Printing 3D Metallic Structures in Porous Matrix.

The fabrication of metallic micro/nanostructures has great potential for advancing optoelectronic microdevices. Over the past decade, femtosecond laser direct writing (FsLDW) technology has played a crucial role in driving progress in this field. In this study, silica gel glass is used as a supporting medium, and FsLDW is employed to reduce gold and palladium ions using 7-Diethylamino-3-thenoylcoumarin (DETC) as a two-photon sensitizer, enabling the printing of conductive multilayered and 3D metallic structures. How the pore size of the silica gel glass affects the electrical conductivity of printed metal wires is systematically examined. This 3D printing method is versatile and offers expanded opportunities for applying metallic micro/nanostructures in optoelectronic devices.

Hexa-Branched Nanographenes with Large Two-Photon Absorption.

The conventional approach toward molecules with large two-photon absorption (TPA) involves donor-acceptor conjugation. Herein we show a new strategy involving the use of hexa-branched nanographenes. We synthesized two hexa-branched nanographenes, one with six benzoaceanthrylene arms fused to the coronene core and the other with six pyrenyl arms fused to the coronene core. Neither of these hexa-branched nanographenes has a donor-acceptor structure, yet they exhibited high TPA values of 3.6 × 103 and 1.9 × 104 GM, respectively, which are the highest values recorded for heteroatom-free hydrocarbon molecules. Theoretical analysis suggests that the fused branched structures are responsible for the large TPA cross-section. These findings illustrate the importance of the topology of the fused conjugated skeleton in TPA and provide an alternative structural design toward large TPA.

Exploration and Insights on Topology Adjustment of Giant Heterometallic Cages Featuring Inorganic Skeletons Assisted by Machine Learning.

Inorganic molecular cages are emerging multifunctional molecular-based platforms with the unique merits of rigid skeletons and inherited properties from constituent metal ions. However, the sensitive coordination bonds and vast synthetic space have limited their systematic exploration. Herein, two giant cage-like clusters featuring the organic ligand-directed inorganic skeletons of Ni4[La74Ni104(IDA)96(OH)184(C2O4)12(H2O)76]·(NO3)38·(H2O)120 (La74Ni104, 5 × 5 × 3 - C2O4) and [La84Ni132(IDA)108(OH)168(C2O4)24(NO3)12(H2O)116]·(NO3)72·(H2O)296 (La84Ni132, 5 × 5 × 5 - C2O4) were discovered by a high-throughput synthetic search. With the assistance of machine learning analysis of the experimental data, phase diagrams of the two clusters in a four-parameter synthetic space were depicted. The effect of alkali, oxalate, and other parameters on the formation of clusters and the mechanism regulating the size of two n × m × l clusters were elucidated. This work uses high-throughput synthesis and machine learning methods to improve the efficiency of 3d-4f cluster discovery and finds the highest-nuclearity 3d-4f cluster to date by regulating the size of the n × m × l inorganic cages through oxalate ions, which pushes the synthetic methodology study on elusive inorganic giant cages in a significantly systematic way.

Why Ligand Doping Increases the Fluorescence of an Anthracene-Based Metal-Organic Framework.

Metal-organic frameworks (MOFs) built from fluorescent ligands frequently exhibit enhanced fluorescence when doped with inert ligands. This study focuses on a MOF of the UiO-68 structure, which is built from a fluorescent dibenzoate-anthracene ligand doped with a dibenzoate-benzene ligand. Our investigation aims to understand the mechanism behind the doping-enhanced emission of this MOF. We rule out several possible mechanisms, including exciton coupling, electron transfer between ligand and metal center, and ligand intersystem crossing induced by the metal center. Inhibition of the interligand charge transfer is considered a possible way to enhance emission. Furthermore, we propose that the conformational change of the anthracene-based ligand in the MOF cavity is also a way for enhancement. Our molecular dynamics simulations of the MOF structure filled with solvents reveal that the steric crowding in the cavity induces a conformational change at different doping levels, affecting the rate of intersystem crossing of the ligand.

Photo-Driven Aerobic Methane Nitration.

Conversion of methane to liquid oxygenates is challenging but of great value. Here, we report the oxidation of methane (CH4) to methanol (CH3OH) assisted by nitrogen dioxide (NO2) as a photo-mediator and using molecular oxygen (O2) as the terminal oxidant. Similar photoreactions are widely studied in atmospheric chemistry but were not previously used in preparative methane conversion. We used visible light to excite NO2 generated by heating aluminum nitrate Al(NO3)3 and drove its reaction with methane and O2 to produce methyl nitrate (CH3ONO2), which is then hydrolyzed to CH3OH. Nitric acid (HNO3) and nitrate (NO3-) were produced and recycled back to Al(NO3)3, completing a chemical loop. HCl can catalyze this photochemical process via relay hydrogen atom-transfer reactions, with up to 17% methane conversion and 78% CH3ONO2 selectivity. This simple photochemical system provides new opportunities in selective methane transformation.

All-printed multiplexed electrocatalytic biosensors with rationally designed nanoparticle inks.

Inkjet printing, capable of rapid and template-free fabrication with high resolution and low material waste, is a promising method to construct electrochemical biosensor devices. However, the construction of fully inkjet-printed electrochemical biosensor remains a challenge owing to the lack of appropriate inks, especially the sensing inks of bioactive materials. Herein, we demonstrate a fully inkjet-printed, integrated and multiplexed electrochemical biosensor by combining rationally designed nanoparticle Inks. The stable gold (Au) nanoparticles ink with lower sintering temperature is prepared by using L-cysteine as stabilizer, and it is used to print the interconnects, the counter electrodes, and the working electrodes. The SU-8 ink is used to serve as dielectric layer for the biosensor, whereas the silver electrode is printed on the Au electrode by using commercially silver nanoparticles ink before it is chlorinated to prepare Ag/AgCl reference electrode. Moreover, we synthesize an inkjet-printable and electroactive ink, by the 'one-pot method', which is composed of conductive poly 6-aminoindole (PIn-6-NH2) and gold-palladium (Au-Pd) alloy nanoparticle (Au-Pd@PIn-6-NH2) to enhance the sensing performance of gold electrode towards hydrogen peroxide (H2O2). Especially, the amino groups in PIn-6-NH2can be further used to immobilizing glucose oxidase (GOx) and lactic acid oxidase (LOx) by glutaraldehyde to prepare printable sensing ink for the detection of glucose and lactate. The fully inkjet-printed electrochemical biosensor enabled by advanced inks can simultaneously detect glucose and lactate with good sensitivity and selectivity, as well as facile and scalable fabrication, showing great promise for metabolic monitoring.

Research on the Forward Solving Method of Defect Leakage Signal Based on the Non-Uniform Magnetic Charge Model.

Pipeline magnetic flux leakage inspection is widely used in the evaluation of material defect detection due to its advantages of having no coupling agent and easy implementation. The quantification of defect size is an important part of magnetic flux leakage testing. Defects of different geometrical dimensions produce signal waveforms with different characteristics after excitation. The key to achieving defect quantification is an accurate description of the relationship between the magnetic leakage signal and the size. In this paper, a calculation model for solving the defect leakage field based on the non-uniform magnetic charge distribution of magnetic dipoles is developed. Based on the traditional uniformly distributed magnetic charge model, the magnetic charge density distribution model is improved. Considering the variation of magnetic charge density with different depth positions, the triaxial signal characteristics of the defect are obtained by vector synthesis calculation. Simultaneous design of excitation pulling experiment. The leakage field distribution of rectangular defects with different geometries is analyzed. The experimental results show that the change in defect size will have an impact on the area of the defect leakage field distribution, and the larger the length and wider the width of the defect, the more sensitive the impact on the leakage field distribution. The solution model is consistent with the experimentally obtained leakage signal distribution law, and the model is a practical guide by which to improve the quality of defect evaluation.

Two-Photon 3D Printing in Metal-Organic Framework Single Crystals.

Two-photon polymerization (TPP) is a micro/nano-fabrication technology for additive manufacturing, enabling 3D printing of polymeric materials using ultrafast laser pulses. In this work, two-photon polymerization is realized inside a metal-organic framework (MOF) crystal. Intricate structures are built in the porous crystal to create a microstructure-in-crystal hybrid. Furthermore, the MOF can be removed by acid treatment to release the printed structure. The two-photon polymerization inside the crystal has the potential for MOF sensing device fabrication and data storage applications. In the future development, printing different materials in the same MOF crystal for creating functional 3D devices is hoped.

Prognostic value of the combination of primary tumor location and RAS mutational status on patients with colorectal liver metastasis undergoing hepatectomy.

OBJECTIVE: To assess prognostic influences of RAS mutational status and primary tumor site on cases with colorectal liver metastasis (CRLM) who underwent hepatectomy. METHODS: Clinicopathological data of 762 patients with CRLM who underwent hepatectomy between January 2000 and November 2018 were retrospectively analyzed. The left-sided tumors (LST) included tumors located in the splenic flexure, descending colon, sigmoid colon, and rectum; while right-sided tumors (RST) included those located in the cecum, ascending colon, and transverse colon. RAS mutational status was determined using Sanger sequencing or next-generation sequencing, including KRAS (Codons 12, 13, and 61) and NRAS (Codons 12, 13, and 61), which were defined as wild-type (RASwt) and mutant-type (RASmut), respectively. Survival curves were plotted using the Kaplan-Meier plotter and compared by the log rank test. The clinicopathological data were analyzed using univariate and multivariate analyses. RESULTS: The 5-year overall survival (OS) in the LST group was longer than that in the RST group (OS: 47.1% vs. 31.0%, p = 0.000, respectively), and the OS in the RASwt group was longer compared with that in the RASmut group (OS: 53.6% vs. 24.0%, p = 0.000). Besides, overall survival of the patients after hepatectomy was alternative, which was stratified by primary tumor site, with the 1-, 3-, and 5-year survival rates of 93.1%, 62.1%, and 47.1% for patients with LST, and 91.1%, 42.8%, and 31.0% for patients with RST, respectively. OS and disease-free survival (DFS) were significantly different stratified by RAS mutational status, with the 1-, 3-, and 5-year rates of 96.9%, 67.9%, and 53.6% for patients with RASwt tumors, and 85.7%, 41.5%, and 24.0% for patients with RASmut tumors, respectively. The 1-, 3-, and 5-year DFS rates were 51.9%, 30.0%, and 26.7% for patients with RASwt tumors, and 35.8%, 18.2%, and 14.9% for patients with RASmut tumors, respectively. The results of multivariate analysis showed that RAS mutational status and primary tumor site were both independent influencing factors of OS. CONCLUSION: RAS mutational status and primary tumor site affect OS independently in CRLM patients undergoing hepatectomy. The worse prognosis of RST cannot be simply attributed to the imbalance of RAS mutational status in different primary tumor sites.

Research on the Analytical Model of Improved Magnetic Flux Leakage Signal for the Local Stress Concentration Zone of Pipelines.

Local stress concentrations pose a significant hazard to the safe operation of pipelines. However, the classical analytical model of the magnetic flux leakage (MFL) signal is still unable to effectively quantitatively analyze and accurately evaluate the local stress concentration zone of a pipeline. In this paper, based on the Jiles-Atherton model of the magnetomechanical effect, the mathematical relationship between stress and the magnetization of ferromagnetic material under hysteresis conditions is introduced, and an improved analytical model of the MFL signal based on the magnetomechanical model is established. The influence law of stress intensity on the MFL signal in the local stress concentration zone of the pipeline is calculated and analyzed, and the theoretical calculation results are verified through experiments. Simulation and experimental results show that, considering the hysteresis condition, the stress causes a change in the hysteresis loop of the ferromagnetic material, and the magnetization strength of the material decreases with increasing stress; the effect of stress on the magnetization strength of ferromagnetic materials is most obvious when the external magnetic field is approximately 5 KA/m. The MFL signal on the surface of the local stress concentration zone of the pipe changes abruptly, and the amount of change in the axial amplitude and radial peak-to-peak value of the leakage signal of the pipe tends to increase with the increase in the stress intensity of the local stress concentration zone. A comparison of the analysis with the classical analytical model of the MFL signal shows that the improved analytical model of the MFL signal is more suitable for the quantification study of the local stress concentration zone of the pipeline.

Machine-Learning-Guided Discovery and Optimization of Additives in Preparing Cu Catalysts for CO2 Reduction.

Discovery and optimization of new catalysts can be potentially accelerated by efficient data analysis using machine-learning (ML). In this paper, we record the process of searching for additives in the electrochemical deposition of Cu catalysts for CO2 reduction (CO2RR) using ML, which includes three iterative cycles: "experimental test; ML analysis; prediction and redesign". Cu catalysts are known for CO2RR to obtain a range of products including C1 (CO, HCOOH, CH4, CH3OH) and C2+ (C2H4, C2H6, C2H5OH, C3H7OH). Subtle changes in morphology and surface structure of the catalysts caused by additives in catalyst preparation can lead to dramatic shifts in CO2RR selectivity. After several ML cycles, we obtained catalysts selective for CO, HCOOH, and C2+ products. This catalyst discovery process highlights the potential of ML to accelerate material development by efficiently extracting information from a limited number of experimental data.

Nanoscale Metal-Organic Frameworks and Metal-Organic Layers with Two-Photon-Excited Fluorescence.

Metal-organic frameworks (MOFs) based on 9,10-diphenylanthracene-derived ligands had been reported to exhibit upconverted fluorescence through triplet-triplet annihilation. We found that zirconium MOFs based on 9,10-diphenylanthracene can also give upconverted fluorescence via two-photon absorption without adding a triplet photosensitizer when a femtosecond pulsed laser is used as the excitation source. By tuning the synthetic condition, we obtained nanoscale MOFs of UiO structure in both octahedral and hexagonal nanoplate shapes, as well as a hexagonal nanoplate of MOFs of hcp-UiO structure and two-dimensional metal-organic layers. All of them, as well as a homogeneous solution of the 9,10-diphenylanthracene ligand, exhibit upconverted fluorescence upon excitation using a laser pulse of 60 fs with a pulse energy of ∼1.1 × 106 nJ/cm2 (unfocused). Moreover, we observed different emission spectra by two-photon excitation compared to those by one-photon excitation, which indicates access to a unique initial excited state via two-photon excitation. This phenomenon is not observed for a homogeneous solution of the ligand. These nanoscale MOFs may find application in two-photon fluorescence imaging.

A facile synthesis of high quality nanostructured CeO2 and Gd2O3-doped CeO2 solid electrolytes for improved electrochemical performance.

This study describes the use of a composite nitrate salt solution as a precursor to synthesize CeO2 and Gd2O3-doped CeO2 (GDC) nanoparticles (NPs) using an atmospheric pressure plasma jet (APPJ). The microstructures of CeO2 and GDC NPs were found to be cubical and spherical shaped nanocrystallites with average particle sizes of 10.5 and 6.7 nm, respectively. Reactive oxygen species, detected by optical emission spectroscopy (OES), are believed to be the major oxidative agents for the formation of oxide materials in the APPJ process. Based on the material characterization and OES observations, the study effectively demonstrated the feasibility of preparing well-crystallized GDC NPs by the APPJ system as well as the gas-to-particle mechanism. Notably, the Bader charge of CeO2 and Ce0.9Gd0.1O2 characterized by density function theory (DFT) simulation and AC impedance measurements shows that Gd helps in increasing the charge on Ce0.9Gd0.1O2 NPs, thus improving their conductivity and making them candidate materials for electrolytes in solid oxide fuel cells.

Automation and machine learning augmented by large language models in a catalysis study.

Recent advancements in artificial intelligence and automation are transforming catalyst discovery and design from traditional trial-and-error manual mode into intelligent, high-throughput digital methodologies. This transformation is driven by four key components, including high-throughput information extraction, automated robotic experimentation, real-time feedback for iterative optimization, and interpretable machine learning for generating new knowledge. These innovations have given rise to the development of self-driving labs and significantly accelerated materials research. Over the past two years, the emergence of large language models (LLMs) has added a new dimension to this field, providing unprecedented flexibility in information integration, decision-making, and interacting with human researchers. This review explores how LLMs are reshaping catalyst design, heralding a revolutionary change in the fields.

Magnetic leakage behavior assessment in pipeline stress-concentrated areas using improved force-magnetic coupling model.

Magnetic flux leakage (MFL) technology is remarkable for its capability to detect pipeline geometric deformation and general corrosion defects. However, it cannot characterize the MFL behavior in stress-concentrated areas, thereby greatly challenging the subsequent pipeline maintenance. This study suggests that the MFL characteristics of pipeline in stress-concentrated areas are caused by the combined effect of the face magnetic charge on the deformed end-face and the body magnetic charge of the dislocation stack. In addition, an improved force-magnetic coupling model of the pipeline in stress-concentrated areas is established based on the magnetic dipole model and Jiles-Atherton (J-A) theory. In the verification experiment, the Q235 steel plate is magnetized along the extension direction (axis of the pipeline) through the solenoid coil to obtain the distribution law of the MFL signal in the stress-concentrated area under different excitation intensities. The results show that with the increase in excitation intensity, the deformation of the MFL field signal caused by the end-face of the stress-concentrated area gradually increases to a stable state. Moreover, the internal stress of the MFL field signal generated by the pipe dislocation rapidly increases to a peak value and then decays exponentially to a certain base value. The overall change trend is in good agreement with the calculation results of the established force-magnetic coupling model. Meanwhile, the differentiation research between deformation and internal stress MFL field signals under different magnetic field intensities can provide a reliable theoretical basis for the subsequent accurate identification and quantification of pipeline stress-concentrated areas.

MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows.

Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command-line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing service at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pretrained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries.

Benzophenone-containing phosphors with an unprecedented long lifetime of 1.8 s under ambient conditions.

It is well-known that benzophenone has a short phosphorescence lifetime of around 1 ms even at 77 K. Here we report a benzophenone-containing emitter with an unprecedented long phosphorescence lifetime of 1.8 s under ambient conditions, which can be attributed to its T1 state of localized excitation nature as revealed by detailed studies.

Interpretable Machine Learning of Two-Photon Absorption.

Molecules with strong two-photon absorption (TPA) are important in many advanced applications such as upconverted laser and photodynamic therapy, but their design is hampered by the high cost of experimental screening and accurate quantum chemical (QC) calculations. Here a systematic study is performed by collecting an experimental TPA database with ≈900 molecules, analyzing with interpretable machine learning (ML) the key molecular features explaining TPA magnitudes, and building a fast ML model for predictions. The ML model has prediction errors of similar magnitude compared to experimental and affordable QC methods errors and has the potential for high-throughput screening as additionally validated with the new experimental measurements. ML feature analysis is generally consistent with common beliefs which is quantified and rectified. The most important feature is conjugation length followed by features reflecting the effects of donor and acceptor substitution and coplanarity.

PEDOT:PSS hydrogels with high conductivity and biocompatibility for in situ cell sensing.

Conducting polymer hydrogels, especially poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) hydrogels, show great promise in soft bioelectronics due to their high conductivity and the ability of electrical coupling with tissues for sensing and stimulation. However, it is challenging to solve the problem of poor biocompatibility of PEDOT:PSS hydrogels due to the existing harsh preparation methods with the use of toxic and harmful reagents. Herein, we report the synthesis of PEDOT:PSS hydrogels with positively charged conductive polymers as a cross-linker and the application of PEDOT:PSS hydrogels as in situ electrochemical sensors for living cells. The conductivities of PEDOT:PSS hydrogels prepared using this method without any toxic or harmful reagents can reach up to 3265 S m-1. The facile synthesis approach with a simple mixture of PEDOT:PSS aqueous solution and the monomers of conductive polymers at room temperature also enables the printability of PEDOT:PSS hydrogels to fabricate patterned electrodes. Moreover, all the proposed PEDOT:PSS hydrogels demonstrated good biocompatibility. The in situ electrochemical detection of dopamine secreted from PC12 cells cultured within PEDOT:PSS hydrogels suggests that our PEDOT:PSS hydrogels with high conductivity and biocompatibility offer great potential for the integration of biosensors within 3D cell culture.

Five-year survival post hepatectomy for colorectal liver metastases in a real-world Chinese cohort: Recurrence patterns and prediction for potential cure.

BACKGROUND: Patients with a 5-year recurrence-free survival post liver resection for colorectal cancer liver metastases (CRLM) are considered to be potentially cured. However, there is a deficit of data on long-term follow-up and the recurrence status among these patients in the Chinese population. We analyzed real-world follow-up data of patients with CRLM who underwent hepatectomy, explored the recurrence patterns, and established a prediction model for a potential cure scenario. METHODS: Patients who underwent radical hepatic resection for CRLM during 2000-2016, with actual follow-up data for at least 5 years, were enrolled. The observed survival rate was calculated and compared among the groups with different recurrence patterns. The predictive factors for 5-year non-recurrence were determined using logistic regression analysis; a recurrence-free survival model was developed to predict long-term survival. RESULTS: A total of 433 patients were included, of whom 113 patients were found non-recurrence after 5 years follow-up, with a potential cure rate of 26.1%. Patients with late recurrence (>5 months) and lung relapse showed significantly superior survival. Repeated localized treatment significantly improved the long-term survival of patients with intrahepatic or extrahepatic recurrences. Multivariate analysis showed that RAS wild-type CRC, preoperative CEA <10 ng/ml, and liver metastases ≤3 were independent factors for a 5-year disease-free recurrence. A cure model was developed based on the above factors, achieving good performance in predicting long-term survival. CONCLUSIONS: About one quarter patients with CRLM could achieve potential cure with non-recurrence at 5-year after surgery. The recurrence-free cure model could well distinguish the long-term survival, which would aid clinicians in determining the treatment strategy.