Browse Articles

Bat communities of savanna biome in the Kruger National Park, South Africa

Enhanced K ⁺ Currents Contribute to Saltatory Conduction Impairment in Mechanically Demyelinated Aα-Fibers of Rats

Saltatory conduction of action potentials (APs) through nodes of Ranvier (NRs) is essential for the rapid and precise conduction of neuronal signals along heavily myelinated axons. However, in demyelinating diseases, saltatory conduction becomes impaired, leading to sensory and motor dysfunctions. At NRs of normal heavily myelinated nerves, APs are depolarized by voltage-gated Na + channels and repolarized mainly by K + currents flowing through two-pore-domain K + channels (K2P). Interestingly, although voltage-gated K + channels are present at and around NRs, they exhibit only limited activity and contribute minimally to nodal excitability and AP repolarization under physiological conditions. Here, we show that voltage-activated K + currents are significantly increased at NRs of motor Aα-fibers in the ventral roots of male and female rat lumbar spinal nerves following mechanical compression to induce acute demyelination. The increase in K + currents is associated with profound changes in intrinsic electrophysiological properties that reflect hypoexcitability at NRs and impairments of saltatory conduction. Voltage-gated K + channel blockers significantly reduce K + currents, restore excitability, and improve saltatory conduction at NRs of demyelinated Aα-fibers. Pharmacological profiling identifies Kv1.1 and Kv1.2 as the predominant voltage-gated K + channel subtypes contributing to the functional abnormality at NRs of demyelinated Aα-fibers. Collectively, our findings uncover a mechanistic link between abnormally enhanced K + currents via Kv1.1 and Kv1.2 channels and the impairment of saltatory conduction at NRs of demyelinated Aα-fibers, providing new insights into potential interventions for demyelinating disorders.

Programmable Functional Silicification of DNA Origami Nanostructures

ABSTRACT The silicification of DNA origami nanostructures offers a powerful strategy for enhancing their mechanical stability and resistance against detrimental environmental conditions. In the past years, several studies have investigated key aspects of the silicification process, resulting in a variety of established protocols. However, until now, the silica coating generally served as a passive protective layer or as the base for the further deposition of inorganic materials, but it did not carry any additional functionality itself. Here, we introduce two complementary, programmable approaches for the direct fabrication of functionalized silica coatings of DNA origami nanostructures. First, we synthesized a fluorescein‐bearing silica precursor which imparts fluorescence to the silica coating of both individual DNA origami nanostructures and crystals, enabling intracellular tracking of silica‐stabilized structures. Second, we employed a silica precursor containing a disulfide bridge to generate a redox responsive silica coating that degrades in a reducing environment. By introducing functionality at the precursor level, our approach establishes silicification as a modular platform for constructing responsive and traceable DNA‐based hybrid materials. These strategies expand the chemical scope of DNA nanotechnology and facilitate future applications in drug delivery and advanced materials science.

Electrophysiological, biochemical, and histopathological evidence of thymoquinone-mediated protection in cisplatin-induced cardiotoxicity

Different Learning Stabilization Processes between Wakefulness and Sleep

Visual perceptual learning (VPL) is initially fragile and must be stabilized after training to enable long-term retention. While sleep-dependent stabilization has been linked to changes in excitation/inhibition (E/I) balance, the neural mechanisms supporting stabilization during wakefulness remain unclear. Here, we investigated whether electrophysiological indices associated with sleep-based stabilization also characterize stabilization during posttraining wakefulness. Humans of both sexes completed visual training tasks with two competing training blocks separated by an awake rest period during which electroencephalography (EEG) was recorded. We conducted two studies targeting different stabilization states: one in which learning was vulnerable to retrograde interference and a control condition in which learning was largely stabilized through extended training and rest. Across studies, we estimated theta-band power and a criticality-based EEG index of E/I balance from posterior cortical sites. When stabilization was incomplete, elevated E/I balance during wakeful rest was associated with greater subsequent interference, whereas reduced E/I balance predicted stronger stabilization, paralleling findings previously reported during REM sleep. In contrast, when learning was fully stabilized, elevated E/I balance was associated with continued performance improvement rather than interference. Theta power showed a distinct, state-dependent pattern, predicting interference during wakefulness but not after stabilization. These findings indicate that wakeful stabilization of VPL shares a common inhibitory-dominant mechanism with REM sleep, while theta power is related to stabilization in different directions depending on whether the brain is wake or in REM sleep. Together, the results suggest that stabilization during wakefulness and REM sleep share partially common but not completely the same processing.

Engineering the Assembly Freedom of Donor–Acceptor Type Self‐Assembled Monolayers Toward Efficient and Stable Flexible Perovskite Photovoltaics

ABSTRACT Flexible perovskite solar cells (f‐PSCs) are promising contenders for portable and wearable photovoltaics, yet developing f‐PSCs that simultaneously achieve high power conversion efficiency (PCE) and superior operational stability, including mechanical robustness, remains challenging. Herein, we propose a strategy of modulating the assembly freedom of donor–acceptor type self‐assembled monolayers (SAMs) to address this issue. The newly designed 2FMPA‐BT‐PPA (PPA) SAMs exhibit higher assembly freedom on flexible ITO substrates compared to the previously reported 2FMPA‐BT‐BA (BA) SAMs, delivering two key benefits: first, they enable the formation of a higher‐quality monolayer via improved conformational adaptability and strengthened π–π stacking, which facilitates efficient carrier transport. Second, the SAMs’ conformational adaptability and uniformly tilted orientations can effectively dissipate interfacial strain under external mechanical loads, thereby enhancing the mechanical robustness of flexible devices. These synergies yield f‐PSCs with a champion PCE of 25.3% (26.3% for rigid device), alongside exceptional operational stability. Crucially, PPA‐based devices retain 98% initial PCE after 10 000 multidirectional bending cycles (3 mm radius) with no observable structural damage, outperforming BA‐based devices and all reported SAMs‐based f‐PSCs. This work offers donor‐acceptor SAMs design experiences for efficient, robust f‐PSCs, revealing assembly freedom's key role in interfacial carrier extraction and mechanical robustness.

BAASNet: boundary-aware deep learning for accurate polyp segmentation in colonoscopy

Abstract Colorectal polyps are primarily detected through colonoscopy, which plays a central role in early cancer prevention. Precise polyp segmentation supports treatment planning and diagnostic accuracy by providing masks that encode clinically relevant structures. Recent advancements in deep learning have led to several polyp segmentation models. However, performance remains hindered by challenges such as image noise, complex textures, indistinct boundaries, and diverse polyp morphologies. The high cost and time burden of manual annotation underscore the need for automated segmentation systems. To overcome these limitations, BAASNet, a Boundary-Aware Attention-Based Segmentation framework, is introduced for polyp segmentation. A boundary-aware loss function is integrated to improve performance, particularly in delineating polyp edges. The method is evaluated on nine publicly available datasets spanning five imaging modalities, including two center-wise polyp detection benchmarks, demonstrating strong generalization capability. On PolypDB, the model attains a mean Dice similarity coefficient (mDSC) of at least $$89.60\%$$ across all five modalities. Across all evaluated benchmarks, the proposed model achieves an average absolute improvement of approximately $$3.3\%$$ in Dice. Gains vary by dataset, ranging from approximately $$0.7\%$$ to $$4.7\%$$ relative improvement over the best previous results. These results demonstrate BAASNet’s potential for robust, real-time clinical deployment in automated colonoscopy workflows.

Machine‐Learning‐Guided Polarization‐Lattice Decoupling Enables Ultrahigh Energy Storage in Lead‐Free Dielectric Ceramics

ABSTRACT Achieving ultrahigh energy storage in lead‐free dielectric ceramics is fundamentally constrained by the intrinsic trade‐off between large polarization and high dielectric breakdown strength. Here, we establish an interpretable machine‐learning‐guided design framework that quantitatively links ionic descriptors with polarization behavior in ABO 3 ‐based dielectric matrices, enabling the rational identification of compositions with intrinsically high polarization potential. Guided by this strategy, a (Bi 0.275 Na 0.2255 K 0.0495 Ba 0.3 )(Ti 0.985 Hf 0.015 )O 3 ‐0.15(La 0.5 Sm 0.5 ) 2 Ti 2 O 7 (BNBT‐3) composition is discovered that exhibits an exceptional maximum polarization of 50.19 µC cm −2 . When processed via a viscous polymer process, the resulting BNBT‐3‐VPP capacitors achieve an ultrahigh breakdown strength of 1400 kV cm −1 and a recoverable energy density of 25.1 J cm −3 with high efficiency, placing them among the best‐performing lead‐free dielectric ceramics reported to date. Structural characterization combined with phase‐field simulations reveals that the outstanding performance originates from polarization‐lattice decoupling, where nanoscale polarization clusters and multiphase coexistence suppress long‐range ferroelectric order while enabling reversible polarization rotation. This work establishes a generalizable strategy that integrates interpretable machine learning with physically grounded materials design, providing a powerful route for discovering high‐performance dielectric energy storage materials.

Effect of mild hydrothermal modification on surface color, surface chemistry, UV-induced photodegradation and physico-mechanical performance of Pseudotsuga menziesii wood

Abstract Thermal modification at high temperatures improves the physical and surface properties of wood, but it often reduces its mechanical strength. This study examines mild hydrothermal treatment as an alternative method to enhance wood performance while limiting strength loss. Specimens of Pseudotsuga menziesii (Douglas fir) were treated with saturated steam at 140 ° C, 160 °C, and 180 °C for 0.5, 1, and 2 hours. The treated wood was analyzed for changes in colour, surface chemistry, dimensional stability, crystallinity index (CrI%), mechanical properties, and resistance to ultraviolet (UV) weathering. Mass loss increased from 0.86% to 19.61% with higher temperature and longer duration, indicating severe thermal degradation. Dimensional stability improved with treatment severity, reaching 33.39% at 180 °C for 2 hours. The crystallinity index also increased compared to the control, suggesting changes in cellulose structure. The wood became darker in color with increasing treatment intensity due to hemicellulose degradation, confirmed by FTIR analysis. Treated samples showed better resistance to UV-induced discoloration. In mechanical properties, surface hardness decreased by 7.41-68.81%, while bending modulus (MOE) and bending strength (MOR) declined by 8.36-61.76% and 5.26-81.84%, respectively, indicating reduced mechanical performance, particularly for samples modified at high temperature and for extended duration. Overall, mild hydrothermal treatment improved surface properties, dimensional stability, and UV resistance while causing less loss in strength compared to high-temperature treatments. This makes it a promising and sustainable method for enhancing wood performance.

Spatiotemporally Controlled Lysosomal Membrane Permeabilization Amplifies STING‐Driven Antitumor Immunity in Prostate Cancer

ABSTRACT Prostate cancer (PCa) remains a major clinical challenge due to therapeutic resistance and immunologically cold tumor microenvironment. Lysosomal membrane permeabilization (LMP)–induced lysosome‐dependent cell death offers an alternative route to eliminate resistant tumor cells and initiate immunogenic cell death, yet its efficacy is often limited by insufficient spatiotemporal control and immune activation. Here, we report a spatiotemporally programmable supramolecular nanoplatform (Cu‐P‐MSA) that integrates lysosome‐targeted sonodynamic therapy with tumor‐confined innate immune activation for PCa treatment. Cu‐P‐MSA is a modular self‐assembling peptide incorporating a PSMA‐targeting ligand, morpholine moiety, and cathepsin B–cleavable linker, enabling tumor‐selective uptake and in situ formation of fibrous sonosensitizer depots within lysosomes. Upon ultrasound irradiation, a glutathione‐responsive open‐shell sonosensitizer induces controlled LMP, simultaneously activating ferroptosis and pyroptosis and promoting immunogenic cell death. Meanwhile, tumor‐specific release of a STING agonist MSA‐2 elicits robust type I interferon responses, driving dendritic cell maturation and cytotoxic T‐cell infiltration. This coordinated lysosomal disruption–immune amplification strategy effectively reprograms the tumor immune microenvironment and suppresses both primary and distant tumors, with inhibition rates reaching 84.3% and 77.5%, respectively. Overall, this work establishes a spatiotemporally controlled supramolecular approach that integrates lysosomal disruption with innate immune activation to overcome therapeutic resistance and immunosuppression in PCa.

Therapeutic current estimation and leakage current safety of a cold air plasma jet on chronic wounds

Abstract Cold atmospheric air plasma jets (CAAPJs) are increasingly recognised for their efficacy in chronic wound treatment, with reactive oxygen and nitrogen species (RONS) traditionally cited as the primary therapeutic mechanism. However, their electromagnetic nature inevitably involves electrical interactions with wound tissue that have not yet been explicitly quantified. This work presents a quantitative framework distinguishing two coexisting electrical phenomena during CAAPJ treatment. First, the oscillating field at 30 kHz induces ionic currents in microscopic closed loops on the wound surface. Applying Ohm’s law with the measured electric field ( $$E \approx 10$$  V/mm) and wound exudate conductivity ( $$\sigma = 0.5$$  S/m), an induced current of 2–20 mA is obtained depending on exudate layer thickness, spanning and exceeding the therapeutic range of established Wound Healing Electrostimulation Devices (WHESDs). Second, the systemic patient leakage current — defined by IEC 60601-1 for Type B applied parts (limit: 100  $$\mu$$ A) — was measured under worst-case conditions using a brass target at zero resistance to earth, yielding values below 100 nA, more than three orders of magnitude below the normative limit. These results suggest that the long-standing RONS-versus-electric-field discussion in plasma medicine may be more productively framed as a question of complementary contributions: the therapeutic relevance of CAAPJs may lie in the co-delivery of both chemical and electrical mechanisms, while the device simultaneously guarantees full electrical safety. The two-current framework also provides a rigorous physical basis for the regulatory assessment of CAAPJ devices under IEC 60601-1.

Astrocyte TrkB.T1 Deficiency Disrupts Glutamatergic Synaptogenesis and Astrocyte–Synapse Interactions

Perisynaptic astrocyte processes (PAPs) contact pre- and postsynaptic elements to provide structural and functional support to synapses. Accumulating research demonstrates that the contact of synapses by PAPs is critical for synapse formation, stabilization, and plasticity. The specific signaling pathways that govern these astrocyte–synapse interactions, however, remain to be elucidated. Herein, we demonstrate the role of the astrocyte TrkB.T1 receptor, a truncated isoform of the canonical receptor for brain-derived neurotrophic factor (BDNF), in modulating astrocyte–synapse interactions and excitatory synapse development. Neuron–astrocyte coculture studies revealed that loss of astrocyte TrkB.T1 disrupts the formation of PAPs. To elucidate the role of TrkB.T1 in synapse development, we conditionally deleted TrkB.T1 in astrocytes in mice of either sex. Synaptosome preparations were employed to probe for TrkB.T1 localization at the PAP, and confocal three-dimensional microscopy revealed a significant reduction in synapse density and astrocyte–synapse interactions across development in the absence of astrocytic TrkB.T1. Furthermore, conditional knock-out of astrocyte TrkB.T1 alters motor learning and experience-dependent astrocyte–synapse interactions. These findings suggest that BDNF/TrkB.T1 signaling in astrocytes is critical for normal excitatory synapse formation in the cortex and that astrocyte TrkB.T1 serves a requisite role in astrocyte synapse interactions. Overall, this work provides new insights into the molecular mechanisms of astrocyte-mediated synaptogenesis.

Breaking the Permeability–Selectivity Trade‐Off With Irreversible‐Knot Rubbery Organic Frameworks

ABSTRACT The permeability–selectivity trade‐off fundamentally constrains polymeric membranes, rooted in the dichotomy between chain flexibility and precise molecular sieving. The emerging concept of rubbery organic frameworks (ROFs) aims to bridge this gap, yet its reliance on reversible covalent chemistry inherently compromises structural stability. Here, we introduce an irreversible‐chemistry paradigm by programming β ‐ketoenamine “irreversible knots” into flexible polydimethylsiloxane (PDMS) networks via enol–keto tautomerization. This approach synergistically co‐programs crosslinking density and chain rigidity, yielding a stabilized and optimized microstructure. The resulting membrane transcends the classic trade‐off, delivering a record‐high flux of 5.4 kg m −2 h −1 for ethanol/water separation—three times higher than conventional PDMS—while maintaining a separation factor of 9.2. The “rigidity‐programming” strategy demonstrates remarkable versatility, achieving top‐tier performance across diverse separations spanning representative organic/water and gas‐pair systems. Beyond performance, the membranes exhibit scalable fabrication, robust anti‐swelling stability, and long‐term operational durability, highlighting their practical potential for industrial deployment. This work establishes irreversible chemistry as a general paradigm for polymer network design, providing a robust platform to overcome traditional limitations from molecular separation to flexible functional materials.

Adaptive hybrid framework for low-latency DDoS defense in consumer-centric SDN architectures

Abstract Software-Defined Networking (SDN) increases networking flexibility, scalability, and programmability by separating the control plane from the data plane. Nevertheless, this structure is also vulnerable to DDoS, which may result in performance degradation and service unavailability. We propose a new adaptive hybrid real-time DDoS detection and mitigation framework designed for consumer applications with tight low latency constraints such as telemedicine, online gaming, and video streaming services. The suggested architectural model incorporates four well-known techniques (LSTM-based DeepPredict-DDoS, Reinforcement Learning-based Adaptive DeepPredict-DDoS, Genetic Algorithm-based DeepPredict-GA, and ARIMA-based DeepPredict-ARIMA) through an innovative decision-making engine. Experimental results show an average detection accuracy of 97.08%, reduced latency by 80%, and packet loss rate as low as 0.027%. These properties make the solution scalable, efficient, and effective for consumer-level SDN systems. Graphic Abstract

Ion Diffusion‐Induced Multi‐Interface Reconstruction for High‐Resolution Perovskite X‐Ray Flat‐Panel Detectors

ABSTRACT A critical challenge with state‐of‐the‐art perovskite x‐ray flat‐panel detectors (FPDs) is their limited spatial resolution, primarily due to the presence of multiple poorly integrated interfaces. In this study, we report high‐resolution perovskite FPDs that achieve a record modulation transfer function (MTF) among polycrystalline perovskite direct‐conversion x‐ray FPDs of 6.2 line pairs per millimetre (lp mm −1 ) with a large imaging area of 8.5 × 8.5 cm 2 through an interface reconstruction strategy specifically tailored for perovskites. We reveal that Fick's law‐guided ion diffusion across hundreds of microns‐thick perovskites contributes to a reconstructed x‐ray sensing layer with highly integrated interfaces and a gradient energy band alignment. As such, we have realized an ultrasensitive x‐ray detection with a leading sensitivity‐to‐dark current ratio (2.61 × 10 11 µC Gy air −1 A −1 ) and outstanding stability under ambient conditions over 5760 h. The prototype perovskite FPDs exhibit a detective quantum efficiency (76.9%) and enable high‐resolution x‐ray imaging at a low dosage (0.98 µGy air ), substantially lower than previous polycrystalline perovskite FPDs. Our multi‐interface reconstruction strategy successfully addresses long‐standing issues in perovskite FPDs, advancing their progress from laboratory prototypes to commercial applications in digital radiography and industrial inspection.

A comparative evaluation of deep learning architectures, loss functions, and ensemble strategies for underwater fish species classification

Abstract The accurate classification of underwater fish species is important for biodiver-sity monitoring, sustainable fisheries management, and aquaculture. However, underwater fish classification remains challenging due to class imbalance, varying imaging conditions, and high visual similarity among species. This study presents a comprehensive evaluation of deep learning architectures, loss functions, and ensemble strategies for underwater fish species classification. Using the Mark Daniel Lampa Kaggle Fish Dataset comprising 13,304 images across 31 species, duplicate images were removed using a combination of MD5 hashing and perceptual hashing, resulting in a cleaned dataset for model training and evaluation. EfficientNet-B0, EfficientNetV2-S, Swin-Tiny Transformer, and ConvNeXt-Tiny were trained using both class-weighted cross-entropy loss and focal loss. The experimental results indicate that class-weighted cross-entropy loss consistently provided stronger performance for individual models, whereas focal loss produced a marginal improvement when used with the soft-voting ensemble. Among the individual models, EfficientNet-B0 achieved the highest accuracy of 94.81%. Ensemble learning further improved performance, with the four-model soft-voting ensemble achieving a test accuracy of 97.21% and a macro-F1 score of 0.9678 on the cleaned Kaggle dataset. An external dataset of 310 internet-sourced fish images was additionally used to assess model generalization under domain-shift conditions, where the ensemble achieved an accuracy of 76.39% and a macro-F1 score of 0.7335. While performance decreased substantially under domain shift, the results highlight the challenges of generalizing fish-species classification models beyond the training distribution. Fish species classification represents an important component of future end-to-end underwater monitoring systems incorporating fish detection and localization modules. The study provides a benchmark for future research on underwater fish-species classification, ensemble learning, and real-world deployment. The source code is openly available on GitHub and permanently archived on Zenodo (DOI: https://doi.org/10.5281/zenodo.19315919 ).

Laser‐Induced Synthesis and Passivation of Intrinsically Antioxidative Nano‐Copper for Durable Electronics

ABSTRACT The ever‐increasing costs, along with rigorous storage requirements, are the primary obstacles to the sustainable development of pure metallic inks for advanced electronics. The chemically reductive conversion of copper (Cu) from its much cheaper oxides offers a comparable alternative, whereas suffering from the intrinsic oxidation issues that lead to conductivity failures. Here, we report laser‐induced synthesis and passivation of robust nano‐Cu, achieving superior durability under harsh humid‐thermal conditions (190°C and 90°C, 90% RH) in the absence of additional encapsulations. Through selective and controllable metallization, the nascent Cu is equipped with well‐defined dual‐ligand barriers at interface, which are functionalized by formate crystallographic coordination and oleylamine hydrophobization. Such surface modification imparts the as‐formed Cu with limited resistance increase over prolonged humid‐thermal fluctuations. As demonstrations, we create conformal and wearable sensor systems with passivated Cu interconnects that withstand humid‐thermal erosion and provide signal alerts. This laser technology bridges the gap between high‐performance Cu nanomaterials and cost‐effectiveness, empowering endurable electronics for real‐life extremes.

A preliminary evaluation of SPEEK nanofibers as multi-drug carrier delivering diclofenac, paracetamol and alcoholic extract of Strobilanthes ciliata

Selective Neural Responses to Conspecific Vocalizations in Marmoset Area 32

Understanding how primate frontal circuits encode socially informative vocalizations is central to elucidating the neural basis of communication. In common marmosets, functional MRI has identified robust selectivity for conspecific vocalizations in the pregenual anterior cingulate cortex (pgACC; area 32), but the underlying neural dynamics driving this response remain unknown. Here, we investigated this by recording single-neuron activity in area 32 of five awake marmosets (three female and two male) using acute and chronic Neuropixels probes and Utah microelectrode arrays during presentation of a standardized cross-species auditory stimulus set comprising marmoset, macaque, and human vocalizations as well as a suite of nonvocal sounds. Of 1,713 neurons recorded across 17 sessions, 945 (55%) showed significant auditory responsiveness, and 362 (21%) were categorically selective. Selective neurons exhibited their strongest responses to marmoset calls, and population preference indices revealed a robust bias toward conspecific vocalizations. Temporal population analyses showed rapid encoding: decoding accuracy for detection of marmoset calls exceeded chance within ∼50–100 ms, and representational similarity analysis demonstrated that marmoset calls formed a distinct cluster separate from macaque, human, and nonvocal stimuli. These results provide the first direct evidence that ACC area 32 contains a specialized neural representation for conspecific vocal signals. By linking fMRI-defined selectivity to single-neuron population dynamics, our findings identify area 32 as a key node in primate vocal communication networks and illuminate how frontal circuits transform auditory input into socially meaningful categorical information.

Cu‐Pd Dual Single Atoms Promoting Selective CO ₂ Photoreduction to C ₂ Products in Seawater

ABSTRACT The solar‐powered CO 2 conversion via the photocatalysis route offers a sustainable pathway toward carbon neutrality while mitigating energy/environmental pressure. Nevertheless, the selective and efficient conversion of CO 2 via photoreduction to C 2 products remains a formidable challenge. Here, we engineered a dual‐single‐atom photocatalyst by controllably embedding Pd and Cu single atoms into a TiO 2 matrix. The optimized catalyst (Cu 0.5 Pd 0.5 /TiO 2 ) exhibits the outstanding yield (119.2 µmol/g cat ) and selectivity (84.8%) for acetic acid production from CO 2 photoreduction, performed in seawater and in a photothermal‐aided reactor. Various in situ/ex situ characterizations were employed to investigate atomic‐level structure‐performance correlation and reaction mechanism in practical condition. In situ x‐ray photoelectron spectroscopy, in situ atomic force microscopy‐Kelvin probe force microscopy, transient‐state surface photovoltage, and in situ electron paramagnetic resonance (EPR) collectively indicate that loading Pd and Cu single atoms onto TiO 2 apparently accelerates charge kinetics. This modification results in increased photogenerated electrons for CO 2 reduction, facilitating C─C coupling and hydrogenation reactions. Additionally, in situ infrared (IR) spectroscopy and theoretical computations affirm the pivotal function of Pd single atoms for lowering the energy barrier to form the * OCCO intermediate, apparently improving selectivity for acetic acid production. Overall, our work presents an innovative approach to tackle kinetic and thermodynamic challenges for light‐induced CO 2 ‐to‐C 2 conversion.