This article recounts the many memorable moments the author experienced over more than two decades as a student and an assistant to his revered mentor—Academician TANG Au-chin, a towering figure in Chinese Chemistry Science, a distinguished educator, and an excellent leader in the advancement of scientific research and education. Through true personal recollections, the narrative offers a heartfelt portrayal of Professor Tang’s extraordinary legacy—as a titan of chemistry, a masterful cultivator of talent, and a founding architect of the National Natural Science Foundation of China. It also gives voice to the author’s deep and abiding remembrance, expressing a student’s profound gratitude and eternal reverence for a mentor, whose guidance, wisdom and virtue remain unforgettable. This tribute is dedicated with the utmost respect to commemorate the 110th anniversary of Professor Tang’s birth, and the everlasting spiritual legacy of a great teacher.

Prof. TANG Au-chin was an outstanding educator, scientist and leader in the field of education and science-technology in China. He created a miracle of taking the responsibility for education, scientific research and administration, which all reached a very high level in these three aspects. He devoted all his life to establishing the discipline of chemistry in Jilin University, and made it soar into the forefront of China and become world-famous. He also established the discipline of theoretical chemistry for New China and trained a large number of senior talents engaged in theoretical chemistry research and teaching, who were praised as “Chinese School” by international pees. Among Prof. Tang’s students, more than ten were elected academician of Chinese Academy of Sciences and Chinese Academy of Engineering. He together with his colleagues made many high-level scientific research achivements which made China’s theoretical chemistry at the forefront in the world and won the first award twice and the second award.

Machine learning (ML) has emerged to play a more and more important role in material science. Here, we develop a platform named Darwin4Matter that integrates machine learning and quantum chemistry for new materials discovery. The framework consists of six steps: quantum chemistry prediction, Δ-machine learning correction, molecular augmentation, machine learning prediction, molecular production, and verification. Using this platform, we start from a very small dataset and design three new functional molecules with high refractive indexes in the visible spectrum, which serves as the capping layer of organic light-emitting diode devices for improving light extraction efficiency. The superior performance over currently used materials has been verified experimentally, exhibiting significant commercial value in the field of advanced display materials.

A global potential energy surface (PES) for the H+HCN reaction has been constructed using the fundamental invariants neural networks (FI-NN) fitting method, based on 38662 ab initio energy points calculated at the UCCSD(T)-F12a/AVTZ theoretical level. The PES not only covers the well-studied abstract channel, but also accurately describes the stationary points and reaction pathways of the exchange channel, achieving an overall root-mean-square error (RMSE) of 2.85 meV. Total integral cross sections of both channels were computed, and the branching ratios of HCN and HNC product isomers were also analyzed, with HNC further distinguished by its formation through three different pathways.

Accurate computation of the Green’s function is crucial for connecting experimental observations to the underlying quantum states. A major challenge in evaluating the Green’s function in the time domain is the efficient simulation of quantum state evolution under a given Hamiltonian, a task that becomes exponentially complex for strongly correlated systems on classical computers. Quantum computing provides a promising pathway to overcome this challenge by enabling efficient simulation of the time evolution operator. However, for near-term quantum devices with limited coherence times and fidelity, the deep quantum circuits required to implement time-evolution operators present a significant challenge for practical applications. In this work, we introduce an efficient algorithm for computing Green’s functions via Cartan decomposition, which requires only fixed-depth quantum circuits for arbitrarily long time simulations. Additionally, analytical gradients are formulated to accelerate the Cartan decomposition by leveraging a unitary transformation in the factorized form. The new algorithm is applied to simulating longtime Green’s functions for the Fermi-Hubbard and transverse-field Ising models, extracting the spectral functions through Fourier transformation.

Organic electrochemical transistors (OECTs) garner significant attention in biosensing and neuromorphic computing applications owing to their high transconductance, low operating voltage, and biocompatibility. Among the various factors influencing OECTs performance and functionality, particularly synaptic behavior emulation, ion doping/dedoping kinetics play a pivotal role. However, precise control of ion dynamics remains challenging because of the complex interplay between material properties and microstructural characteristics. In this study, we demonstrate the modulation of the ion doping dynamics and synaptic behavior of OECTs based on hydrophilic-hydrophobic block copolymers (DPP-b-g2T-T) through thermal annealing. We investigate the correlations among segmental hydrophilicity/hydrophobicity, crystallinity, and ion transport kinetics. Our findings reveal that hydrophilic g2T-T segments enhance the ion doping efficiency, whereas hydrophobic DPP segments restrict ion transport. Thermal annealing reduces the ion doping/dedoping rates for both segments, particularly Au-gated OECTs. This phenomenon is attributed to the enhanced film crystallinity, which impedes ion transport, especially under the relatively weak gate control effect characteristic of Au. Leveraging the annealing-modulated ion doping/dedoping dynamics and prolonged retention time, we emulate short-term plasticity (STP) and long-term plasticity (LTP). This work establishes a strategy for optimizing OECTs synaptic performance through synergistic molecular design and thermal annealing, contributing to the advancement of neuromorphic technology.

Nanofabrication of tunable two-dimensional (2D) supramolecular architecture relies on the delicate balance between molecule-molecule and molecule-substrate interactions, where carefully designed molecules as building blocks are required. In this study, we introduced isopropylethynyl groups into two tripodal molecules, i.e., 1,3,5-tris-(isopropylethynyl)-benzene (iPr-TEB) and 2,4,6-tris-(isopropylethynyl)-1,3,5-triazine (iPr-TET), and investigated their self-assembly on Au(111) and Ag(111) surfaces under ultra-high-vacuum using scanning tunneling microscopy (STM). On Au(111) and Ag(111), iPr-TEB formed relatively comparable self-assembled nanopatterns through side-by-side dimers aggregation. These subtle differences in aggregation correlate with their negligible variations in adsorption conformation and energy. In contrast, iPr-TET exhibited pronounced substrate-dependent adsorption geometries due to stronger molecule-substrate interactions, resulting in disparate self-assembled nanoarchitectures on these two surfaces. Our results highlight the rotational flexibility of the isopropyl groups enabled by the single bond connecting them to the main acetylenic core, modulating intermolecular interactions and fine-tuning molecule-substrate interactions strength, hence providing a new strategy for crystal engineering in two dimensions.

The confined water in carbon nanotubes (CNTs) has an extremely fast transport rate, which has aroused great research interest. Previous studies have explored how various factors affect the structures and transport properties of confined water in CNTs. However, the fundamental understanding is still incomplete. Therefore, we studied confined water systems of different diameters and lengths in detail, and considered the effects of thermal fields and terahertz fields through molecular dynamics simulations. The simulation results revealed that the tube diameter mainly affected confined water structure; the tube length regulated the overall transport performance by adjusting the dipole flipping process and molecular translocation distance of confined water. Increasing the tube length led to a decrease in water flow, but enhanced the stability of transient unidirectional transport events. When the temperature rose, water molecules accelerated their movement, resulting in an increase in the system flow. Meanwhile, the increase in temperature caused the dipole flipping of confined water more frequently, thereby making the unidirectional transport unstable and affecting the system flux. Unlike the non-selectivity of the thermal effect, terahertz light could selectively enhance the transport of confined water. The research results are expected to promote a deeper understanding of confined water in CNTs.

Non-metal doping is an effective strategy to modulate the electronic structure of graphitic carbon nitride (g-C₃N₄) and optimize its photocatalytic activity. Based on first-principles density functional theory, this work calculated the formation energy, electronic properties, and optical performance of S-doped monolayer g-C₃N₄. The results demonstrate that S atoms preferentially occupy interstitial sites, as characterized by low formation energy and thermodynamic spontaneity, which leads to stabilization, followed by the edge N2-sites. After introducing S impurities via the N2 and interstitial doping sites, the band gap of g-C₃N₄ is narrowed from 2.63 eV (calculated by the HSE06 functional) to 2.35 eV (for N2-site doping) and 1.99 eV (for interstitial-site doping), respectively. Both C₃N₄-N2 and S-interstitial doping enhance the delocalization of the highest occupied molecular orbital and the lowest unoccupied molecular orbital. Specifically, interstitial S atoms act as “bridges” to connect adjacent structural units, significantly improving carrier mobility and facilitating the separation of photogenerated electron-hole pairs. Furthermore, S-interstitial doping reduces the work function of g-C₃N₄ from 4.16 eV to 3.64 eV, which strengthens visible light absorption. This work provides theoretical support for the design and preparation of non-metal-doped modified g-C₃N₄ photocatalysts.

The extensive use and excellent durability of polyethylene terephthalate (PET) have caused a surge in plastic waste. The discovery of the Ideonella sakaiensis PETase (IsPETase) has opened up a promising avenue for PET bio-recycling, as it can effectively depolymerize PET into valuable monomers. In this work, we employed the M06-2X/MM-MD method (MM-MD: molecular mechanics-molecular dynamics) to study the deacylation reaction of a PET heptamer and reveal the structural features that influence the free energy barrier. This reaction proceeds in a stepwise manner, and the first step is rate-limiting. The energy barrier is 20.4 kcal/mol (1 kcal=4.18 kJ), higher than those for other PET substrates with shorter chains, which supports our previous finding that the deacylation becomes difficult with increasing PET chain length. The hydrogen bonds in the oxyanion hole and between His208 and Asp177 play an important role in the reaction mechanism. In addition, PET self-interaction increases the free energy barrier compared with the short oligomers. This work reveals the catalytic mechanism of PETase in degrading long-chain PET, aiming to promote the engineering of a critical class of enzymes in plastic recycling.

Descriptors can accelerate the search for high-performance nonlinear optical (NLO) materials. Herein, we constructed 48 donor-acceptor (D-A) skeleton systems based on intramolecular B-locking strategies involving TPA-TCN, TPA-CN-N4, and TPA-CN-N4-2Py. The calculated results demonstrated that the constructed D-A molecules can serve as potential NLO material candidates, and torsion angles between donor and acceptor units are concentrated in narrow ranges is beneficial to obtain the large first hyperpolarizability (β). Intriguingly, the desirable R² reached to 0.95 for the TPA-TCN series, 0.96 for the TPA-CN-N4 series and 0.96 for the TPA-CN-N4-2Py series when torsion angles were served as features. Furthermore, when charge transfer and the excited energy of crucial states are used as features, several comprehensive descriptors have been also identified. And the favorable R² values of 0.95 for the TPA-TCN series, 0.97 for the TPA-CN-N4 series, and 0.93 for the TPA-CN-N4-2Py series were achieved between these parameters and lgβ values. These high R² values confirm that these parameters can be regarded as valuable features connecting them to β, aiding in the design of NLO materials with D-A architecture systems. Consequently, these findings provide novel insights into the relationships among torsion angles and β, which facilitates effort to identify high-performance NLO materials within D-A backbone systems tailored for specific application requirements.

Alloying-type anodes hold promise for sodium-ion batteries (SIBs) due to their high theoretical capacities, yet they suffer from severe capacity fading caused by large volume expansion during sodiation. Identifying a universal structural descriptor that links lattice chemistry with stress resistance and ion transport is therefore critical. Here, we introduce the crystal packing factor (PF) as a predictive metric and validate its effectiveness in layered bismuth compounds (BiOCl, Bi₂O₂S, and Bi₂O₂NCN) spanning a broad PF range. Bi₂O₂NCN, characterized by an atomically sparse and electronically conjugated [Bi₂O₂]²⁺-NCN²⁻ layered framework with the lowest PF (0.613), markedly outperforms Bi₂O₂S (0.694) and BiOCl (0.763). Its open framework provides abundant interlayer free volume and weak steric constraints, thereby buffering mechanical strain and accelerating Na⁺ diffusion. First-principles calculations corroborate that Bi₂O₂NCN shows suppressed stress accumulation and a lower migration barrier of 0.18 eV compared to Bi₂O₂S and BiOCl. Experimentally, Bi₂O₂NCN delivers a high capacity of 486 mA·h·g⁻¹ at 0.3 C (1 C=656 mA·g⁻¹) and maintains 230 mA·h·g⁻¹ after 6600 cycles at 15 C with 93% capacity retention. Multimodal structural characterizations further confirm a reversible conversion-alloying mechanism. These findings establish the crystal PF as a generalizable guideline for designing stress-resistant alloying-type anodes, offering a powerful pathway toward durable, high-performance SIBs.

We present a novel breathing orbital valence bond (BOVB) scheme, termed generalized BOVB (GBOVB), which constructs the wave function as a linear combination of valence bond self-consistent field (VBSCF) and its excited structures without requiring SCF orbital optimization. By applying different truncation levels to the excited configurations, multiple GBOVB variants are developed, offering flexible trade-offs between computational efficiency and accuracy. Benchmark tests reveal that GBOVB4 achieves the highest accuracy at a greater computational cost, while GBOVB4(D) provides the best balance between performance and efficiency. Notably, GBOVB overcomes convergence challenges of conventional BOVB methods when dealing with delocalized orbitals. Despite these advantages, limitations remain: excitations beyond double excitations may be important, and neglecting interactions between doubly excited structures in GBOVB4(D) can reduce accuracy, especially for systems with large active spaces.

The development of functionally accurate stimuli-responsive materials based on the principle of dissipative self-assembly (DSA) poses significant challenges within polymer chemistry, a field critical for elucidating the fundamental mechanisms underpinning the specific functions of living organisms. Based on the dissipative particle dynamics simulations, this study proposes a novel approach to driving the DSA process of polymer solutions through the application of periodic external fields, thereby modulating enthalpy changes. We aim to design stimuli-responsive materials capable of dynamically transitioning between non-equilibrium three-dimensional (3D) nanogels and steady-state spherical micelles or layered structures. Our findings indicate that the formation of the dissipative structure of the 3D gel is contingent upon the frequency of the external field exceeding a critical threshold, which instigates high-frequency oscillations of the conformational transitions of the polymer block copolymer. Concurrently, we observe that the power of the external field predominantly influences the formation rate of the dissipative structure; specifically, higher external field power correlates with accelerated formation kinetics. Moreover, the design principle outlined in this research is applicable to polymer concentrations ranging from 20% to 40%, effectively streamlining the experimental procedure by obviating the requirement for precise concentration control. This investigation offers valuable insights into the design of biomimetic stimui-responsive materials and contributes to a deeper understanding of the mechanisms, by which external fields facilitate DSA processes in polymer systems.

Accurate modeling of charge distribution plays a vital role in molecular simulations, electrostatic energy evaluation, and mechanistic analysis. The atom-bond electronegativity equalization method (ABEEM) provides a physically interpretable framework for computing atomic and electronic site charges by partitioning molecular space into atoms, bonds, and lone-pair regions. However, conventional ABEEM parameterization relies heavily on manual tuning, limiting its adaptability and predictive accuracy. In this work, an automated parameter learning strategy for ABEEM was proposed, guided by intelligent optimization algorithms and formulated within a goal programming framework. The framework systematically calibrates the key parameters of multiple types of charge sites. A chemically diverse training set including proteins, lipids, and nucleotides was constructed, and a dual-level objective function was designed to improve accuracy at both site and atomic levels. This approach significantly enhances the predictive performance and consistency of the ABEEM model across complex biomolecular systems. It also eliminates human bias and provides a scalable and generalizable pathway for force field development.

We introduce a quantum chemical topology (QCT) approach using the Kohn-Sham one-electron potential (KSpot) as a scalar function, revealing unique spatial features of atoms in molecules and the chemical bonds. The KSpot and its electron force lines demonstrate that an atom is a 3D basin governed by its nucleus as an attractor. Notably, KSpot atomic charges exhibit less basis set dependence, whose physical reliability is further confirmed through accurate reproduction of electrostatic potentials and dipole moments. To assess performance, we systematically compared KSpot QCT atomic charges with six established methods (QTAIM, Hirshfeld, Mülliken, NPA, CHELPG, and MK) across 20 amino acid dipeptides. KSpot charges have strong correlations with QTAIM and Hirshfeld ones, with correlation coefficients of 0.9207 and 0.9160, respectively. Furthermore, we successfully parameterized the atom bond electronegativity equalization method (ABEEM) using KSpot QCT charges, achieving a good linear agreement between them. These results establish KSpot QCT as a robust tool for molecular structure analysis, electrostatic interaction studies, and force field development.

This study systematically investigated the structure-property relationship of Cu(I) carbene-metal-amide (CMA) complexes using density functional theory (DFT) and time-dependent DFT (TD-DFT) calculations. Potential energy surface analysis revealed that planar geometry represents the most stable configuration for both ground and excited states. By modulating ligand structures, we not only elucidated the luminescence mechanism in solution phase but also demonstrated that the synergistic effect of small singlet-triplet energy gap (ΔE_ST) and low reorganization energy (λ) can facilitate rapid reverse intersystem crossing (RISC) despite weak spin-orbit coupling (SOC). Comparative studies between solution and solid phases showed that molecular packing in crystalline state effectively suppresses structural distortion, significantly enhancing radiative transition efficiency by reducing non-radiative decay. The planar geometry-enabled fast ISC/RISC cycling ensures efficient triplet exciton utilization, leading to high-performance thermally activated delayed fluorescence (TADF). Our work provides molecular-level insights into the TADF mechanism of Cu-CMA systems, particularly revealing a non-conventional exciton conversion mechanism governed by the “weak SOC-small ΔE_ST-low λ″ synergy, which offers new design principles for developing cost-effective, high-efficiency copper-based TADF materials.

Understanding the assembly pathways of heteromultimeric protein complexes is crucial for deciphering their functional mechanisms. However, traditional computational methods often struggle with the combinatorial NP-hard (non-deterministic polynomial-time hard) problem. This study employs a computational method, multilevel merge dock (MuMD), which uses a competition-based strategy to simulate the actual assembly process of heteromultimeric proteins. By avoiding the combinatorial NP-hard problem typically associated with the exhaustive conformation sampling, MuMD reduces the need to generate and evaluate a large number of conformations, thereby reducing computational costs while improving accuracy and efficiency. In the process, the ZDOCK scoring function is used to select optimal protein pair, followed by subsequent assembly using the roulette-wheel algorithm. Each component undergoes competition during the assembly process, with the subcomplex having the highest score taking priority in assembly steps. The results showed that MuMD can effectively simulate the dynamic assembly process of heteromultimeric protein complexes. Additionally, it accurately predicts the assembly pathway of the heteromultimeric protein complexes. Notably, MuMD method demonstrates significant advantages in accurately predicting the assembly pathways of complexes with fewer than 6 chains. This work provides an efficient and accurate tool for studying protein complex assembly.

Linear fused acenes exhibit excellent charge transport properties but suffer from poor solubility and stability. This study investigates “H-type” acene derivatives to address the poor solubility and stability of linear acenes by Marcus theory and kinetic Monte Carlo simulations. The findings reveal that “H-type” conjugation enhances photostability, oxidative stability, and solubility compared to linear acenes, with series a exhibiting superior solubility. The reorganization energy decreases with molecular extension, following trends similar to linear acenes. Key electronic structure differences were identified: thiophene substitution (series b) localizes the highest occupied molecular orbital (HOMO) on the bridging carbon atoms, while benzene substitution (series a) localizes it on the central acene core. Crucially, a strong correlation is established between the molecular structure parameter |ΔR| and crystal packing modes: |ΔR|<0.7 yields 1D/2D stacking; 0.7<|ΔR|<1.1 produces herringbone packing; and |ΔR|>1.1 leads to beneficial pitched π-stacking. Mobility calculations showed benzene-extended derivatives exhibit strong anisotropy. Thiophene expansion (b-3) reduces anisotropy, and benzothiophene (b-3*) improves the anisotropic mobility of molecules. This is attributed to favorable S…C interactions that suppress detrimental molecular slip. These results provide a critical theoretical framework for future development of “H-type” acene materials.

Multiple resonance thermally activated delayed fluorescence (MR-TADF) materials have attracted significant attention in organic electroluminescent devices due to their high exciton utilization efficiency and narrow emission spectra. However, their key performance parameters, singlet-triplet energy gap (ΔE_ST) and emission spectral full width at half maximum (FWHM), exhibit complex nonlinear relationships with molecular structures. To overcome the challenges of time-consuming, costly experiments and the limitations and insufficient accuracy of theoretical calculations, this study proposes a lightweight prediction framework based on SMILES encoding. By integrating Morgan fingerprints and physicochemical descriptors, end-to-end predictive models for ΔE_ST and FWHM were established. Skeleton similarity constraints were introduced to prevent data leakage, while feature selection and Bayesian optimization were applied to further enhancing model performance. Several machine learning algorithms were explored, among which the XGBoost model demonstrated the best predictive ability. Shapley additive explanations (SHAP) analysis revealed that ΔE_ST is mainly associated with electronic distribution, whereas FWHM is influenced by local skeleton structures and polar surface area. This approach achieves high-accuracy predictions without relying on three-dimensional structural information, providing an efficient solution for the rational design of MR-TADF materials.

A series of Eu³⁺ doped manganese phosphite (JIS-10:Eu³⁺) red phosphors was prepared via the hydrothermal method. The regulation of Eu³⁺ doping concentration on the material structure, luminescence properties, and antibiotic fluorescence sensing characteristics was systematically investigated. Experiments showed that when the Eu³⁺ doping concentration (molar fraction) was 0.01, the material exhibited the strongest red emission at 617 nm. Under near-ultraviolet excitation at 394 nm, the luminescence intensity of JIS-10:0.01Eu³⁺ showed a linear relationship with the Eu³⁺ concentration. When this material was used as a fluorescent probe for tetracycline detection, its fluorescence intensity exhibited a good linear response to tetracycline concentration in the range of 0.1–200 µmol/L, and it showed significant selectivity towards structural analogs, such as sulfadiazine and norfloxacin.

This study theoretically investigated the regulation mechanism of catalytic activity in the copolymerization of propylene oxide (PO) and carbon dioxide (CO₂) catalyzed by a well-designed bis-boron phosphonium salt (PBB) catalyst through density functional theory (DFT) calculations. Mechanistic analysis suggests that the alternating copolymerization of PO and CO₂ is thermodynamically and kinetically favorable. The results revealed that initiation efficiency is dependent on the counter anions of phosphonium salt, where Br. exhibited a higher ring-opening reactivity of PO than Cl.. The electronic effect of the catalyst for the initiation step is investigated by altering substituent groups in PBB catalysts and results suggest that the catalytic activity is improved by the substitution of electron-donating groups. On the other hand, the catalytic activity of such PBB catalysts is also significantly dependent on the number of methylene groups (chain length) between the phosphorus center and boron atom, suggesting a synergism and confinement characteristic of PBB catalysts. Interestingly, computational results clearly show that secondary (electrostatic) interaction induced by the tetra-coordinated boronate complex stabilizes the transition states of ring-opening of PO and accelerates the polymerization. This work provides theoretical insights for designing efficient supramolecular catalysts and optimizing CO₂ utilization strategies.

The aluminum-water reaction for hydrogen production exhibits significant potential in the energy sector due to its low cost and high hydrogen density. However, the great challenge of controlling the aluminum-water reaction’s kinetics hinders its application advancement. This study proposes polymer-encapsulated aluminum nanoparticles (Al NPs) and NaOH nanocomposite coating for on-demand hydrogen production via aluminum-water reaction. By monitoring the coating reaction process using scanning electron microscopy and X-ray diffraction, the results indicate that fibrous AlO(OH) gradually forms on the Al NP surface during the reaction, eventually transforming entirely into bulk Al(OH)₃. Furthermore, by adjusting the size of Al NPs (50–1000 nm), the mass fraction of Al NPs, and the hydrophilicity/hydrophobicity of the polymer matrix, the hydrogen production rate can be regulated within the range of 0.08–9.65 mL·s⁻¹·g⁻¹. Finally, multiple interval reactions were adopted to verify the effectiveness of water-controlled on-demand hydrogen production. This strategy overcomes the dependence of the aluminum-water reaction on extensive alkali solution, providing new insights for the development of hydrogen production.

Graphene oxide (GO)-based membranes have garnered significant attention in water purification and dye separation due to their electrostatic interactions arising from abundant functional groups and exceptional molecular sieving capabilities. However, challenges such as the inability to produce uniform GO membranes at an industrial scale and poor stability in an aqueous environment hinder their widespread application in industry. In this work, a scalable and easily adjustable technique is proposed to GO membrane with long-term stability in an aqueous environment by multilayer integration rod-coating with highpower UV reduction. A piece of multilayer graphene oxide membrane (MGM) with a size of 30 cm×30 cm with uniformity and efficiency can be easily formed. MGM demonstrates remarkable performance in dye separation, achieving a stable flux of 10.76 LMH·bar⁻¹ [LMH: L/(m²·h); 1 bar=10⁵ Pa] over more than 300 h of testing, along with a dye separation efficiency exceeding 95.0%. Moreover, the separation performance as well as the pore parameter can be flexibly modulated by changing the rod-coating times to adapt to the dye molecules under different conditions. The excellent performance of MGM paves the way for their largescale industrial production in dye separation applications.

Design and synthesis of highly active and durable bifunctional electrocatalysts is crucial toward oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) in unitized regenerative proton exchange membrane fuel cells (UR-PEMFCs). Herein, we report a simple phase-transfer reduction method to synthesize PtIr nanoparticles with different molar ratios. When the Pt/Ir molar ratio is 2.2:1, the bifunctional oxygen activity is optimal. The ORR mass activity of Pt_2.2Ir nanoparticles is 190.3 mA/mg_Pt @ 0.9 V (vs. RHE), which is 1.8 times and 3.7 times those of commercial Pt black and physically mixed commercial Pt and Ir black (Pt+Ir black), respectively. At the potential of 1.53 V vs. RHE, the OER mass activity of Pt_2.2Ir nanoparticles is 202.7 mA/mg_Ir, which is 2.0 times and 1.3 times those of Ir black and Pt+Ir black, respectively. An overpotential gap of Pt_2.2Ir nanoparticles (618 mV) between the half-wave potential of ORR and the potential at 10 mA/cm² of OER is superior to Pt+Ir black (662 mV). After durability tests, the ORR/OER activity of Pt_2.2Ir nanoparticles remained much better than Pt+Ir black. X-Ray photoelectron spectroscopy suggests that the electronic interaction between Pt and Ir accounts for enhanced bifunctional oxygen activity. Eventually, the Pt_2.2Ir nanoparticles were evaluated in UR-PEMFCs.

Marine microplastics have emerged as critical pollutants, attracting significant attention in marine scientific research. However, the complex and high-salinity seawater matrix poses substantial challenges for their separation and detection. This study presents the membrane filtration-pyrolysis-mass spectrometry (MF-Eh-Pyr-MS) method, which integrates membrane filtration (MF) with electromagnetic heating pyrolysis-mass spectrometry (Eh-Pyr-MS) to enrich and detect micro- and nano-plastics in real seawater environments. Using polypropylene (PP), polyethylene (PE), and polystyrene (PS) as target microplastics, the study systematically explores the effects of salt solute types, salt concentrations, and microplastic properties (such as type and particle size) on the applicability of the proposed method. Scanning electron microscopy (SEM) was utilized to characterize the filter membranes before and after pyrolysis and following filtration of different solutes, further validating the method’s feasibility. The results indicate that, except for a 3.5% (mass fraction) magnesium chloride solution, other salt solutes and salinity levels have minimal impact on analysis outcomes. Real seawater samples collected near Weihai were used for practical validation, with recovery rates for plastics ranging from 32.8% to 104.8% across three sampling points. This work provides a straightforward and effective approach for the separation and detection of nanoplastics in seawater, offering valuable insights into marine micro- and nano-plastic research.

Quantum dot (QD) lasers exhibit exceptional promise in optical communication, laser display, and biomedical diagnostic applications. However, the toxicity of heavy metals in II–VI QDs has impeded their broader deployment. Herein, red-emitting InP QDs bearing a gradient ZnSeS alloy shell were synthesized to suppress Auger recombination and thereby attain a lasing threshold as low as 32.1 µJ/cm². A capillary-bridge confined self-assembly method was employed to arrange these InP QDs into high-density micro-ring resonator arrays with resolutions exceeding 1000 ppi (ppi: proton pump inhibitor). Systematic variation of the microring diameter enabled modulation of the lasing mode structure, and single-mode lasing was realized in 6 µm rings under defined pumping conditions. This work establishes a heavy-metal-free platform for scalable, high-resolution quantum dot laser arrays.