Spatial and geometric learning for classification of breast tumors from multi-center ultrasound images: a hybrid learning approach.

BACKGROUND: Breast cancer is the most common cancer among women, and ultrasound is a usual tool for early screening. Nowadays, deep learning technique is applied as an auxiliary tool to provide the predictive results for doctors to decide whether to make further examinations or treatments. This study aimed to develop a hybrid learning approach for breast ultrasound classification by extracting more potential features from local and multi-center ultrasound data. METHODS: We proposed a hybrid learning approach to classify the breast tumors into benign and malignant. Three multi-center datasets (BUSI, BUS, OASBUD) were used to pretrain a model by federated learning, then every dataset was fine-tuned at local. The proposed model consisted of a convolutional neural network (CNN) and a graph neural network (GNN), aiming to extract features from images at a spatial level and from graphs at a geometric level. The input images are small-sized and free from pixel-level labels, and the input graphs are generated automatically in an unsupervised manner, which saves the costs of labor and memory space. RESULTS: The classification AUCROC of our proposed method is 0.911, 0.871 and 0.767 for BUSI, BUS and OASBUD. The balanced accuracy is 87.6%, 85.2% and 61.4% respectively. The results show that our method outperforms conventional methods. CONCLUSIONS: Our hybrid approach can learn the inter-feature among multi-center data and the intra-feature of local data. It shows potential in aiding doctors for breast tumor classification in ultrasound at an early stage.

Advance on combination therapy strategies based on biomedical nanotechnology induced ferroptosis for cancer therapeutics.

Globally, cancer is a serious health problem. It is unfortunate that current anti-cancer strategies are insufficiently specific and damage the normal tissues. There's urgent need for development of new anti-cancer strategies. More recently, increasing attention has been paid to the new application of ferroptosis and nano materials in cancer research. Ferroptosis, a condition characterized by excessive reactive oxygen species-induced lipid peroxidation, as a new programmed cell death mode, exists in the process of a number of diseases, including cancers, neurodegenerative disease, cerebral hemorrhage, liver disease, and renal failure. There is growing evidence that inducing ferroptosis has proven to be an effective strategy against a variety of chemo-resistant cancer cells. Nano-drug delivery system based on nanotechnology provides a highly promising platform with the benefits of precise control of drug release and reduced toxicity and side effects. This paper reviews the latest advances of combination therapy strategies based on biomedical nanotechnology induced ferroptosis for cancer therapeutics. Given the new chances and challenges in this emerging area, we need more attention to the combination of nanotechnology and ferroptosis in the treatment of cancer in the future.

Bionic Scotopic Adaptation Transistors for Nighttime Low Illumination Imaging.

Human vision excels in perceiving nighttime low illumination due to biological feedforward adaptation. Replicating this ability in biomimetic vision using solid-state devices has been highly sought after. However, emulating scotopic adaptation, entailing a confluence of efficient photoexcitation and dynamic carrier modulation, presents formidable challenges. Here, we demonstrate a low-power and bionic scotopic adaptation transistor by coupling a light-absorption layer and an electron-trapping layer at the bottom of the semiconducting channel, enabling simultaneous achievement of efficient generation of free photocarriers and adaptive carrier accumulation within a single device. This innovation empowers our transistor to exhibit sensitivity-potentiated characteristics after adaptation, detecting scotopic-level illumination (0.001 lx) with exceptional photosensitivity up to 103 at low voltages below 2 V. Moreover, we have successfully replicated diverse scotopic vision functions, encompassing time-dependent visual threshold enhancement, light intensity-dependent adaptation index, imaging contrast enhancement for nighttime low illumination imaging, opening an opportunity for artificial night vision.

Understanding secondary particles in a regional site of Yangtze River Delta: Insights from mass spectrometric measurement.

Submicron particulate matter (PM1) poses significant risks to health risks and global climate. In this study, secondary organic aerosols (SOA) and inorganic compositions were examined for their physicochemical characteristics and evolution using high-resolution aerosol instruments in Changzhou over one-month period. The results showed that transport accompanied by regional static conditions leaded to the occurrence of heavy pollution. In addition, regional generation and local emissions also leaded to the occurrence of light and moderate pollution during the observation period in Changzhou. Organic aerosols (OA) and nitrate (NO3-) accounted for 45 % and 23 % of PM1, respectively. The increase in PM1 was dominated by the contribution of NO3- and OA. SOA was dominance in OA (63 % with 40 % MO-OOA), which was higher than primary organic aerosols (POA). Besides, photochemical reactions and the high oxidizing nature of the urban atmosphere promoted the production of OA, especially MO-OOA in Changzhou. Our results highlight that secondary particles contribute significantly to PM pollution in Changzhou, underlining the importance of controlling emissions of gaseous precursors, especially under high oxidation conditions.

A survey of label-noise deep learning for medical image analysis.

Several factors are associated with the success of deep learning. One of the most important reasons is the availability of large-scale datasets with clean annotations. However, obtaining datasets with accurate labels in the medical imaging domain is challenging. The reliability and consistency of medical labeling are some of these issues, and low-quality annotations with label noise usually exist. Because noisy labels reduce the generalization performance of deep neural networks, learning with noisy labels is becoming an essential task in medical image analysis. Literature on this topic has expanded in terms of volume and scope. However, no recent surveys have collected and organized this knowledge, impeding the ability of researchers and practitioners to utilize it. In this work, we presented an up-to-date survey of label-noise learning for medical image domain. We reviewed extensive literature, illustrated some typical methods, and showed unified taxonomies in terms of methodological differences. Subsequently, we conducted the methodological comparison and demonstrated the corresponding advantages and disadvantages. Finally, we discussed new research directions based on the characteristics of medical images. Our survey aims to provide researchers and practitioners with a solid understanding of existing medical label-noise learning, such as the main algorithms developed over the past few years, which could help them investigate new methods to combat with the negative effects of label noise.

Breaking Fundamental Limitation of Flow-Induced Anisotropic Growth for Large-Scale and Fast Printing of Organic Single-Crystal Films.

Advanced organic electronic technologies have put forward a pressing demand for cost-effective and high-throughput fabrication of organic single-crystal films (OSCFs). However, solution-printed OSCFs are typically plagued by the existence of abundant structural defects, which pose a formidable challenge to achieving large-scale and high-performance organic electronics. Here, it is elucidated that these structural defects are mainly originated from printing flow-induced anisotropic growth, an important factor that is overlooked for too long. In light of this, a surfactant-additive printing method is proposed to effectively overcome the anisotropic growth, enabling the deposition of uniform OSCFs over the wafer scale at a high speed of 1.2 mm s-1 at room temperature. The resulting OSCF exhibits appealing performance with a high average mobility up to 10.7 cm2 V-1 s-1, which is one of the highest values for flexible organic field-effect transistor arrays. Moreover, large-scale OSCF-based flexible logic circuits, which can be bent without degradation to a radius as small as 4.0 mm and over 1000 cycles are realized. The work provides profound insights into breaking the limitation of flow-induced anisotropic growth and opens new avenues for printing large-scale organic single-crystal electronics.

Motion-artifact-augmented pseudo-label network for semi-supervised brain tumor segmentation.

MRI image segmentation is widely used in clinical practice as a prerequisite and a key for diagnosing brain tumors. The quest for an accurate automated segmentation method for brain tumor images, aiming to ease clinical doctors' workload, has gained significant attention as a research focal point. Despite the success of fully supervised methods in brain tumor segmentation, challenges remain. Due to the high cost involved in annotating medical images, the dataset available for training fully supervised methods is very limited. Additionally, medical images are prone to noise and motion artifacts, negatively impacting quality. In this work, we propose MAPSS, a motion-artifact-augmented pseudo-label network for semi-supervised segmentation. Our method combines motion artifact data augmentation with the pseudo-label semi-supervised training framework. We conduct several experiments under different semi-supervised settings on a publicly available dataset BraTS2020 for brain tumor segmentation. The experimental results show that MAPSS achieves accurate brain tumor segmentation with only a small amount of labeled data and maintains robustness in motion-artifact-influenced images. We also assess the generalization performance of MAPSS using the Left Atrium dataset. Our algorithm is of great significance for assisting doctors in formulating treatment plans and improving treatment quality.

An AFM-Based Model-Fitting-Free Viscoelasticity Characterization Method for Accurate Grading of Primary Prostate Tumor.

Viscoelasticity is a crucial property of cells, which plays an important role in label-free cell characterization. This paper reports a model-fitting-free viscoelasticity calculation method, correcting the effects of frequency, surface adhesion and liquid resistance on AFM force-distance (FD) curves. As demonstrated by quantifying the viscosity and elastic modulus of PC-3 cells, this method shows high self-consistency and little dependence on experimental parameters such as loading frequency, and loading mode (Force-volume vs. PeakForce Tapping). The rapid calculating speed of less than 1ms per curve without the need for a model fitting process is another advantage. Furthermore, this method was utilized to characterize the viscoelastic properties of primary clinical prostate cells from 38 patients. The results demonstrate that the reported characterization method a comparable performance with the Gleason Score system in grading prostate cancer cells, This method achieves a high average accuracy of 97.6% in distinguishing low-risk prostate tumors (BPH and GS6) from higher-risk (GS7-GS10) prostate tumors and a high average accuracy of 93.3% in distinguishing BPH from prostate cancer.

Topology-Mediated Molecule Nucleation Anchoring Enables Inkjet Printing of Organic Semiconducting Single Crystals for High-Performance Printed Electronics.

Printable organic semiconducting single crystals (OSSCs) offer tantalizing opportunities for next-generation wearable electronics, but their development has been plagued by a long-standing yet inherent problem─spatially uncontrolled and stochastic nucleation events─which usually causes the formation of polycrystalline films and hence limited performance. Here, we report a convenient approach to precisely manipulate the elusive molecule nucleation process for high-throughput inkjet printing of OSSCs with record-high mobility. By engineering curvature of the contact line with a teardrop-shaped micropattern, molecule nucleation is elegantly anchored at the vertex of the topological structure, enabling formation of a single nucleus for the subsequent growth of OSSCs. Using this approach, we achieve patterned growth of 2,7-dioctyl[1]benzothieno[3,2-b][1]benzothiophene single crystals, yielding a breakthrough for an organic field-effect transistor array with a high average mobility of 12.5 cm2 V-1 s-1. These findings not only provide keen insights into controlling molecule nucleation kinetics but also offer opportunities for high-performance printed electronics.

An integrated hinged dual-probe for co-target fast switching imaging.

The diversity of functional applications of atomic force microscopes is the key to the development of nanotechnology. However, the single probe configuration of the traditional atomic force microscope restricts the realization of different application requirements for the same target area of a single sample, and the replacement of the working probe will lead to the loss of the target area. Here, the design, simulation, fabrication, and application of a unique atomic force microscope dual-probe are presented, which consists of a pair of parallel cantilevers with a narrow gap and a U-shaped hinged probe base. The Integrated Hinged Dual-Probe (IHDP) is developed specifically for fast switching of probes working in limited space and independent and precise manipulation of each probe. The deflection signal sensing of two cantilevers is achieved simultaneously by a single laser beam, and the decoupled independent cantilever deflection signals do not interfere with each other. The switching of the working probe is achieved by a piezoelectric ceramic with a 2 µm stroke and U-shaped hinge structure, which is fast and does not require tedious and repetitive spatial position calibration. By measuring standard grid samples, IHDP exhibits excellent measurement and characterization capabilities. Finally, a working probe switching imaging experiment was conducted on solidified rat cardiomyocytes, and the experimental process and imaging results demonstrated the superiority of IHDP in switching probe scanning imaging of the same target area of a single sample. The two probes of IHDP can undergo arbitrary functionalization modifications, which helps achieve multidimensional information acquisition for a single target.

ErbB4 promotes M2 activation of macrophages in idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is the most common and fatal diffuse fibrotic lung disease accompanied by macrophage M2 activation. ErbB4 is involved in and affects the process of inflammation. In this study, we determined that the mRNA level and protein expression of ErbB4 and M2 cytokine members were increased in the serum of IPF patients. In mouse alveolar macrophage MH-S cells, after knocking down ErbB4 by siRNA, the mRNA level and protein expression of M2 activator induced by interleukin (IL)-4 were decreased compared with the control group. Activating by ErbB4 agonist neuromodulatory protein (NRG)-1, IL-4-induced M2 program was promoted. Mechanistically, treated with NRG-1 in MH-S cells, the phosphorylation level of Akt did not change, while the phosphorylation level of ERK increased. Using SCH772984 to inhibit ERK pathway, the increasing IL-4-induced M2 activation by NRG-1 was inhibited, and the high level of M2 activator protein expression and mRNA expression was restored. Collectively, our data support that ErbB4 and M2 programs are implicated in IPF, and ErbB4 participates in the regulation of M2 activation induced by IL-4 through the ERK pathway.

Identification of key biomarkers based on the proliferation of secondary hyperparathyroidism by bioinformatics analysis and machine learning.

Objective: Secondary hyperparathyroidism (SHPT) is a frequent complication of chronic kidney disease (CKD) associated with morbidity and mortality. This study aims to identify potential biomarkers that may be used to predict the progression of SHPT and to elucidate the molecular mechanisms of SHPT pathogenesis at the transcriptome level. Methods: We analyzed differentially expressed genes (DEGs) between diffuse and nodular parathyroid hyperplasia of SHPT patients from the GSE75886 dataset, and then verified DEG levels with the GSE83421 data file of primary hyperparathyroidism (PHPT) patients. Candidate gene sets were selected by machine learning screens of differential genes and immune cell infiltration was explored with the CIBERSORT algorithm. RcisTarget was used to predict transcription factors, and Cytoscape was used to construct a lncRNA-miRNA-mRNA network to identify possible molecular mechanisms. Immunohistochemistry (IHC) staining and quantitative real-time polymerase chain reaction (qRT-PCR) were used to verify the expression of screened genes in parathyroid tissues of SHPT patients and animal models. Results: A total of 614 DEGs in GSE75886 were obtained as candidate gene sets for further analysis. Five key genes (USP12, CIDEA, PCOLCE2, CAPZA1, and ACCN2) had significant expression differences between groups and were screened with the best ranking in the machine learning process. These genes were shown to be closely related to immune cell infiltration levels and play important roles in the immune microenvironment. Transcription factor ZBTB6 was identified as the master regulator, alongside multiple other transcription factors. Combined with qPCR and IHC assay of hyperplastic parathyroid tissues from SHPT patients and rats confirm differential expression of USP12, CIDEA, PCOLCE2, CAPZA1, and ACCN2, suggesting that they may play important roles in the proliferation and progression of SHPT. Conclusion: USP12, CIDEA, PCOLCE2, CAPZA1, and ACCN2 have great potential both as biomarkers and as therapeutic targets in the proliferation of SHPT. These findings suggest novel potential targets and future directions for SHPT research.

Boosting the Performance of Organic Photodetectors with a Solution-Processed Integration Circuit toward Ubiquitous Health Monitoring.

Organic photodetectors, as an emerging wearable photoplethysmographic (PPG) technology, offer exciting opportunities for next-generation photonic healthcare electronics. However, the mutual restraints among photoresponse, structure complexity, and fabrication cost have intrinsically limited the development of organic photodetectors for ubiquitous health monitoring in daily activities. Here, an effective route to dramatically boost the performance of organic photodetectors with a solution-processed integration circuit for health monitoring application is reported. Through creating an ideal metal-semiconductor junction interface that minimizes the trap states within the device, solution-printed organic field-effect transistors (OFETs) are achieved with an ultrahigh signal amplification efficiency of 37.1 S A-1 , approaching the theoretical thermionic limit. Consequently, monolithic integration of the OFET with an organic photoconductor enables the remarkable amplification of photoresponse signal-to-noise ratio by more than four orders of magnitude from 5.5 to 4.6 × 105 , which is able to meet the demand for accurately extracting physiological information from the PPG waveforms. This work offers an effective and versatile approach to greatly enhance the photodetector performance, promising to revolutionize health monitoring technologies.

Efficient inactivation of harmful algae K. mikimotoi by a novel algicidal bacterium via a rare direct contact pathway: Performances and mechanisms.

Harmful algal blooms (HABs) caused by Karenia mikimotoi have posed great threats to marine ecosystems, and algal inactivation by symbiotic bacteria has been recognized as environmental benign methods for controlling HABs. However, the identified algicidal bacteria for K. mikimotoi is limited and exclusively based on indirect algicidal pathways, which may cause secondary pollution due to releasing toxic algicidal agents. In this study, a novel strain of algicidal bacteria Tenacibaculum sp. GD3 was isolated from the phycosphere of K. mikimotoi. The bacterial strain GD3 could achieve 92.6 % of inactivation efficiency against K. mikimotoi within 8 h of co-culturing period, which outperformed those in existing literatures reported so far. The algicidal mechanisms were revealed to be a rare direct cell-to-cell contact pathway, and the GD3 could grow by utilizing metabolites from K. mikimotoi, exhibiting excellent bacterial adaptability in the phycosphere. Cell morphology changes were monitored by live cell imaging system combined with SEM and TEM observations, which showed that the GD3 was first attached to the algal cell membrane, followed by lipid peroxidation and lysis of membrane protein. Oxidative stress responses were induced as reveled by up-regulation of intracellular ROSs and antioxidant enzyme activity. Photosynthetic parameters including rETRmax, Fv/Fm, YII and NPQ were reduced, and expression of functional genes involved in decomposition of chlorophyll and cell wall was significantly suppressed. Moreover, the intracellular release profile and acute toxicity assessment indicated that the GD3 could also detoxify the K. mikimotoi cultures and the released biomolecules would not cause adverse effect to marine environment. This study not only provides a novel algicidal bacterium against K. mikimotoi via a rare direct mode, but also helps to better understand the algicidal mechanisms at physiological and genetic level, thus moving forward the areas of HABs control by microbiological strategies.

Emotional involvement matters, too: Associations among parental involvement, time management and academic engagement vary with Youth's developmental phase.

BACKGROUND: Prior studies have emphasized the importance of parents' educational involvement (a type of cognitive involvement) to academic engagement, although little is known about emotional involvement. AIMS: This study investigated whether and how different facets of involvement (cognitive vs. emotional, paternal vs. maternal) are differentially related to academic engagement and whether and how the associations among parental involvement, time management and academic engagement vary by adolescents' developmental phases. SAMPLES: The participants of this large national survey were students in elementary, middle and high school across different regions of mainland China. A total of 2687 adolescents (52.7% females, Mage = 14.07 ± 2.47) participated in this study. METHODS: Structural equation models and multigroup analysis were conducted. RESULTS: We found that the total effect of paternal and maternal emotional involvement on academic engagement was positive in elementary-, middle- and high school students, and an indirect effect of time management underlying the above paths was found in all three groups. In contrast, the positive effect of maternal cognitive involvement on academic engagement as well as the indirect effects underlying the above pathways was established only in high school students. CONCLUSIONS: The findings highlight the necessity of parents' emotional involvement and the consideration of adolescent developmental characteristics in the design of interventions.

Attention guided neural ODE network for breast tumor segmentation in medical images.

Breast cancer is the most common cancer in women. Ultrasound is a widely used screening tool for its portability and easy operation, and DCE-MRI can highlight the lesions more clearly and reveal the characteristics of tumors. They are both noninvasive and nonradiative for assessment of breast cancer. Doctors make diagnoses and further instructions through the sizes, shapes and textures of the breast masses showed on medical images, so automatic tumor segmentation via deep neural networks can to some extent assist doctors. Compared to some challenges which the popular deep neural networks have faced, such as large amounts of parameters, lack of interpretability, overfitting problem, etc., we propose a segmentation network named Att-U-Node which uses attention modules to guide a neural ODE-based framework, trying to alleviate the problems mentioned above. Specifically, the network uses ODE blocks to make up an encoder-decoder structure, feature modeling by neural ODE is completed at each level. Besides, we propose to use an attention module to calculate the coefficient and generate a much refined attention feature for skip connection. Three public available breast ultrasound image datasets (i.e. BUSI, BUS and OASBUD) and a private breast DCE-MRI dataset are used to assess the efficiency of the proposed model, besides, we upgrade the model to 3D for tumor segmentation with the data selected from Public QIN Breast DCE-MRI. The experiments show that the proposed model achieves competitive results compared with the related methods while mitigates the common problems of deep neural networks.

Large Range Atomic Force Microscopy with High Aspect Ratio Micropipette Probe for Deep Trench Imaging.

Atomic force microscopy (AFM) has been adopted in both industry and academia for high-fidelity, full-profile topographic characterization. Typically, the tiny tip of the cantilever and the limited traveling range of the scanner restrict AFM measurement to relatively flat samples (recommend 1 µm). The primary objective of this work is to address these limitations using a large-range AFM (measuring height >10 µm) system consisting of a novel repairable high aspect ratio probe (HARP) with a nested-proportional-integral-derivative (nested-PID) AFM system. The HARP is fabricated using a reliable, cost-efficient bench-top process. The tip is then fused by pulling the end of the micropipette cantilever with a length up to hundreds of micrometers and a tip diameter of 30 nm. The design, simulation, fabrication, and performance of the HARP are described herein. This instrument is then tested using polymer trenches which reveals superior image fidelity compared to standard silicon tips. Finally, a nested-PID system is developed and employed to facilitate 3D characterization of 50-µm-step samples. The results demonstrate the efficacy of the proposed bench-top technique for the fabrication of low-cost, simple HAR AFM probes that facilitate the imaging of samples with deep trenches.

Simultaneous Anodic and Cathodic Formate Production in a Paired Electrolyzer by CO2 Reduction and Glycerol Oxidation.

Electrochemical CO2 conversion is a key technology to promote the production of carbon-containing molecules, alongside reducing CO2 emissions leading to a closed carbon cycle economy. Over the past decade, the interest to develop selective and active electrochemical devices for electrochemical CO2 reduction emerged. However, most reports employ oxygen evolution reaction as an anodic half-cell reaction causing the system to suffer from sluggish kinetics with no production of value-added chemicals. Therefore, this study reports a conceptualized paired electrolyzer for simultaneous anodic and cathodic formate production at high currents. To achieve this, CO2 reduction was coupled with glycerol oxidation: a BiOBr-modified gas-diffusion cathode and a Nix B on Ni foam anode keep their selectivity for formate in the paired electrolyzer compared to the half-cell measurements. The paired reactor here reaches a combined Faradaic efficiency for formate of 141 % (45 % anode and 96 % cathode) at a current density of 200 mA cm-2 .

Development and evaluation of machine learning models and nomogram for the prediction of severe acute pancreatitis.

BACKGROUND AND AIM: Severe acute pancreatitis (SAP) in patients progresses rapidly and can cause multiple organ failures associated with high mortality. We aimed to train a machine learning (ML) model and establish a nomogram that could identify SAP, early in the course of acute pancreatitis (AP). METHODS: In this retrospective study, 631 patients with AP were enrolled in the training cohort. For predicting SAP early, five supervised ML models were employed, such as random forest (RF), K-nearest neighbors (KNN), and naive Bayes (NB), which were evaluated by accuracy (ACC) and the areas under the receiver operating characteristic curve (AUC). The nomogram was established, and the predictive ability was assessed by the calibration curve and AUC. They were externally validated by an independent cohort of 109 patients with AP. RESULTS: In the training cohort, the AUC of RF, KNN, and NB models were 0.969, 0.954, and 0.951, respectively, while the AUC of the Bedside Index for Severity in Acute Pancreatitis (BISAP), Ranson and Glasgow scores were only 0.796, 0.847, and 0.837, respectively. In the validation cohort, the RF model also showed the highest AUC, which was 0.961. The AUC for the nomogram was 0.888 and 0.955 in the training and validation cohort, respectively. CONCLUSIONS: Our findings suggested that the RF model exhibited the best predictive performance, and the nomogram provided a visual scoring model for clinical practice. Our models may serve as practical tools for facilitating personalized treatment options and improving clinical outcomes through pre-treatment stratification of patients with AP.

Ag-induced Phase Transition of Bi2 O3 Nanofibers for Enhanced Energy Conversion Efficiency towards Formate in CO2 Electroreduction.

Bi-based electrocatalysts have been widely investigated in the CO2 reduction reaction (CO2 RR) for the formation of formate. However, it remains a challenge to achieve high Faradaic efficiency (FE) and industrial current densities at low overpotentials for obtaining both high formate productivity and energy efficiency (EE). Herein, we report an Ag-Bi2 O3 hybrid nanofiber (Ag-Bi2 O3 ) for highly efficient electrochemical reduction of CO2 to formate. Ag-Bi2 O3 exhibits a formate FE of >90% for current densities from -10 to -250 mA ⋅ cm-2 and attains a yield rate of 11.7 mmol ⋅ s-1 ⋅ m-2 at -250 mA ⋅ cm-2 . Moreover, Ag-Bi2 O3 increased the EE (52.7%) by nearly 10% compared to a Bi2 O3 only counterpart. Structural characterization and in-situ Raman results suggest that the presence of Ag induced the conversion of Bi2 O3 from a monoclinic phase (α-Bi2 O3 ) to a metastable tetragonal phase (ß-Bi2 O3 ) and accelerated the formation of active metallic Bi at low overpotentials (at > -0.3 V), which together contributes to the highly efficient formate formation.