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High-performance ultraviolet (UV) photodetectors (PDs) with rapid response and high responsivity are crucial for environmental monitoring and optoelectronic applications.However, traditional N-type porous silicon (N-PSi) PDs often suffer from limited carrier separation efficiency and significant recombination losses. Here, we report an ultra-sensitive UV-PD based on a novel NP-type porous silicon (NP-PSi) thin film architecture, demonstrated for the first time. By implementing a hydrogen peroxide (H 2 O 2 )-assisted electrochemical anodization technique, a hierarchical and uniform PSi nanostructure was successfully engineered on an N-Si/P-Si layered template. The integrated NP-junction provides a robust built-in electric field that effectively facilitates the spatial separation of photogenerated electron-hole pairs while suppressing recombination. Under optimized conditions, the NP-PSibased PD exhibits a remarkable sensitivity of 67808.33%, which is approximately 28-fold higher than that of conventional N-PSi counterparts. Furthermore, the device achieves a superior responsivity of 38.60 A/W and an ultra-fast response time (rise/fall times of 0.03 s).These results underscore the synergistic effect of built-in field assistance and refined nanostructured engineering, offering a promising strategy for developing next-generation, high-gain silicon-based UV sensing technologies.
The migration and separation behavior of photoinduced electrons and holes are crucial factors for photocatalytic efficiency. Elevating the reaction temperature accelerates the kinetics of carrier migration, while the construction of heterojunctions and built-in electric fields facilitates effective carrier separation. Herein, a Bi2O2CO3-PbTiO3-PVDF sponge with outstanding photothermal piezo-photocatalytic performance was fabricated for the first time. The photocatalytic performance of Bi2O2CO3-PbTiO3-PVDF sponge is 2.69 times that of PbTiO3-PVDF sponge and the piezo-photocatalytic performance is better than both photocatalytic and piezocatalytic performance. The synergism of built-in electric field and photothermal effect not only accelerates carriers but also separates them, creating more active carriers for elevated catalytic performance. Furthermore, the excellent elasticity and stability make the porous flexible sponge structure suitable for practical applications. Density functional theory (DFT) calculations and finite element simulations further support the experimental data and the proposed reaction mechanism. This work focuses on the controlling of carriers through electric fields and photothermal effects, providing a novel strategy for the design and development of high-performance photocatalytic composites.
The built-in electric field (BIEF) is a fundamental driving force governing the separation, transfer, and lifetime of photogenerated charge carriers, thereby dictating the activity of photocatalysts. Herein, a local p-π conjugation regulation strategy was developed to tailor the BIEF in covalent organic frameworks (COFs) as advanced photocatalysts. Three COFs of NKU-191, NKU-191-OH, and NKU-191-OMe, featuring robust acid-base resistance, high stability, and high specific surface area, were synthesized via Schiff base reactions under mild conditions. Without altering their intrinsic backbone structure, the photocatalytic hydrogen evolution activity was enhanced from 4.8 mmol g-1 h-1 (NKU-191) to 35.6 mmol g-1 h-1 (NKU-191-OMe). Comprehensive characterizations and systematic analysis revealed that the introduction of electron-donating groups effectively strengthens the local p-π conjugation within the COF skeletons, which in turn reinforces the BIEF intensity. This enhanced BIEF accelerates the separation and migration kinetics of photogenerated charge carriers, thereby enabling remarkable photocatalytic activity. This work not only establishes a facile synthetic protocol for synthesizing COFs with high specific surface areas and high stability but also clarifies the regulatory role of local p-π conjugation in regulating the BIEF intensity of COF-based photocatalysts, providing valuable insights for promoting the rational design and development of high-performance COF-based photocatalysts.
Arecoline and arecaidine, the primary alkaloids in betel nut, are responsible for betel-quid chewing addiction. Vaccination against small molecules is a promising approach but limited by their weak immunogenicity. Invariant NKT cells (iNKT cells) serve as a key bridge between innate and adaptive immunity, providing cognate B cell help and enhancing antibody responses. Herein, we developed two fully synthetic, structurally defined arecoline (Arec) and arecaidine (Areca) vaccine candidates with NKT cell agonist α-Galactosylceramide (αGalCer) as a built-in adjuvant. Both these vaccine candidates efficiently promoted class switching from IgM to IgG, with a response dominated by the IgG1 and IgG3 subclasses. Cross-reactivity analysis found that antibodies elicited by Arec-αGalCer recognized both arecoline and guvacoline but with reduced affinity for the latter. Meanwhile, antibodies induced by Areca-αGalCer specifically recognized arecaidine, and the affinity was comparable to that of Arec-αGalCer antibodies for arecoline. In vivo, Arec-αGalCer vaccination caused a modest mitigation of arecoline-induced hypothermia. In addition, a mixed vaccine formulation elicited a broader serological response than the single vaccine.
We report a highly sensitive electric field (E-field) sensor based on a multilayer MoS2/multilayer graphene (ML-MoS2/MLG) heterostructure with built-in tensile strain. The MLG functions as a bottom source-drain contact, thereby enhancing the charge injection into the ML-MoS2 channel. The unique device geometry further induces tensile strain in the ML-MoS2 channel by bending it over the MLG edge, which improves the carrier mobility through reduced electron-phonon scattering. As a result, the ML-MoS2/MLG device achieves an average carrier mobility of 75.7 cm2 V-1 s-1, with values up to ∼108 cm2 V-1 s-1 at room temperature, significantly exceeding that of conventional metal-contacted MoS2 devices. Upon exposure to external E-fields, the device exhibits polarity-dependent variations in the drain current arising from field-induced carrier transfer between the ML-MoS2 channel and trap states at the SiO2/channel interface. The E-field sensitivity, defined as the relative change in drain current, increases linearly with the E-field magnitude. Owing to the enhanced charge injection and improved carrier mobility, the ML-MoS2/MLG device demonstrates superior E-field sensing performance, achieving a sensitivity around three times that of metal-contacted MoS2 devices. Notably, the minimum detectable E-field reaches ∼100 V/m, highlighting its potential for atmospheric E-field monitoring toward lightning detection applications.
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Ceribell Inc.'s point-of-care electroencephalographic (EEG) system and artificial intelligence-based Automated Seizure Burden Estimator (ASBE; ClarityPro) have US Food and Drug Administration clearance for diagnosing electrographic status epilepticus (ESE). The AccuRASE study using ASBE version 6 (V6) showed high negative predictive value (NPV) but limited sensitivity and positive predictive value (PPV) at certain thresholds. Version 8 (V8) is the updated algorithm trained with additional EEG samples. We tested V8 on the previously used rapid-response EEG test dataset (not used to train V8). Sensitivity, specificity, PPV, and NPV were compared against blinded expert annotations. ESE, ESE and possible ESE (ESE/pESE), electrographic seizures (ESz), and Esz with highly epileptiform patterns (Esz/HEPs) at burden thresholds of >1%, >10%, >20%, >50%, and >90% were analyzed. Additionally, we evaluated the built-in ESE and ESE/pESE alerts (≥90% over 5 min, ≥20% over 1 h, or ≥10 continuous minutes of seizure). V8 showed 100% sensitivity at lower thresholds (>10%-20%) for ESE (V6 .86), without a specificity loss (V8 .9 vs. V6 .85), and retained NPV (V8 1.0 vs. V6 .99). Sensitivity at 50% threshold was .86 for ESE (V6 .71), and specificity was .94 (V6 .91); PPV for ESE was 30% for ESE and 80% for ESE/pESE. The specificity for ESE remained high for 90% burden (V8 .96, V6 .97) but the sensitivity dropped (.29 vs. V6 .43). Sensitivity was lower for ESz/HEPs (.54 → .44 at >10% threshold), although the algorithm is not designed to detect HEPs. Built-in alert analysis showed significantly improved sensitivity for ESE (.57 → .86); specificity and NPV remained high (>.94). ASBE V8 showed meaningful improvements in sensitivity, continued excellent NPV, and improved built-in alert accuracy for ESE and ESE/pESE. The best seizure burden threshold for ruling out ESE now is 20% (vs. 10% with V6) and that for ruling in ESE/pESE with reasonable certainty is 50% (vs. 90% with V6).
Although prophylactic vaccinations for human papillomaviruses (HPVs) have been approved, these vaccines lack therapeutic efficacy and cannot eradicate pre-existing infections. Although epitope-based vaccines represent a promising therapeutic vaccine platform, their anti-tumor efficacy has been limited due to low immunogenicity. This study aimed to apply bioinformatics tools to design a built-in adjuvant therapeutic candidate vaccine targeting HPV16 infections and associated cancers. The designed vaccine consists of HPV16 E6 and E7 epitopes conjugated to the domain 4 of pneumolysin (Ply4) from Streptococcus pneumonia, which serves as a potential toll-like receptor 4 (TLR4) agonist. In silico analyses were performed to evaluate the vaccine's physicochemical properties, antigenicity, immunogenicity, and binding interactions with the TLR4 receptor. The designed vaccine was expressed in E. coli and its expression was confirmed by SDS-PAGE and Western blot analysis. In silico analysis predicted that the designed vaccine could have desirable qualities, including non-toxicity, non-allergenicity, antigenicity, immunogenicity, hydrophilicity, and stability. Docking analysis between the vaccine and the TLR4 proteins predicted a high binding capacity and efficient binding. Furthermore, immunoinformatics tools showed that the vaccine could induce robust immune responses, specifically helper and cytotoxic T-cell responses, and promote the production of IFN-γ. The vaccine was successfully expressed in the E. coli system after being cloned into the pET28a vector. SDS-PAGE and Western blotting assays confirmed the purification of the target protein. The novel built-in adjuvant therapeutic candidate vaccine is a rationally designed construct for eradicating pre-existing HPV infections and HPV-induced cervical cancers that warrants further preclinical evaluation.
This study aimed to evaluate macular and peripapillary optical coherence tomography angiography (OCTA) parameters in mild and moderate myopic eyes compared with emmetropic controls, utilizing built-in projection artifact removal (PAR) and rigorous magnification correction. This prospective cross-sectional study included 218 eyes (90 emmetropic, 59 mild myopic, and 69 moderate myopic) of adults aged 20-52 years. Macular and optic disc OCTA scans were acquired using the RTVue-XR Avanti system with the built-in 3D-PAR algorithm enabled. Lateral magnification correction was performed post-hoc using the full Littmann-Bennett method ([Formula: see text]), with quadratic scaling applied to area measurements to ensure absolute mathematical precision. Eyes with axial length (AL) > 25.8 mm were excluded to minimize segmentation artifacts associated with posterior staphyloma and globe deformation. Deep macular vessel density (VD) demonstrated a significant stepwise reduction across groups: emmetropia (45.92% ± 8.83) → mild myopia (41.44% ± 7.94) → moderate myopia (39.64% ± 10.74) (ANOVA P < 0.001). In contrast, superficial macular VD showed no statistically significant difference between mild myopia and emmetropia, with a significant decline observed only in moderate myopia (P < 0.05). The Foveal Avascular Zone (FAZ) area was significantly enlarged in moderate myopia (0.35 ± 0.12 mm², P < 0.001) but remained stable in mild myopia. In multivariable regression, spherical equivalent (SE) was the dominant independent predictor of deep VD reduction (Standardized β = -0.41, P < 0.001). Retinal microvascular attenuation appears to begin early in the deep capillary plexus (even in mild myopia), whereas superficial plexus and FAZ changes are features of more advanced severity. These findings suggest that DCP vessel density is a sensitive population-level indicator of early pathophysiological remodeling, although its current diagnostic utility for individual clinical monitoring is limited by measurement variability relative to the effect size.
Modern connected vehicles rely on the controller area network (CAN) to disseminate safety-critical in-vehicle information, including sensor-related and vehicle-state signals such as engine revolutions per minute (RPM) and gear state, among electronic control units (ECUs). Because CANs lack built-in authentication and encryption, malicious message injection and spoofing can compromise the integrity and availability of vehicular sensing and control functions. Existing deep-learning-based intrusion-detection systems (IDSs) show a clear trade-off: supervised methods perform well on known attacks but rely on costly labels, whereas unsupervised methods can identify unseen attacks but often suffer from high false-positive rates. To address these limitations, this paper proposes a semi-supervised generative adversarial network (SGAN) framework for CAN bus intrusion detection that combines image-based CAN representation with adversarial learning. Consecutive CAN messages are converted into 64×9 grayscale images, and the proposed framework is trained in three phases. First, the discriminator establishes an initial decision boundary using a small labeled subset. It then refines this boundary through distribution-level likelihood objectives and generated samples. Finally, the generator is trained to produce realistic samples capable of deceiving the discriminator. The proposed method was evaluated on the Hacking and Countermeasure Research Lab (HCRL) car-hacking dataset using leave-one-class-out experiments to simulate unknown attacks and achieved an average accuracy of 99.73% and an average F1-score of 99.63% on unknown attacks. Moreover, with only 0.21 M parameters and 3.25 M floating-point operations (FLOPs), the model is well suited for resource-constrained in-vehicle platforms. These results indicate that the proposed framework can serve as a practical cybersecurity component for protecting CAN-carried data in vehicular sensing applications.
This study developed a transcription-based, multimodal cell-free biosensing platform for the sensitive detection of α-amanitin, a deadly mushroom toxin. The core design of the platform converts α-amanitin-dependent RNA polymerase inhibition into tunable, genetically encoded outputs. The system uses programmable DNA templates to produce specific RNA sequences, which are then processed in parallel by three independent signal transduction modules: fluorescence using the Malachite Green (MG) light-up RNA aptamer; colorimetry through RNA-directed gold nanoparticle aggregation; and enzymatic catalyzed by an in vitro transcribed G-quadruplex/hemin (G4/hemin) RNAzyme. This multimodal output strategy provides built-in self-validation for a single detection event, significantly enhancing result reliability. Evaluations demonstrated that the biosensor exhibits high sensitivity, with limits of detection (LODs) of 1.30 μg/mL for fluorescence detection, 0.69 μg/mL for AuNP-based colorimetric detection, and 6.29 μg/mL for RNAzyme-based colorimetric detection, and showed good selectivity and robust performance in complex matrices. Lyophilization tests confirmed operational stability for potential on-site applications. This work establishes a versatile, modular framework for transcription-based biosensing, with potential extension to other RNA polymerase inhibitors.
Artificial intelligence (AI)-aided electrochemical biosensing is becoming integral parts in numerous scenarios. However, existing systems generally perform algorithms in external signal processing units. The necessity of analog-to-digital conversion and data transfer results in high complexity, low working efficiency and concern of privacy. In-sensor computing has made great progress in perceiving and processing physical signals, which, nevertheless, faces inherent restriction in biochemical scenarios due to the lack of aqueous compatibility and the necessity of an array. Here, we realized neuromorphic electrochemical in-biosensing computing using just a single photoelectrochemical transistor, which can itself not only perform multi-target biosensing but also constitute a single-layer algorithmic classifier. It is based on a rationally designed multi-gate photoelectrochemical transistor, whose architecture and synaptic memory enable built-in vector-matrix multiplication and light-tunable responsivity. The proof-of-concept is demonstrated by simultaneous sensing and classification of biomarker microRNA fingerprints in real biological samples, which opens the possibilities for next-generation AI-driven electrochemical biosensing with edge computing ability.
Visible light driven photocatalytic hydrogen evolution using sacrificial agents is a promising route for solar to chemical energy conversion. However, achieving efficient charge separation and migration in heterogeneous semiconductor photocatalysts through a one-photon excitation process remains challenging. In this work, we report a one-photon excitation approach by integrating polymeric carbon nitride (PCN) with gadolinium oxychloride (GdOCl) by a molten salt method for photocatalytic hydrogen evolution over PCN/GdOCl photocatalysts. Notably, the optimized PCN/GdOCl-1.5 exhibits an impressive H2 performance with a yield of 86.38 μmol h-1, surpassing bare PCN by a factor of 4.7. Additionally, PCN/GdOCl-1.5 showcases enhanced photocatalytic H2 production with an apparent quantum efficiency (AQE) of 6.17% under monochromatic light at 420 nm. The improved separation of photogenerated charge carriers and reduced recombination rates in PCN/GdOCl-1.5 were evidenced by photoluminescence (PL) and electrochemical impedance spectroscopy (EIS). In addition, the photocatalyst displays outstanding stability and retains its photocatalytic performance over five successive reaction cycles, thereby emphasizing the potential of PCN/GdOCl-1.5 for efficient and sustainable hydrogen evolution. The enhanced H2 evolution performance is attributed to visible light excitation of PCN, followed by GdOCl assisted interfacial charge regulation and built-in electric field (BIEF) driven charge carrier migration. This work provides insight into the design of PCN based heterojunction photocatalysts for sacrificial agent assisted photocatalytic hydrogen evolution.
Dipolar excitons typically emerge in weakly coupled van der Waals heterostructures (vdWHs), where electrons and holes are confined in different layers. However, the tunability of these extrinsic interlayer dipolar excitons under external out-of-plane electric fields is constrained by built-in interfacial electric fields and significant nonradiative processes. Here, we propose a dipolar exciton in monolayer Hf2SiCO2, where vertically separated electrons and holes reside in a single layer of several atoms' thickness. The dipolar excitons in the two X valleys, connected by rotoreflection symmetry, possess alternating antiparallel out-of-plane electric dipole moments, which are termed an alterexciton. These dipolar excitons exhibit electrically tunable polarization in a single valley, which further leads to a single-valley excitonic insulator under an increasing electric field. Because of the optical selection rules, the layer-locked valley excitons exhibit linear dichroism and valley-dependent electrical tunability. Furthermore, under linearly polarized light, the Coulomb-bound electrons and holes of the excitons are simultaneously deflected by the Berry curvature in each layer-locked valley, giving rise to the exciton Hall effect. These results not only contribute to the valley-polarized manipulation of dipolar excitons but also facilitate the exploration of single-valley single-photon emitters.
Tata Memorial Centre maintains one of the largest hospital-based cancer registries (HBCR) in India. The registry captures demographic, diagnostic, and treatment information from the electronic medical record (EMR), while Patterns of Cancer Care and Survival Studies (POCSS) collect additional clinical variables including comorbidity, life style, family history of cancer, histopathology, and detailed treatment and follow-up information. As these data are distributed across multiple EMR modules, abstraction is time-consuming. In 2021, the HBCR software was upgraded to the Onco-Insight platform with EMR integration to facilitate automated data retrieval. This study evaluated the platform for data completeness and abstraction turnaround time. An observational evaluation using an intra-observer comparison design was conducted. Trained registry abstractors collected data using two approaches: conventional manual abstraction and the EMR-integrated Onco-Insight platform. The platform retrieves structured variables directly from the EMR while allowing manual entry for variables unavailable in structured format. Automated mapping from EMR modules to registry variables was implemented for sociodemographic, diagnostic, and treatment domains. Built-in validation rules, range checks, and mandatory field alerts were incorporated to enhance data quality. Data completeness was assessed for sociodemographic, diagnostic, and treatment variables and summarized as the proportion of cases with retrievable information. Data entry turnaround time was defined as the mean time required for abstraction and entry per case and assessed by cancer subsite for both HBCR and POCSS variables. Cases diagnosed and treated in 2021 were evaluated. Of 37,495 registrations in 2021, 32,359 were confirmed cancer cases. Sociodemographic variables were retrieved for all cases (97.59%). Diagnostic variables were retrieved for 13,987 cases (43.22%), and treatment variables for 16,835 cases (50%), followed by manual validation. Manual review was mainly required for patients treated outside the institution, referred between TMC centers, or to distinguish initial therapy from treatment for recurrence or progression. Follow-up information after completion of cancer-directed therapy remained fully manual. Mean abstraction time decreased substantially for HBCR from 27.17 ± 4.40 minutes to 15.07 ± 3.00 minutes. The EMR-integrated Onco-Insight platform reduced abstraction time and improved efficiency while enabling unified entry for HBCR and POCSS. Further EMR standardization, expanded integration, and AI-enabled data extraction could strengthen registry efficiency and cancer surveillance.
Facing the challenge of achieving efficient and sustainable hydrogen peroxide (H2O2) production, a promising strategy is developing highly selective electrocatalysts with controllable synthesis, structural design and performance optimization. Herein, an interfacial acid sites-mediated ZnSe/ZnO heterojunction is synthesized for highly selective two-electron oxygen reduction reaction (ORR) toward H2O2 production. Experimental and theoretical results reveal that surface selenization induced reconstruction, forming a synergistic interface with a built-in electric field and tailored oxygen vacancies (Ovs), which collaboratively optimize the electronic structure and accelerate reaction kinetics of two-electron ORR. Moreover, interfacial unsaturated Zn2+ sites and OVs served as Lewis acids sites to enhance O2 adsorption and activation, while Brønsted acids sites were liable to donate protons to promote *OOH formation. Consequently, a ZnSe/ZnO ‖ ZnO flow cell enabled paired electrolysis for concurrent H2O2 production with a high H2O2 yield of 754.4 M gcat -1 over 12 h. A rechargeable Zn-H2O2 cell using ZnSe/ZnO cathode delivered a power density of 11.99 mW cm-2 as a self-sustaining process for simultaneous on-site H2O2 production and electrical energy generation. This work offers a sustainable route for on-site H2O2 synthesis with improved energy efficiency, advancing green chemistry and circular economy.
This study reports an integrated dual-signal microfluidic immunosensor for point-of-care detection of ovalbumin-specific IgE (OVA-sIgE) in interstitial fluid. A pH-responsive composite material, ZIF-8 encapsulating phenolphthalein and surface-conjugated with antimouse OVA-sIgE (ZIF-8@PP∼Abs), was synthesized and characterized. Structural and elemental analyses confirmed the successful synthesis and antibody functionalization, while the composite retained strong alkaline-triggered colorimetric response due to ZIF-8 decomposition and subsequent phenolphthalein release. Carbon dots (CDs) show concentration-dependent fluorescence, which is effectively quenched by ZIF-8@PP∼Abs. A paper-based chip coimmobilized with CDs and OVA antigen enabled simultaneous colorimetric and fluorescent detection. The colorimetric pathway operates as a "turn-on" system through alkaline-induced color change, while the fluorescent pathway functions as a "turn-off" system via CDs quenching by ZIF-8@PP∼Abs. Quantitative analysis using the green channel─identified as the most sensitive through RGB decomposition─showed linear responses across 0.06-8.00 ng/mL OVA-sIgE, with detection limits of 0.026 ng/mL (colorimetric) and 0.032 ng/mL (fluorescence). The dual-signal design, featuring built-in cross-verification, demonstrates high reliability through favorable spike-and-recovery results. ROC analysis shows AUC values of 0.9999 for both signals, with a significant reduction in false positives. To achieve operational integration, a custom-designed negative-pressure-driven microfluidic chip was developed, incorporating all necessary steps into a negative-pressure valve-controlled microfluidic chip. The chip demonstrated robust fluid handling, repeatable operation, and user-friendly functionality. The integrated sensor exhibited excellent selectivity, strong anti-interference capability against common interstitial fluid constituents. This work presents a practical, accurate, and reliable platform for decentralized allergy testing, merging advanced nanomaterials, dual-mode transduction, and microfluidic engineering.
Shannon entropy is the most common measure that one could use to check if a data source has random behaviour or not. A value close to the maximum is usually considered as evidence that the source is "random enough". The present paper shows that this criterion alone is not enough. A deterministic logistic map driven at r=3.9999 reaches 94.97% of the Shannon maximum, yet it is fully predictable once we look at the built-in patterns: its permutation entropy drops to 77.01% of the maximum and its sample entropy falls to 0.67, against 2.33 for a high-quality pseudo-random generator (PRNG). Building on this observation, we combine four entropy measures-Shannon, Rényi, permutation, and sample-into a single diagnostic profile of the analyzed source. In order to validate our approach with practical, real life data, we test it on 2538 official draws of the Romanian Loto 6/49 lottery, recorded between August 1993 and April 2026. The lottery historical data set is very close to a high-quality PRNG (pseudo-random number generator) from the point of view of all four measures. We also observe that the entropy deficit of both the lottery and the PRNG decays as a power law with exponent α≈-0.96; in contrast, the logistic map sits at α≈-0.07. A Random Forest classifier trained only on the entropy profile reaches 78% accuracy on the analyzed four-way classification task (lottery, PRNG, logistic map, biased distribution), but scores 55.7% on the binary lottery-versus-PRNG task, consistent with chance. The method introduced in this study is domain-independent and applies directly to RNG certification, cryptographic key auditing, and any setting where structured pseudo-randomness has to be ruled out.
Conversational AI is now deeply embedded in adolescents' mental health help-seeking and emotional lives, creating both risks and a rare population health opportunity. This debate piece argues that the key question for researchers and implementers to consider is no longer whether adolescents should use general-purpose chatbots for mental health support, but rather how scientists, regulators, and technology companies should shape these systems to reduce harm and promote constructive action. At present, built-in chatbot responses to user expressions of distress often emphasize detection, refusal, or crisis referral, strategies that may protect developers but can fail to meet adolescents' immediate needs. At the same time, open-ended, pseudo-therapeutic interactions with untested agents can reinforce risk, dependency, and inaccurate or harmful beliefs about mental health and help-seeking. We propose that general-purpose conversational AI platforms are well-positioned to function as bridges to evidence-based support, rather than as replacements for formal therapy or treatment. Brief, bounded interventions, including digital single-session interventions, offer a promising model for responding to moments of need while preserving adolescent agency. Ethical AI design should prioritize safeguards, empirical testing, and pathways to evidence-based care.
Heterogeneous hydrogels capable of complex, programmable deformation are highly desirable for soft actuators, yet general strategies that simultaneously impart structural anisotropy, rapid responsiveness, and mechanical robustness remain limited. Here, a gradient anisotropic natural rubber-poly(N-isopropylacrylamide) (NR-PNIPAM) composite hydrogel is developed through a simple one-pot polymerization strategy by coupling pH-regulated colloidal stability with gravity-directed redistribution of natural rubber latex particles. Under an optimized pH window, NR nanoparticles gradually migrate during gelation and are fixed as a continuous gradient within the PNIPAM network, generating built-in structural asymmetry for nonuniform deformation. Meanwhile, NR nanoparticles act as soft reinforcing domains to improve mechanical strength, while water-soluble graphene nanosheets provide efficient photothermal conversion for remotely-controlled near-infrared (NIR)-responsive actuation. Benefiting from this synergistic design, the hydrogel exhibits programmable bending and localized folding with high actuation rates of 129° s-1 and 46° s-1, respectively, along with a tensile strength of 0.32 MPa and an active lifting capability exceeding 70 times its own weight. The material further enables biomimetic gripping and lifting under NIR stimulation. This work establishes a general route to robust gradient hydrogels by integrating colloidal regulation, structural anisotropy, and photothermal actuation, offering a versatile platform for high-performance soft intelligent systems.