation that covalent functionalization can renormalize the CNT band gap, split the degenerate CNT band edge states, and strongly influence charge carrier separation and recombination dynamics. Open-ring CNT functionalization (O-CNT) largely retains the π-conjugation, whereas closed-ring functionalization (C-CNT) perturbs the electronic structure due to sp3 hybridization at the functionalized site. The energy gaps for the charge separation and recombination in the hybrid of O-CNT with the tetra-cyano-anthra-quinodimethane (TCAQ) molecule are comparable to those in the corresponding noncovalent van-der-Waals hybrid (V-CNT@TCAQ). In contrast, localized band edge states appear in C-CNT@TCAQ, renormalizing the energy gaps. Generally, the covalent functionalization accelerates charge separation relative to the V-CNT system, with C-CNT@TCAQ showing the most efficient separation due to the smallest energy offset. C-CNT@TCAQ also exhibits the most advantageous, slowest charge recombination, due to enhanced charge localization and reduced electron-hole overlap, resulting in the smallest nonadiabatic coupling and the shortest coherence time. Both charge separation, occurring within picoseconds, and recombination, taking nanoseconds, become more favorable, when the functionalization creates a stronger perturbation to the CNT. The reported theoretical investigation reveals how rapid charge separation and slow recombination can be achieved through covalent functionalization of CNTs, providing key guidelines for design of modern and efficient optoelectronic and solar energy materials.

Metal-organic frameworks (MOFs) are premier platforms for photocatalytic hydrogen evolution (PHER), yet navigating their multidimensional parameter space typically relies on inefficient trial-and-error approach. While machine learning (ML) can accelerate discovery, it is often hindered by ″black-box″ predictions that lack mechanistic transparency and experimental validation. Herein, we establish an interpretable ML-to-experimental framework for rational MOF engineering. By training a CatBoost model on a curated database and employing SHapley Additive Explanations (SHAP), we deconstructed the hierarchical influence of ligand motifs on catalytic activity. This revealed the cooperative effect of hydroxyl and amino dual functionalization, which optimizes the electronic landscape through balanced bandgap dynamics and hard-soft acid-base (HSAB) matching. Guided by these insights, we synthesized benzophenanthrene-based mixed-ligand MOFs. The champion catalyst achieved a peak HER rate of 73.7 mmol g-1 h-1─without external photosensitizers or cocatalysts─exhibiting a 4.18% deviation from algorithmic predictions and a 15.8% enhancement over the top of the data set. This work develops a high-performance photocatalytic system and provides a generalizable, interpretable paradigm for data-driven discovery of advanced energy materials.

To meet the increasingly stringent demands of next-generation electronic systems, magnetoresistive sensors are required to simultaneously deliver environmental compatibility, advanced functionality, and enhanced intelligence. Here, we demonstrate a synergistic strategy spanning device, algorithm, and system levels to address these challenges in a unified manner. By rationally designing functional inks, fully printable magnetoresistive sensors are realized through additive manufacturing, substantially reducing energy consumption and material waste during fabrication. Introducing magnetic-field guidance during printing enables vertical alignment of functional nanowires, resulting in an out-of-plane sensor architecture. This configuration not only reduces nanowire surface coverage, imparting exceptional optical transparency, but also suppresses the adverse influence of inter-nanowire junctions on electrical percolation, thereby enhancing mechanical robustness. Beyond materials and device engineering, the integration of machine-learning algorithms and system-level optimization extends sensor operation beyond conventional threshold-based mechanisms, enabling robust multi-pattern recognition capabilities. Notably, this functionality is achieved using a single sensing element without relying on sensor matrices or additional electronic components, thus preserving the intrinsic transparency and mechanical flexibility of the system. Leveraging the synergistic combination of these achievements, the proposed sensors offer an eco-responsible platform for next-generation imperceptible and intelligent magnetic sensing.

Reaching extremely high levels of sophistication of naturally occurring supramolecular polymers (SPs) with artificial structures represents a paradigm with important underlying application potential in various fields such as biomaterials and optoelectronics. A key challenge in synthetic SP research is mimicking the complex hierarchical and multiple level folding of natural SPs, such as DNA for instance. In previous works, we have developed an artificial mimic of the chlorosome pigment antenna, consisting of micrometer-long phenanthrene-based SP nanofibers, with exceptional light-harvesting properties. In the present work, we have advanced this system one step further, by assembling the Trimer A nanofibers into sophisticated hierarchical nanostructures. To our knowledge, this work is the first report of hierarchical nanostructures of a functional synthetic SP. The polymerization was monitored by means of UV/visible and fluorescence spectroscopy. Cryo-EM and AFM revealed high aspect-ratio nanoribbons as well as large annular nanostructures. Our thorough study with acridine orange as the energy acceptor shows that the excellent light-harvesting antenna effect is preserved. The annular nanostructures observed are unprecedented in the field of synthetic hierarchical SPs, and this constitutes a notable chemical achievement. We also report the first calculation of a dimensionality compression factor, and we found a remarkable two-orders-of-magnitude reduction for an annular structure. Overall, the present work shows that dimensional compression does not impede light-harvesting performance. It paves the way, in SP science, for potential applications such as advanced energy materials.

Prussian blue analogs (PBAs) represent a versatile class of molecule-based magnetic materials in which subtle changes or distortion in local coordination geometry can strongly influence their macroscopic properties. In general, many types of distortions could exist in PBAs, such as octahedral tilts, A-site slides, Jahn-Teller (JT), vacancy, hydration-driven distortions, and so on. Among all types of distortions, JT distortions play a pivotal role by coupling electronic degeneracy with lattice deformation, thereby governing structural symmetry, magnetic exchange pathways, and ion-transport behavior. In addition, JT distortions play a pivotal role in governing the structural phase transitions of PBAs. This review critically surveys the latest advances in understanding how JT distortions, particularly those induced by the spin configuration of JT-active transition-metal ions, drive symmetry-breaking phase transitions within PBA frameworks. The interplay between electronic structure and lattice deformation is discussed, highlighting the mechanisms by which distortion leads to new crystallographic phases, influences ion diffusion, and modulates the electrochemical properties. Special attention is given to understanding the evolution of the JT effect in relation to structural phase transitions. It also summarizes effective strategies to suppress unwanted phase changes and improve overall structural stability. Ultimately, this review elucidates how control over JT effects can tailor electrochemical properties such as specific capacity, cycling stability, and voltage profile, offering design principles for the next generation of high-performance PBA-based energy materials.

Owing to well-defined topologies and structural orderings, metal-organic frameworks (MOFs) can serve as a prototype platform for designing new energy materials with predesigned structures for efficient energy and electron transfer. This study explores the photoinduced electron transfer dynamics of monoanionic radicals within two different UiO-type MOFs, distinguished by their degree of interpenetration. In 0-MOF, which has relatively large pores (18.6 Å), electron transfer is primarily facilitated by solvent-assisted electron hopping, with dimethylformamide (DMF) molecules serving as bridges between naphthalenediimide (NDI)-based ligands. In contrast, the smaller pores (12.1 Å) of 100-MOF admit only one or two DMFs, favoring direct through-space electron transfer between neighboring NDI units. This comparative study highlights the role of pore size and intermolecular interactions in governing the electron transfer mechanisms within MOFs. These findings contribute to a better understanding of the photophysical properties of MOFs and open new avenues for their potential use in future energy applications.

Pathways and structural dynamics of phase transformations impact performance of materials in energy and information storage technologies. Palladium hydride (PdHx) nanocrystals are an ideal model system for studying solute-induced phase transformations, where elastic energy from lattice mismatch between α-PdHx and β-PdHx phases is often considered a key to determining the transformation pathways. α/β-PdHx interfacial elastic energy is affected by the confined geometry of a nanocrystal. However, how nanocrystal geometry influences phase transformation pathways is largely unknown. Using in situ liquid phase transmission electron microscopy, we directly visualize hydrogenation in Pd nanocrystals with two geometries, a nanocube and a hexagonal nanoplate. Both follow similar sequences of an initially curved nucleus, interface flattening, and reverse-stage nucleation; however, their evolving α/β-PdHx interfaces exhibit geometry-dependent crystallographic alignments. In nanocubes, {100}-aligned configurations conform to static elastic energy ordering, representing a pathway that maintains a local mechanical equilibrium, whereas nanoplates display both {110}- and {211}-aligned interfaces. Theoretical simulations show that geometry determines the accessibility of alternative phase transformation pathways as the system is driven far from equilibrium during hydrogenation. These findings identify geometry as a fundamental parameter for directing phase transformation pathways, offering design principles for accessing atypical configurations and improving properties of intercalation-based devices.

Continuous gasification-pyrolysis of fresh palm fruit bunches for biochar production and carbon sequestration

Graphdiyne: A rising star in carbon-based 2D materials for energy and sensing

Mechanism and precise control of element detection deviation in automatic ferroalloy sample preparation system — Industrial application optimization for new energy material supply chain

Structural distortions in modified two-dimensional transition metal dichalcogenides (MX2) influence electrocatalytic activity, yet quantitative and predictive structure-property relationships remain underdeveloped. To bridge this gap, we perform data-driven structural angle mining across hundreds of thousands of single-atom doped configurations (TM1@MX2) and establish geometrically defined angular descriptors. These descriptors exhibit high predictive accuracy for hydrogen evolution electrocatalysis. Crucially, our analysis reveals that catalytic activity correlates more strongly with long-range angular parameters describing peripheral geometric effects than with the local coordination environment. Guided by these descriptors, we identify specific angular signatures as quantitative predictors for high-performance catalysts: an outer-shell S-centered angle indicates optimal hydrogen evolution reaction (HER) activity for Ir1@MoS2 (S-vacancy), while a distinct Mo-centered angle identifies V1@MoS2 (Mo-vacancy) as a promising earth-abundant candidate. Experimental verification confirms these predictions: synthesized Ir1@MoS2, with an ultralow loading of 0.1 wt%, achieves performance comparable to commercial Pt/C on a mass-activity basis, while V1@MoS2 enhances HER performance relative to pristine MoS2. The framework also shows strong computational correlations with oxygen evolution activity, though experimental validation for OER remains an important direction for future investigation. The angular descriptor framework introduced here provides a geometrically intuitive and electronically grounded strategy for the rational design and accelerated discovery of advanced energy materials.