Since the discovery of 3D-printing, it has revolutionized personalized drug delivery and implants by enabling intricate, customizable designs. However, key challenges remain, including complex design, host immune response, biofilm formation, and infection-induced inflammation at the implant site. This work offers, first-ever, unique ginger-based 3D-printable resins by chemically modifying Zingerol (Zing-OH, a ginger-based component) into photopolymerizable compositions that can print high-resolution complex designs via DLP 3D-printing. Briefly, the Zing-OH is amended via different functional group backbones, resulting in Zing-OH-based resins (ether, ester, and urethane) and their respective prints. Moreover, the Zing-OH prints’ thermal, mechanical, and biodegradation properties can be fine-tuned by simply customizing the backbone. Furthermore, the shape memory efficacy and the human bone (nasal cartilage, vestibular, cortical, femur, etc.) mimicking mechanical properties (exhibiting 2–200 MPa compressive strength) makes them more enticing. In tandem, the prints are also hemocompatible as well as cyto-friendly against human skin (HaCaT) and lung (BEAS-2B) cells, and mouse fibroblast (NIH-3T3) cells. Concurrently, an in vivo biocompatibility study in a rat model indicates that the printed materials are biocompatible, showing no signs of severe inflammatory response over a 28-day period. More importantly, the outstanding anti-biofilm and antioxidant efficacies of the Zing-OH prints make them more appealing due to their potential to prevent implant rejection, thus making them promising tools for bone-tissue engineering (BTE) applications.

The desalination fuel cell (DFC) represents a novel electrochemical technology that simultaneously desalinates water and generates electricity by coupling oxidation–reduction reactions. This study introduces a theoretical framework to support experimental findings and further elucidates DFC performance mechanisms. Unlike traditional electrodialysis (ED), where an externally applied electric potential drives desalination, the DFC employs redox half-reactions to achieve ion separation and energy conversion. The proposed H ${}_2$-O ${}_2$ DFC system, previously tested only experimentally, is examined here theoretically for the first time. The system features a desalination channel, bordered by anion and cation exchange membranes (AEM and CEM), with reacting catholyte and anolyte streams driving the desalination process. To evaluate cell performance, the model incorporates electrolyte composition variations, ionic interactions, resistances, and electrode kinetics. Mass transport is governed by the Nernst–Planck equation under the electroneutrality assumption, while electrochemical kinetics account for both activation and concentration overpotentials. The results provide insights into the interplay between desalination efficiency, ionic fluxes, and electrochemical processes, advancing the understanding of DFC operation and optimization strategies.

Pancreatic cancer ranks fourth among cancer-related deaths. Despite decades of research, the cure rate of disease remains disappointingly low. This dismal prognosis is due to late detection and to resistance of tumors to all known systemic therapies. Here, we build upon our previous findings that highlighted the selective uptake of tumor-associated macrophage-derived exosomes by cancer cells. We hypothesize that the prominent lipid contents on the exosome surface play a crucial role in facilitating selective cellular interactions, offering a novel avenue for developing efficient drug delivery platforms for pancreatic cancer. Lipids9 affinity toward cancer cells was measured, following mass spectral lipidomic analysis for screening the lipidic composition of macrophages and their derived exosomes. Subsequently, optimized and characterized synthetic exosomes underwent detailed intracellular trafficking studies in human pancreatic cancer cells, demonstrating enhanced cellular uptake kinetics. Notably, gemcitabine-loaded synthetic exosomes exhibited superior efficacy in inducing programmed cell death compared to both the free drug and conventional liposome formulations. The biodistribution examination of these synthetic exosomes underscored their potential for tumor specificity. In vivo experiments further demonstrated that the treatment with gemcitabine-incorporated synthetic exosomes inhibited tumor growth by nearly 50% compared to the administration of the free drug, indicating a significantly enhanced treatment efficacy. Our findings indicate that leveraging the lipid membrane composition of exosomes could lead to breakthroughs in drug delivery efficiency. This innovative strategy offers a promising direction in the ongoing battle against pancreatic cancer, highlighting the potential of exosomal lipid profiles for cancer therapy.

The doping factor approach is a simple, sometimes a “back-of-the-envelope” method, to predict concentrations of native point defects upon doping with aliovalent dopants. The doping factor is defined as the ratio between the concentration of quasi-free electrons in the doped state and their concentration in a reference state, typically at the undoped state. In binary compounds, as long as the Boltzmann statistics hold, the neutrality equation becomes a polynomial equation for the doping factor. Once the doping factor is found, all the concentrations of the native point defects are readily determined. While other computational methods allow us to find those concentrations, it is beneficial for researchers to possess such a simple tool for calculating the resulting new concentrations. Furthermore, this method may be the only one available in cases with small deviations from stoichiometry. The method is explained and demonstrated using a simple case of magnesium oxide, as well as on intrinsic compound oxide semiconductors.

Anion-exchange membranes (AEMs) enable electrochemical energy devices to operate in alkaline environments, allowing the use of earth-abundant, platinum group metal-free catalysts. This makes them highly attractive for applications such as AEM fuel cells (AEMFCs), water electrolyzers (AEMWEs), and oxygen separators (AEMOSs). However, two key challenges still hinder their commercialization: chemical degradation of the membranes and carbonation from atmospheric CO₂, both of which impair conductivity and reduce long-term performance. Building upon our ex situ novel technique to measure the alkaline stability of quaternary ammonium salts (QAs) and AEMs at different hydroxide hydration levels, in this study we investigate the effect of CO₂ on the stability of the AEMs. Specifically, we evaluate the stability of AEM standard benzyltrimethyl ammonium cations (BTMA⁺) in the presence of mixtures of hydroxide and (bi)carbonate anions at different ratios and hydration levels. Additionally, we introduce a novel technique to simultaneously evaluate (bi)carbonate impact and hydroxide conductivity in AEMs. Our results reveal that the presence of (bi)carbonate anions surrounding BTMA⁺ significantly reduces QA degradation. This phenomenon is further supported by molecular dynamics simulations, which suggest a “salting-out” effect, where water more strongly hydrates hydroxide ions, isolating them from the organic QA sites and reducing degradation probability. These findings provide new insight into AEM behavior in CO₂-containing environments such as ambient air. This study provides new, valuable data to overcome the critical challenge of AEM alkaline stability and opens new avenues for the design of more durable AEM materials, advancing the development of AEMFCs, AEMWEs, and AEMOSs for real-world applications.

Anion exchange membrane fuel cells (AEMFCs) have attracted much attention due to their bipolar design that enables the use of all Pt-group metal (PGM)-free catalysts. As the only PGM-free metal with the anode hydrogen oxidation reaction (HOR), the performance of Ni-based metals in fuel cells is too low to match well with the cathode oxygen reduction reaction (ORR) due to the low utilization rate of the active sites. Here, we activated Ni atoms with single-atom Fe up to its third nearest-neighbor site, and the prepared catalyst Fe₁Ni single-atom alloy (named Fe₁Ni_SAA–O@CN) exhibited a high mass activity of 67.58 A g_Ni⁻¹. More importantly, the peak power density (PPD) of the AEMFC with an Fe₁Ni_SAA–O@CN/C anode reached 729.4 mW cm⁻², which was 4.4 times that of pure Ni, exceeding most of the previously reported Ni-based anode catalysts. Furthermore, when this catalyst was paired with a PGM-free cathode catalyst, a remarkable PPD of 420.9 mW cm⁻² was obtained, highlighting the outstanding performance of the catalyst. Atomic resolution electron energy-loss spectroscopy (EELS) analysis and density functional theory (DFT) calculations revealed that the surrounding Ni atoms up to the third nearest neighbor around the Fe dopants were tuned into active sites via charge transfer from the Fe, thereby increasing the HOR activity. This study provides a new design paradigm for further improving the performance of PGM-free AEMFCs.

Predictive chemical kinetic modeling is foundational to areas ranging from energy and environmental science to pharmaceuticals and advanced materials. While significant progress has been made in automating individual steps, the development of a complete predictive model remains a human-intensive effort to orchestrate existing software tools and revise models. This Perspective outlines a practical path to improved chemical kinetic model development using agentic AI. A dual-lane architecture is introduced: a fast execution lane handles mechanism generation and parameter refinement, while a deliberative agentic lane plans, refines, and revises while executing experiments and computations. The proposed outcome is a robust pathway toward decision-grade models. Humans remain central: researchers set objectives and priors, approve high-impact actions, and adjudicate new chemical insights. Creativity, complex judgment, and strategic thinking remain in the human domain. Ultimately, this approach aims to accelerate trustworthy, transparent, decision-grade model development.

As both a prototype oxygenated biofuel and a central intermediate in combustion chemistry, the predictive modeling of formic acid (HOCHO) reactivity is of fundamental importance. Prior literature work [Energy Fuels 2022, 36, 23, 14382–14392] argued that an automatically-generated chemical kinetic model cannot reproduce jet-stirred reactor (JSR) speciation adequately, concluding that hand-tuned mechanisms are required. Here, we overturn that conclusion by demonstrating the fidelity of an automated model. An out-of-the-box, fully automated predictive model, built deliberately without quantum-chemical refinement or ad hoc fitting, reliably captures formic acid JSR oxidation speciation across 550–1150 K and an equivalence ratio range of 0.5–2.0, as well as laminar burning velocity observations at different mixture compositions. The model’s remaining discrepancies, which appear under pyrolysis conditions, stem from the same core issue masked in prior work [Combust. Flame 2021, 223, 77–87]: the absence of a physically consistent description of the CH2O2 pressure-dependent network. We demonstrate that the apparent agreement in prior pyrolysis modeling relied on fitted, pressure-independent rate coefficients that obscured the unresolved branching ratio between decarboxylation (HOCHO <=> CO2 + H2) and dehydration (HOCHO <=> CO + H2O). By clarifying these issues, we establish that predictive, automated kinetic models are already within reach for small oxygenated fuels and identify this pressure-dependent network as the key remaining challenge for fully reliable formic acid modeling.

The kinetic mechanism of waste plastic pyrolysis and the intricate multiphase hydrodynamics within fluidized bed pyrolyzers are of paramount importance to industrial procedures. Nevertheless, a comprehensive mathematical multiscale model that delineates the complex multiphase reaction system of HDPE pyrolysis is noticeably absent from the literature. Herein, we have formulated a novel neural network (NN)-inspired interpretable kinetics for plastic fast pyrolysis by automatically discovering the reaction pathways and kinetic parameters and later coupled it with a coarse-grained DEM-CFD model for the multiscale modeling of the plastic pyrolyzer. Microscale pyrolysis experiments in tandem microreactors were carried out to generate the data set for the neural network-inspired pyrolysis kinetics, while reactor-scale pyrolysis experiments in a fluidized bed were conducted to authenticate the multiscale model. This work provides a thorough apprehension of plastic pyrolysis at both the microscale and reactor scales, offering crucial guidance for the development and optimization of waste plastic pyrolysis processes.

Ammonia is a promising zero-carbon fuel for industrial and transport applications, but its combustion is hindered by flame instabilities, incomplete oxidation, and the formation of nitrogen oxides. Accurate and detailed kinetic models are critical for designing optimal burners and engines. Despite numerous mechanisms published in recent years, large discrepancies remain between model predictions and experimental data, particularly for NOx species. In this work, we have reviewed the literature to obtain the most up-to-date and reliable thermochemical and kinetic parameters for most reactions present in ammonia combustion, and for reactions for which these parameters are not available, we performed high-level calculations to determine them. The purpose of this was to minimize the number of estimated parameters used in model development. A new, detailed kinetic mechanism was then generated with the Reaction Mechanism Generator (RMG). To ensure physical consistency, geometry optimizations were carried out for all hypothesized 'edge' species, and any non-convergent or non-physical structures were excluded. The resulting mechanism was tested against experimental laminar burning velocities, ignition delay time, flow reactor species profiles, and jet-stirred reactor data, and compared with five recent representative mechanisms. Recently developed bath-gas-mixture rules were applied to a number of key reactions in the mechanism, and we found this to result in better agreement with experiment for a number of modeling targets. While the mechanism does not reproduce all experimental results, it demonstrates improved robustness without parameter tuning, thereby reducing the risk of over-fitting and enhancing predictive reliability under conditions relevant to practical applications.