Cetuximab plus FOLFOXIRI versus cetuximab plus FOLFOX as conversion regimen in RAS/BRAF wild-type patients with initially unresectable colorectal liver metastases (TRICE trial): A randomized controlled trial.

BACKGROUND: It remains unclear whether intensification of the chemotherapy backbone in tandem with an anti-EGFR can confer superior clinical outcomes in a cohort of RAS/BRAF wild-type colorectal cancer (CRC) patients with initially unresectable colorectal liver metastases (CRLM). To that end, we sought to comparatively evaluate the efficacy and safety of cetuximab plus FOLFOXIRI (triplet arm) versus cetuximab plus FOLFOX (doublet arm) as a conversion regimen (i.e., unresectable to resectable) in CRC patients with unresectable CRLM. METHODS AND FINDINGS: This open-label, randomized clinical trial was conducted from April 2018 to December 2022 in 7 medical centers across China, enrolling 146 RAS/BRAF wild-type CRC patients with initially unresectable CRLM. A stratified blocked randomization method was utilized to assign patients (1:1) to either the cetuximab plus FOLFOXIRI (n = 72) or cetuximab plus FOLFOX (n = 74) treatment arms. Stratification factors were tumor location (left versus right) and resectability (technically unresectable versus ≥5 metastases). The primary outcome was the objective response rate (ORR). Secondary outcomes included the median depth of tumor response (DpR), early tumor shrinkage (ETS), R0 resection rate, progression-free survival (PFS), overall survival (not mature at the time of analysis), and safety profile. Radiological tumor evaluations were conducted by radiologists blinded to the group allocation. Primary efficacy analyses were conducted based on the intention-to-treat population, while safety analyses were performed on patients who received at least 1 line of chemotherapy. A total of 14 patients (9.6%) were lost to follow-up (9 in the doublet arm and 5 in the triplet arm). The ORR was comparable following adjustment for stratification factors, with 84.7% versus 79.7% in the triplet and doublet arms, respectively (odds ratio [OR] 0.70; 95% confidence intervals [CI] [0.30, 1.67], Chi-square p = 0.42). Moreover, the ETS rate showed no significant difference between the triplet and doublet arms (80.6% (58/72) versus 77.0% (57/74), OR 0.82, 95% CI [0.37, 1.83], Chi-square p = 0.63). Although median DpR was higher in the triplet therapy group (59.6%, interquartile range [IQR], [50.0, 69.7] versus 55.0%, IQR [42.8, 63.8], Mann-Whitney p = 0.039), the R0/R1 resection rate with or without radiofrequency ablation/stereotactic body radiation therapy was comparable with 54.2% (39/72) of patients in the triplet arm versus 52.7% (39/74) in the doublet arm. At a median follow-up of 26.2 months (IQR [12.8, 40.5]), the median PFS was 11.8 months in the triplet arm versus 13.4 months in the doublet arm (hazard ratio [HR] 0.74, 95% CI [0.50, 1.11], Log-rank p = 0.14). Grade ≥ 3 events were reported in 47.2% (35/74) of patients in the doublet arm and 55.9% (38/68) of patients in the triplet arm. The triplet arm was associated with a higher incidence of grade ≥ 3 neutropenia (44.1% versus 27.0%, p = 0.03) and diarrhea (5.9% versus 0%, p = 0.03). The primary limitations of the study encompass the inherent bias in subjective surgical decisions regarding resection feasibility, as well as the lack of a centralized assessment for ORR and resection. CONCLUSIONS: The combination of cetuximab with FOLFOXIRI did not significantly improve ORR compared to cetuximab plus FOLFOX. Despite achieving an enhanced DpR, this improvement did not translate into improved R0 resection rates or PFS. Moreover, the triplet arm was associated with an increase in treatment-related toxicity. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03493048.

Asymmetric Potential Model of Two-Dimensional Imine Covalent Organic Frameworks with Enhanced Quantum Efficiency for Photocatalytic Water Splitting.

Two-dimensional (2D) covalent organic frameworks (COFs) assembled using building blocks have been widely employed in photocatalysis due to their customizable optoelectronic characteristics and porous structure, which facilitate charge carrier and mass movement. Nevertheless, the development of COF photocatalysts encounters a continuous obstacle in enhancing the efficiency of photocatalysis, impeded by a limited comprehension of the orbital interaction between molecular fragments and linkers. In this study, we present a model that examines the interaction between molecular fragments in an imine-based COF at the frontier molecular orbital level, enabling us to comprehend the impact of manipulating linkers on light adsorption, exciton efficiency, and catalytic activity. Our findings demonstrate that altering the connecting orientation of 14 R-C=N-R imine linkers in 2D COFs can enhance solar-to-hydrogen (STH) efficiency under visible light from 2.76% to 4.24%. This research has the potential to provide a valuable model for refining photocatalysts with enhanced photocatalytic performance.

A Volcano Correlation between Catalytic Activity for Sulfur Reduction Reaction and Fe Atom Count in Metal Center.

Sulfur reduction reaction (SRR) facilitates up to 16 electrons, which endows lithium-sulfur (Li-S) batteries with a high energy density that is twice that of typical Li-ion batteries. However, its sluggish reaction kinetics render batteries with only a low capacity and cycling life, thus remaining the main challenge to practical Li-S batteries, which require efficient electrocatalysts of balanced atom utilization and site-specific requirements toward highly efficient SRR, calling for an in-depth understanding of the atomic structural sensitivity for the catalytic active sites. Herein, we manipulated the number of Fe atoms in iron assemblies, ranging from single Fe atom to diatomic and triatomic Fe atom groupings, all embedded within a carbon matrix. This led to the revelation of a "volcano peak" correlation between SRR catalytic activity and the count of Fe atoms at the active sites. Utilizing operando X-ray absorption and X-ray diffraction spectroscopies, we observed that polysulfide adsorption-desorption and electrochemical conversion kinetics varied up and down with the incremental addition of even a single iron atom to the catalyst's metal center. Our results demonstrate that the metal center with exactly two iron atoms represents the optimal configuration, maximizing atom utility and adeptly handling the conversion of varied intermediate sulfur species, rendering the Li-S battery with a high areal capacity of 23.8 mAh cm-2 at a high sulfur loading of 21.8 mg cm-2. Our results illuminate the pivotal balance between atom utilization and site-specific requirements for optimal electrocatalytic performance in SRR and diverse electrocatalytic reactions.

Analyzing the Active Site and Predicting the Overall Activity of Alloy Catalysts.

As one of the potential catalysts, disordered solid solution alloys can offer a wealth of catalytic sites. However, accurately evaluating their activity localization structure and overall activity from each individual site remains a formidable challenge. Herein, an approach based on density functional theory and machine learning was used to obtain a large number of sites of the Pt-Ru alloy as the model multisite catalyst for the hydrogen evolution reaction. Subsequently, a series of statistical approaches were employed to unveil the relationship between the geometric structure and overall activity. Based on the radial frequency distribution of metal elements and the distribution of ΔGH, we have identified the surface and subsurface sites occupied by Pt and Ru, respectively, as the most active sites. Particularly, the concept of equivalent site ratio predicts that the overall activity is highest when the Ru content is 20-30%. Furthermore, a series of Pt-Ru alloys were synthesized to validate the proposed theory. This provides crucial insights into understanding the origin of catalytic activity in alloys and thus will better guide the rational development of targeted multisite catalysts.

N-methyl Formamide Electrolyte Additive Enabling Highly Reversible Zn Anodes.

Parasitic side reactions and dendrites formation hinder the application of aqueous zinc ion batteries due to inferior cycling life and low reversibility. Against this background, N-methyl formamide (NMF), a multi-function electrolyte additive is applied to enhance the electrochemical performance. Studied via advanced synchrotron radiation spectroscopy and DFT calculations, the NMF additive simultaneously modifies the Zn2+ solvation structure and ensures uniform zinc deposition, thus suppressing both parasitic side reactions and dendrite formation. More importantly, an ultralong cycling life of 3115 h in the Zn||Zn symmetric cell at a current density of 0.5 mA cm-2 is achieved with the NMF additive. Practically, the Zn||PANI full cell utilizing NMF electrolyte shows better rate and cycling performance compared to the pristine ZnSO4 aqueous electrolyte. This work provides useful insights for the development of high-performance aqueous metal batteries.

Fruit freshness detection based on multi-task convolutional neural network.

Background: Fruit freshness detection by computer vision is essential for many agricultural applications, e.g., automatic harvesting and supply chain monitoring. This paper proposes to use the multi-task learning (MTL) paradigm to build a deep convolutional neural work for fruit freshness detection. Results: We design an MTL model that optimizes the freshness detection (T1) and fruit type classification (T2) tasks in parallel. The model uses a shared CNN (convolutional neural network) subnet and two FC (fully connected) task heads. The shared CNN acts as a feature extraction module and feeds the two task heads with common semantic features. Based on an open fruit image dataset, we conducted a comparative study of MTL and single-task learning (STL) paradigms. The STL models use the same CNN subnet with only one specific task head. In the MTL scenario, the T1 and T2 mean accuracies on the test set are 93.24% and 88.66%, respectively. Meanwhile, for STL, the two accuracies are 92.50% and 87.22%. Statistical tests report significant differences between MTL and STL on T1 and T2 test accuracies. We further investigated the extracted feature vectors (semantic embeddings) from the two STL models. The vectors have an averaged 0.7 cosine similarity on the entire dataset, with most values lying in the 0.6-0.8 range. This indicates a between-task correlation and justifies the effectiveness of the proposed MTL approach. Conclusion: This study proves that MTL exploits the mutual correlation between two or more relevant tasks and can maximally share their underlying feature extraction process. we envision this approach to be extended to other domains that involve multiple interconnected tasks.

Rapid determination of glyphosate and glufosinate in human blood by probe electrospray ionization tandem mass spectrometry.

In forensic science, glyphosate (GLYP) and glufosinate (GLUF), a class of non-selective broad-spectrum herbicides, have been frequently encountered in many fatal poisoning and suicide cases due to their widespread availability. Therefore, it is essential to develop an effective method for detecting these compounds. Some conventional methods, such as gas chromatography-mass spectrometry (GC-MS) or liquid chromatography-mass spectrometry (LC-MS), have been reported to detect these compounds. However, these methods are not ideal for their time-consuming and non-sensitive feature. Herein, probe electrospray ionization (PESI) tandem mass spectrometry (MS/MS), a fast and sensitive technique, was applied for the determination of GLYP and GLUF in human blood, which can obtain analytical results within 0.5 min without derivatization and chromatographic separation. After protein precipitation of blood samples, the supernatant was mixed with isopropanol and ultra-pure water (1:1 v/v). Then, 8 µL of the mixture was introduced into the plastic sample plate for PESI-MS/MS analysis. The limits of detection (LODs) of the method were 0.50 µg/mL and 0.25 µg/mL for two analytes, and the limits of quantitation (LOQs) were both 1.00 µg/mL, which are higher than the concentration of reported poisoning and fatal cases. In the linear range of 1-500 µg/mL, the regression coefficients (r2) for GLYP and GLUF were over 0.99. The matrix effects ranged from 94.8 % to 119.5 %, and the biases were below 4.3 %. The recoveries ranged between 84.8 % and 107.4 %, and the biases were below 7.6 %. Meanwhile, the method was effectively utilized to detect and quantify the blood, urine, and other samples. Consequently, the results suggest that PESI-MS/MS is a straightforward, fast, and sensitive method for detecting GLUF and GLYP in forensics. In the future, PESI-MS/MS will become an indispensable technique for polar substances in grassroots units of public security where rapid detection is essential.

Long-term outcomes of intraoperative chemotherapy with 5-FU for colorectal cancer patients receiving curative resection (IOCCRC): a randomized, multicenter, prospective, phase III trial.

BACKGROUND: We aimed to compare combined intraoperative chemotherapy and surgical resection with curative surgical resection alone in colorectal cancer patients. METHODS: We performed a multicenter, open-label, randomized, phase III trial. All eligible patients were randomized and assigned to intraoperative chemotherapy and curative surgical resection or curative surgical resection alone (1:1). Survival actualization after long-term follow-up was performed in patients analyzed on an intention-to-treat basis. RESULTS: From January 2011 to January 2016, 696 colorectal cancer patients were enrolled and randomly assigned to intraoperative chemotherapy and radical surgical resection (n=341) or curative surgical resection alone (n=344). Intraoperative chemotherapy with surgical resection showed no significant survival benefit over surgical resection alone in colorectal cancer patients (3-year DFS: 91.1% vs. 90.0%, P=0.328; 3-year OS: 94.4% vs. 95.9%, P=0.756). However, colon cancer patients benefitted from intraoperative chemotherapy, with a relative 4% reduction in liver and peritoneal metastasis (HR=0.336, 95% CI: 0.148-0.759, P=0.015) and a 6.5% improvement in 3-year DFS (HR=0.579, 95% CI: 0.353-0.949, P=0.032). Meanwhile, patients with colon cancer and abnormal pretreatment CEA levels achieved significant survival benefits from intraoperative chemotherapy (DFS: HR=0.464, 95% CI: 0.233-0.921, P=0.029 and OS: (HR=0.476, 95% CI: 0.223-1.017, P=0.049). CONCLUSIONS: Intraoperative chemotherapy showed no significant extra prognostic benefit in total colorectal cancer patients who underwent radical surgical resection; however, in colon cancer patients with abnormal pretreatment serum CEA levels (> 5 ng/ml), intraoperative chemotherapy could improve long-term survival.

Terahertz-triggered ultrafast nonlinear optical activities in two-dimensional centrosymmetric PtSe2.

The nonlinear mechanisms of polarization and optical fields can induce extensive responses in materials. In this study, we report on two kinds of nonlinear mechanisms in the topological semimetal PtSe2 crystal under the excitation of intense terahertz (THz) pulses, which are manipulated by the real and imaginary parts of the nonlinear susceptibility of PtSe2. Regarding the real part, the broken inversion symmetry of PtSe2 is achieved through a THz-electric-field polarization approach, which is characterized by second harmonic generation (SHG) measurements. The transient THz-laser-induced SHG signal occurs within 100 fs and recombines to the equilibrium state within 1 ps, along with a high signal-to-noise ratio (∼51 dB) and a high on/off ratio (∼102). Regarding the imaginary part, a nonlinear absorption change can be generated in the media. We reveal a THz-induced absorption enhancement in PtSe2 via nonlinear transmittance measurements, and the sheet conductivity can be modulated up to 42% by THz electric fields in our experiment. Therefore, the THz-induced ultrafast nonlinear photoresponse reveals the application potential of PtSe2 in photonic and optoelectronic devices in the THz technology.

High-Throughput Screening of Sulfur Reduction Reaction Catalysts Utilizing Electronic Fingerprint Similarity.

The catalytic performance is determined by the electronic structure near the Fermi level. This study presents an effective and simple screening descriptor, i.e., the one-dimensional density of states (1D-DOS) fingerprint similarity, to identify potential catalysts for the sulfur reduction reaction (SRR) in lithium-sulfur batteries. The Δ1D-DOS in relation to the benchmark W2CS2 was calculated. This method effectively distinguishes and identifies 30 potential candidates for the SRR from 420 types of MXenes. Further analysis of the Gibbs free energy profiles reveals that MXene candidates exhibit promising thermodynamic properties for SRR, with the protocol achieving an accuracy rate exceeding 93%. Based on the crystal orbital Hamilton population (COHP) and differential charge analysis, it is confirmed that the Δ1D-DOS could effectively differentiate the interaction between MXenes and lithium polysulfide (LiPS) intermediates. This study underscores the importance of the electronic fingerprint in catalytic performance and thus may pave a new way for future high-throughput material screening for energy storage applications.

Metal-Sulfur Interfaces as the Primary Active Sites for Catalytic Hydrogenations.

The determination of catalytically active sites is crucial for understanding the catalytic mechanism and providing guidelines for the design of more efficient catalysts. However, the complex structure of supported metal nanocatalysts (e.g., support, metal surface, and metal-support interface) still presents a big challenge. In particular, many studies have demonstrated that metal-support interfaces could also act as the primary active sites in catalytic reactions, which is well elucidated in oxide-supported metal nanocatalysts but is rarely reported in carbon-supported metal nanocatalysts. Here, we fill the above gap and demonstrate that metal-sulfur interfaces in sulfur-doped carbon-supported metal nanocatalysts are the primary active sites for several catalytic hydrogenation reactions. A series of metal nanocatalysts with similar sizes but different amounts of metal-sulfur interfaces were first constructed and characterized. Taking Ir for quinoline hydrogenation as an example, it was found that their catalytic activities were proportional to the amount of the Ir-S interface. Further experiments and density functional theory (DFT) calculations suggested that the adsorption and activation of quinoline occurred on the Ir atoms at the Ir-S interface. Similar phenomena were found in p-chloronitrobenzene hydrogenation over the Pt-S interface and benzoic acid hydrogenation over the Ru-S interface. All of these findings verify the predominant activity of metal-sulfur interfaces for catalytic hydrogenation reactions and contribute to the comprehensive understanding of metal-support interfaces in supported nanocatalysts.

Universal Intercalation/Alloying Hybrid Mechanism with -ICOHP Criterion in MAX Toward Steadily Ascending Lithium-Ion Batteries.

Profiting from the unique atomic laminated structure, metallic conductivity, and superior mechanical properties, transition metal carbides and nitrides named MAX phases have shown great potential as anodes in lithium-ion batteries. However, the complexity of MAX configurations poses a challenge. To accelerate such application, a minus integrated crystal orbital Hamilton populations descriptor is innovatively proposed to rapidly evaluate the lithium storage potential of various MAX, along with density functional theory computations. It confirms that surface A-element atoms bound to lithium ions have odds of escaping from MAX. Interestingly, the activated A-element atoms enhance the reversible uptake of lithium ions by MAX anodes through an efficient alloying reaction. As an experimental verification, the charge compensation and SnxLiy phase evolution of designed Zr2SnC MAX with optimized structure is visualized via in situ synchrotron radiation XRD and XAFS technique, which further clarifies the theoretically expected intercalation/alloying hybrid storage mechanism. Notably, Zr2SnC electrodes achieve remarkably 219.8% negative capacity attenuation over 3200 cycles at 1 A g-1. In principle, this work provides a reference for the design and development of advanced MAX electrodes, which is essential to explore diversified applications of the MAX family in specific energy fields.

FC-TFS-CGRU: A Temporal-Frequency-Spatial Electroencephalography Emotion Recognition Model Based on Functional Connectivity and a Convolutional Gated Recurrent Unit Hybrid Architecture.

The gated recurrent unit (GRU) network can effectively capture temporal information for 1D signals, such as electroencephalography and event-related brain potential, and it has been widely used in the field of EEG emotion recognition. However, multi-domain features, including the spatial, frequency, and temporal features of EEG signals, contribute to emotion recognition, while GRUs show some limitations in capturing frequency-spatial features. Thus, we proposed a hybrid architecture of convolutional neural networks and GRUs (CGRU) to effectively capture the complementary temporal features and spatial-frequency features hidden in signal channels. In addition, to investigate the interactions among different brain regions during emotional information processing, we considered the functional connectivity relationship of the brain by introducing a phase-locking value to calculate the phase difference between the EEG channels to gain spatial information based on functional connectivity. Then, in the classification module, we incorporated attention constraints to address the issue of the uneven recognition contribution of EEG signal features. Finally, we conducted experiments on the DEAP and DREAMER databases. The results demonstrated that our model outperforms the other models with remarkable recognition accuracy of 99.51%, 99.60%, and 99.59% (58.67%, 65.74%, and 67.05%) on DEAP and 98.63%, 98.7%, and 98.71% (75.65%, 75.89%, and 71.71%) on DREAMER in a subject-dependent experiment (subject-independent experiment) for arousal, valence, and dominance.

Electrocatalytic Mechanism and Sabatier Principle in C2N-Supported Atomically Dispersed Catalysts for the Sulfur Reduction Reaction in Lithium-Sulfur Batteries.

The sluggish kinetics of the sulfur reduction reaction (SRR) impedes the practical application of lithium-sulfur batteries (LSBs). Electrocatalysts are necessary to expedite the conversion of polysulfides. Here, we systematically investigate the chemical mechanisms and size dependence of catalytic activities toward the SRR from Li2S4 to Li2S on single-, double-, and triple-atom catalysts supported on C2N (Mn@C2N, where M is a 3d transitional metal and n = 1-3) as model systems by using first-principles calculations and a comprehensive electrocatalytic model. Our results reveal that the adsorption strength of the LiS• intermediate is identified as an optimal descriptor for catalytic activity. M1@C2N exhibits superior stability and exceptional activity compared to those of the other two catalyst types. Cu1@C2N exhibits the lowest overpotential of 0.426 V. Li embedding or a prelithiation strategy verifies the therein Sabatier principle. This work emphasizes the precise control of the active site structure and microenvironment in catalytic SRR and offers guidance for the design of electrocatalysts for metal-sulfur batteries.

CSCA U-Net: A channel and space compound attention CNN for medical image segmentation.

Image segmentation is one of the vital steps in medical image analysis. A large number of methods based on convolutional neural networks have emerged, which can extract abstract features from multiple-modality medical images, learn valuable information that is difficult to recognize by humans, and obtain more reliable results than traditional image segmentation approaches. U-Net, due to its simple structure and excellent performance, is widely used in medical image segmentation. In this paper, to further improve the performance of U-Net, we propose a channel and space compound attention (CSCA) convolutional neural network, CSCA U-Net in abbreviation, which increases the network depth and employs a double squeeze-and-excitation (DSE) block in the bottleneck layer to enhance feature extraction and obtain more high-level semantic features. Moreover, the characteristics of the proposed method are three-fold: (1) channel and space compound attention (CSCA) block, (2) cross-layer feature fusion (CLFF), and (3) deep supervision (DS). Extensive experiments on several available medical image datasets, including Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, ETIS, CVC-T, 2018 Data Science Bowl (2018 DSB), ISIC 2018, and JSUAH-Cerebellum, show that CSCA U-Net achieves competitive results and significantly improves generalization performance. The codes and trained models are available at https://github.com/xiaolanshu/CSCA-U-Net.

Unraveling the Nucleation and Growth Mechanism of Potassium Metal on 3D Skeletons for Dendrite-Free Potassium Metal Batteries.

Designing three-dimensional (3D) porous carbonaceous skeletons for K metal is one of the most promising strategies to inhibit dendrite growth and enhance the cycle life of potassium metal batteries. However, the nucleation and growth mechanism of K metal on 3D skeletons remains ambiguous, and the rational design of suitable K hosts still presents a significant challenge. In this study, the relationships between the binding energy of skeletons toward K and the nucleation and growth of K are systematically studied. It is found that a high binding energy can effectively decrease the nucleation barrier, reduce nucleation volume, and prevent dendrite growth, which is applied to guide the design of 3D current collectors. Density functional theory calculations show that P-doped carbon (P-carbon) exhibits the highest binding energy toward K compared to other elements (e.g., N, O). As a result, the K@P-PMCFs (P-binding porous multichannel carbon nanofibers) symmetric cell demonstrates an excellent cycle stability of 2100 h with an overpotential of 85 mV in carbonate electrolytes. Similarly, the perylene-3,4,9,10-tetracarboxylic dianhydride || K@P-PMCFs cell achieves ultralong cycle stability (85% capacity retention after 1000 cycles). This work provides a valuable reference for the rational design of 3D current collectors.

Highly Crystalline Iridium-Nickel Nanocages with Subnanopores for Acidic Bifunctional Water Splitting Electrolysis.

Developing efficient bifunctional materials is highly desirable for overall proton membrane water splitting. However, the design of iridium materials with high overall acidic water splitting activity and durability, as well as an in-depth understanding of the catalytic mechanism, is challenging. Herein, we successfully developed subnanoporous Ir3Ni ultrathin nanocages with high crystallinity as bifunctional materials for acidic water splitting. The subnanoporous shell enables Ir3Ni NCs optimized exposure of active sites. Importantly, the nickel incorporation contributes to the favorable thermodynamics of the electrocatalysis of the OER after surface reconstruction and optimized hydrogen adsorption free energy in HER electrocatalysis, which induce enhanced intrinsic activity of the acidic oxygen evolution reaction (OER) and hydrogen evolution reaction (HER). Together, the Ir3Ni nanocages achieve 3.72 A/mgIr(η=350 mV) and 4.47 A/mgIr(η=40 mV) OER and HER mass activity, which are 18.8 times and 3.3 times higher than that of commercial IrO2 and Pt, respectively. In addition, their highly crystalline identity ensures a robust nanostructure, enabling good catalytic durability during the oxygen evolution reaction after surface oxidation. This work provides a new revenue toward the structural design and insightful understanding of metal alloy catalytic mechanisms for the bifunctional acidic water splitting electrocatalysis.

Critical Role of Aromatic C(sp2)-H in Boosting Lithium-Ion Storage.

Exploring high-sloping-capacity carbons is of great significance in the development of high-power lithium-ion batteries/capacitors (LIBs/LICs). Herein, an ion-catalyzed self-template method is utilized to synthesize the hydrogen-rich carbon nanoribbon (HCNR), achieving high specific and rate capacity (1144.2/471.8 mAh g-1 at 0.1/2.5 A g-1). The Li+ storage mechanism of the HCNR is elucidated by in situ spectroscopic techniques. Intriguingly, the protonated aromatic sp2-hybridized carbon (C(sp2)-H) can provide additional active sites for Li+ uptake via reversible rehybridization to sp3-C, which is the origin of the high sloping capacity. The presence of this sloping feature suggests a highly capacitance-dominated storage process, characterized by rapid kinetics that facilitates superior rate performance. For practical usage, the HCNR-based LIC device can deliver high energy/power densities of 198.3 Wh kg-1/17.9 kW kg-1. This work offers mechanistic insights on the crucial role of aromatic C(sp2)-H in boosting Li+ storage and opens up new avenues to develop such sloping-type carbons for high-performance rechargeable batteries/capacitors.

SPD Manifold Deep Metric Learning for Image Set Classification.

By characterizing each image set as a nonsingular covariance matrix on the symmetric positive definite (SPD) manifold, the approaches of visual content classification with image sets have made impressive progress. However, the key challenge of unhelpfully large intraclass variability and interclass similarity of representations remains open to date. Although, several recent studies have mitigated the two problems by jointly learning the embedding mapping and the similarity metric on the original SPD manifold, their inherent shallow and linear feature transformation mechanism are not powerful enough to capture useful geometric features, especially in complex scenarios. To this end, this article explores a novel approach, termed SPD manifold deep metric learning (SMDML), for image set classification. Specifically, SMDML first selects a prevailing SPD manifold neural network (SPDNet) as the backbone (encoder) to derive an SPD matrix nonlinear representation. To counteract the degradation of structural information during multistage feature embedding, we construct a Riemannian decoder at the end of the encoder, trained by a reconstruction error term (RT), to induce the generated low-dimensional feature manifold of the hidden layer to capture the pivotal information about the visual data describing the imaged scene. We demonstrate through theory and experiments that it is feasible to replace the Riemannian metric with Euclidean distance in RT. Then, the ReCov layer is introduced into the established Riemannian network to regularize the local statistical information within each input feature matrix, which enhances the effectiveness of the learning process. The theoretical analysis of the activation function used in the ReCov layer in terms of continuity and conditions for generating positive definite matrices is beneficial for network design. Inspired by the fact that the single cross-entropy loss used for training is unable to effectively parse the geometric distribution of the deep representations, we finally endow the suggested model with a novel metric learning regularization term. By explicitly incorporating the encoding and processing of the data variations into the network learning process, this term can not only derive a powerful Riemannian representation but also train an effective classifier. The experimental results show the superiority of the proposed approach on three typical visual classification tasks.

Identification of Synergies in Fe, Co-Coordinated Polyphthalocyanines Scaffolds for Electrochemical CO2 Reduction Reaction.

The synergistic interaction between the isolated metal sites promoted the electrocatalytic activity of the catalysts. However, the structural heterogeneity of the isolated sites makes it challenging to evaluate this effect accurately. In this work, metal-coordinated polyphthalocyanine molecules (Fe-PPc, Co-PPc, FeCo-PPc) with long-range ordered and precise coordination structures are used as a platform to study the synergies of different isolated metal sites in the electrochemical CO2 reduction reaction. The combination means of experimental and theoretical calculation clearly reveal that the coexistence of Fe and Co sites in PPc significantly enhances the conjugation effect of the macrocycle. This enhancement subsequently causes the metal sites to lose more electrons, thereby improving their adsorption of CO2 and facilitating the formation of intermediate *COOH on them. As a result, FeCo-PPc achieves a CO partial current density of about 57.4 mA/cm2 with a high turnover frequency of over 49000 site-1 h-1 at -0.9 V (vs RHE).