Process Monitoring Research Articles

Inline Monitoring of continous Ultrasonic Welding Processes of Thermoplastic Composites via a custom polyCMUT based Ultrasound Array

Ultrasonic welding (UW) of thermoplastic composites (TCs) is an emerging technology in the field of composite joining techniques in the aerospace sector. Through a mechanical oscillator, ultrasound at a frequency of 20kHz is induced into the material via a welding horn, where microscopic friction and damping effects melt the thermoplastic. Under further pressure the weld area cools down, permanently joining both parts together. Like all joining processes in the aerospace industry the resulting joints need to be tested for their quality and structural integrity. The traditional testing method using water-coupled ultrasound includes extra steps. This process could be considerably improved by assessing the quality of the weld directly after or even during the welding process, allowing for immediate rework or discard of the parts in question. Ultrasound is still the best solution for this quality assessment, being inexpensive, well understood, and able to create B-Mode images, allowing a look into the cross-section of the weld. However, there are several major problems: To increase the system complexity as little as possible it is necessary to attach the ultrasound unit next to the welding equipment, and as close to the welding horn and compactor as possible to save space and keep the end- effector manoeuvrable. This brings problems for classic piezoelectric ultrasonic arrays: The low welding frequency and its resonance modes reach into the lower resonance modes of the piezoelectric sensors leading to immense noise, hiding any potential echo from the welding zone. Classic piezoelectric crystals are also very brittle and can suffer damage from sustained exposure to this violent environment. The authors present a novel solution: a custom-made polymer-based capacitive micromachined ultrasonic transducer array (polyCMUT). polyCMUTs are tiny drums with two electrodes. One on the bottom and the other suspended over a cavity sandwiched between two layers of polymers. By applying a DC-bias an electrical field is created and the membrane is set under tension. If then an AC voltage is applied, the strength of the electric field decreases, allowing the membrane to snap back into its original position. If done at the resonance frequency of the membrane, a strong ultrasonic signal is created. To receive this signal the polyCMUT is charged with a DC-bias, allowing it to receive the echo of the transmitted signal by measuring the changing capacitance. Not only is the polymer robust and inexpensive to fabricate, the general architecture of CMUTs also allows a design where the first mode of resonance is the actual mode the CMUT is operating in. By designing for a resonance frequency over 5 MHz all noise from the initial welding process is ignored, leading to a working pulse echo imaging system. The array is then mounted onto a PEEK block attached to the compactor unit of the welding end-effector. This publication is intended to present initial results, the design process of the custom array and the tests leading there.

The development of low-cost spin coater with wireless IoT control for thin film deposition

A low-cost spin coater with a wireless remote system that can deposit thin films of uniform thickness and quality at a significantly lower cost than traditional methods. The system consists of three main parts, a motorized spindle, a spin-coating head, and a control system connected to the network. The mechanical design on the mechanical part, spin coater system design with ESP32, and implementation of wireless control through visual basic. The network-enabled control system allows for real-time monitoring and adjustment of the deposition process, which can improve efficiency and reproducibility. This low-cost spin coating system represents a promising solution for organizations seeking to access thin film deposition technology at a fraction of the cost of traditional systems. By integrating wireless IoT control into low-cost spin coaters, the impact of this technology on coating uniformity will provide valuable insights for future advancements in this field.

Personalized feature extraction for manufacturing process signature characterization and anomaly detection

Manufacturing process signatures reflect the process stability and anomalies that potentially lead to detrimental effects on the manufactured outcomes. Sensing technologies, especially in-situ image sensors, are widely used to capture process signatures for diagnostics and prognostics. This imaging data is crucial evidence for process signature characterization and monitoring. A critical aspect of process signature analysis is identifying the unique patterns in an image that differ from the generic behavior of the manufacturing process in order to detect anomalies. It is equivalent to separating the “unique features” and process-wise (or phase-wise) “shared features” from the same image and recognizing the transient anomaly, i.e., recognizing the outlier “unique features”. In state-of-the-art literature, image-based process signature analysis relies on conventional feature extraction procedures, which limit the “view” of information to each image and cannot decouple the shared and unique features. Consequently, the features extracted are less interpretable, and the anomaly detection method cannot distinguish the abnormality in the current process signature from the process-wise evolution. Targeting this limitation, this study proposes personalized feature extraction (PFE) to decouple process-wise shared features and transient unique features from a sensor image and further develops process signature characterization and anomaly detection strategies. The PFE algorithm is designed for heterogeneous data with shared features. Supervised and unsupervised anomaly detection strategies are developed upon PFE features to remove the shared features from a process signature and examine the unique features for abnormality. The proposed method is demonstrated on two datasets (i) selected data from the 2018 AM Benchmark Test Series from the National Institute of Standards and Technology (NIST), and (ii) thermal measurements in additive manufacturing of a thin-walled structure of Ti–6Al–4V. The results highlight the power of personalized modeling in extracting features from manufacturing imaging data.

A Systematic Review of Global Marine Mammal Rehabilitation and Refloating, 2000–2023

Rehabilitation of marine mammals is performed throughout the world, yet the impact of rehabilitation on individuals and populations is questionable given the lack of robust information on the rehabilitation process and post-release monitoring in most cases. The goals of this study were to perform a systematic review of existing literature on marine mammal rehabilitation between 2000–2023 to determine the current state of rehabilitation and to evaluate the rehabilitation and conservation factors that could be used to build a framework to assess the health of rehabilitation programs worldwide. A total of 418 publications encompassing 52 species of marine mammal were included in the systematic review. Rehabilitation efforts focused mostly on pinnipeds, and North America was the most represented region. Data are scarce for both short- and long-term post-release survival across species. Reports about species threatened with extinction were significantly more likely to include information about conservation parameters than those species of least concern. A meta-analysis demonstrated a fairly strong fit of the dimensions of the proposed framework for a rehabilitation health index. Further refinement would strengthen the utility of this tool for both rehabilitation and conservation programs.

Метагипермнезия: эффекты вознаграждения и типа задач (Изображение vs. Слово)

<p>People sometimes make predictions about life events. These often include monitoring and control processes. Metacognition, which is subfield of cognition, has these processes. Metacognition research includes memory, attention, and comprehension studies. Hypermnesia is one of the topics relating to memory, simply meaning level of recall with regard to a repeated task. Thus, metahypermnesia is thoughts and judgements about hypermnesia. The aim of the present study was to find out whether people would overestimate or underestimate their performance on a given hypermnesia task that used images and words. Additionally, the effect of monetary incentives on the image and word test were investigated. Thus, it was hypothesised that people would overestimate their performance compared to their actual performance in the tasks. 134 participants were recruited for the study. The results illustrated that participants’ performance affected by monetary incentives and recall type. Importantly, giving incentives enhanced participants’ performance on image test but not word test. Participants also overestimated their performance on both the image and word tests, regardless of whether they received monetary incentives. However, participants overestimated their performance when they were incentivized more than they were not.</p>

Construction framework process: a qualitative investigation of behaviour change drivers and project outcomes

Purpose Construction framework agreements are identified by the UK Government’s Construction Strategy 2025 as an integrated procurement path to improve construction industry efficiency. However, criticisms from the industry have arisen from the lack of transparency and incorrect application of such frameworks. This paper aims to examine the client and supplier relationships within a framework agreement to discover what behaviour change drivers should be applied in the framework process to achieve the desired project outcomes. Design/methodology/approach A triangulation approach was adopted. An initial literature review on organisational behaviour and job performance theories was conducted followed by a qualitative expert review survey of client construction managers to confirm the project outcomes desired by public-sector clients and the associated behaviour change drivers. This was followed by a qualitative multiple-case study investigation of eight typical framework projects to form a commonality of views to explain the impact of behaviour change drivers on project outcomes. Findings Results from both qualitative studies demonstrated that improvements in project outcomes of time, cost, quality, sustainability and closer relationships can be driven by two sets of behaviour drivers: client organisational behaviour change drivers (setting up incentive and risk-sharing procurement approach, effective communication through development of stronger relationships and performance monitoring using contract key performance indicators) and supplier contextual behaviour change drivers (motivating conscientious behaviour and attitude towards self-improvement and innovations, supporting shared culture of providing services aligning with the client needs, promoting learning and development amongst all suppliers and most importantly providing trust and collaboration to the client). Research limitations/implications The investigation was based on an expert review with eight multiple-case studies conducted within the geographical area of England. Further research should be conducted nationwide so that the findings can become more robust and benefit the entire public sector. Practical implications It is suggested that framework managers should apply the supplier contextual performance drivers as selection criteria in the procurement process, whilst the client organisational performance drivers should be applied in the whole procurement and construction monitoring process to drive project outcomes aligned with the government construction policy objectives. Originality/value This research demonstrates that the project behaviour of clients and suppliers can be shifted within the construction framework environment to yield the desired project outcomes. This can be achieved by applying the client organisational behaviour drivers and the supplier contextual behaviour change drivers in tandem to optimise the framework process.

Optimal Joint Defense and Monitoring for Networks Security under Uncertainty: A POMDP-Based Approach

The increasing interconnectivity in our infrastructure poses a significant security challenge, with external threats having the potential to penetrate and propagate throughout the network. Bayesian attack graphs have proven to be effective in capturing the propagation of attacks in complex interconnected networks. However, most existing security approaches fail to systematically account for the limitation of resources and uncertainty arising from the complexity of attacks and possible undetected compromises. To address these challenges, this paper proposes a partially observable Markov decision process (POMDP) model for network security under uncertainty. The POMDP model accounts for uncertainty in monitoring and defense processes, as well as the probabilistic attack propagation. This paper develops two security policies based on the optimal stationary defense policy for the underlying POMDP state process (i.e., a network with known compromises): the estimation-based policy that performs the defense actions corresponding to the optimal minimum mean square error state estimation and the distribution-based policy that utilizes the posterior distribution of network compromises to make defense decisions. Optimal monitoring policies are designed to specifically support each of the defense policies, allowing dynamic allocation of monitoring resources to capture network vulnerabilities/compromises. The performance of the proposed policies is examined in terms of robustness, accuracy, and uncertainty using various numerical experiments.

Extending the Digital Twin Ecosystem: A real-time Digital Twin of a LinuxCNC-controlled subtractive manufacturing machine

This research paper describes the design and implementation of a real-time data-driven Digital Twin Ecosystem (DTE) for a LinuxCNC-controlled subtractive manufacturing computer numerical control (CNC) machine. The DTE architecture integrates real-time data acquisition, processing, and synchronization with the physical CNC machine. Our architecture adopts and extends the methodology presented in previous work and utilizes two components, namely the data Acquisition, Processing, and Distribution Component (APDC), and the Virtual Representation Component (VRC), which is developed using the Unity real-time development platform. The DTE accurately models the CNC machine’s behavior under various operating conditions. The DTE is verified and validated through testing and experimentation, particularly focusing on response time, sequencing accuracy, and seamless integration. The DTE offers two modes of operation: the online mode allows for real-time process replication, ideal for process monitoring and remote management, while the offline mode supports asynchronous G-code simulation, enabling what-if analysis and exploration of process variables. In this work, we utilize the offline mode for predictive collision detection. Using the DTE, stakeholders could make informed decisions, optimize machining processes, and ensure cost-effective operations. This research contributes to the advancement of Digital Twin technology, particularly in CNC machining, fostering innovation in the manufacturing sector and promoting the development of practical, efficient, and reliable cyber–physical industrial systems, enhancing operational capabilities and productivity.

Dynamic Sensor Selection for Efficient Monitoring of Coupled Multidisciplinary Systems

Abstract Coupled multidisciplinary systems involve different disciplines/subsystems with feedback-coupled interactions, illustrating the complex interdependencies inherent in real-world engineering systems. Effective monitoring of a coupled multidisciplinary system is crucial for real-time assessment of the interactions between various disciplines within the system. This monitoring provides the data necessary for detecting and addressing issues in a timely manner and facilitates adaptive decision-making for taking reliable design or control actions. However, processing and analyzing data in real time is computationally intensive, and limited resources, such as computational power, sensor capabilities, and budget, may constrain the extent to which a system can be monitored comprehensively. To address this, this paper develops a particle-based approach that dynamically selects a subset of sensors that provides the highest information about the state of the system in real time. The proposed approach first predicts the amount of uncertainty in the estimation of the state of the system given noisy measurements from different subsets of available sensors. Then, it selects the sensors that reduce this uncertainty the most, enhancing the precision and efficiency of the monitoring process. The efficacy of the proposed framework is demonstrated via two coupled multidisciplinary systems in the numerical experiments.

ANALISIS KUALITAS LAYANAN PENGUSAHA PENGURUSAN JASA KEPABEANAN (PPJK) PADA PERUSAHAAN FREIGHT FORWARDING MENGGUNAKAN SERVICE QUALITY (SERVQUAL)

The increasing trend in import-export activities demands optimal service, especially in customs clearance. Exporters and importers who cannot manage these processes can delegate authority to Customs Clearance and Forwarding Service Providers (PPJK). Company data reveals that the number of Sea Freight service users as exporters from January to December 2022 is higher than the number of service users employing PPJK. The research aims to analyze the quality of PPJK services in Freight Forwarding companies to provide services aligned with customer expectations. This study employs questionnaires and the Service Quality (Servqual) method to assess customer satisfaction, Importance Performance Analysis (IPA) to gauge company service quality, and Potential Gain in Customer Value (PGCV) to prioritize improvements. The Servqual results indicate that 33 attributes show negative gap values, indicating customer dissatisfaction, while 5 attributes exhibit positive ones. The IPA matrix results depict 7 attributes in quadrant I, representing low service quality performance but high customer expectations. The PGCV analysis prioritizes improvement in attributes U3, T1, U1, RS3, A4, CC1, and S3. The recommended improvements for the company include organizing customer gatherings, developing a web-based application, enhancing communication, providing problem-solving assistance, increasing staffing, delivering explanations in simple language, and strengthening monitoring processes.

A quality‐related distributed process monitoring framework for large‐scale manufacturing processes with multirate sampling measurements

SummaryQuality‐related process monitoring has become a research hot‐spot in the field of industrial control because it is essential to ensure the process safety and product quality. A great number of data driven quality‐related process monitoring methods have been developed for large‐scale manufacturing processes, and most of them are developed based on homogeneous sampling measurement. Therefore, it is necessary to develop quality‐related monitoring methods for large‐scale processes with multirate sampling measurements. In this paper, a new quality‐related distributed monitoring framework for large‐scale manufacturing processes with multirate sampling measurements is proposed. First, a new subsystem decomposition method for multirate sampling processes combining prior knowledge and mutual information is proposed by introducing mathematic expectations. Second, local monitoring model is designed for each subsystem. Multirate partial least squares regression is adopted for modeling among the process and quality variables. The monitoring metrics of the isolation‐based anomaly detection using nearest‐neighbor ensembles are built in prediction space and process space, respectively. Finally, Bayesian inference is introduced to obtain statistical indicators for the plant‐wide processes. The validity of the proposed framework is verified in Tennessee Eastman process and a real hot strip mill process. The results show that the proposed method has favorable effectiveness and significant performance gains compared with state‐of‐the‐art methods.

A Novel Image Processing Technique for Weighted Particle Size Distribution Assessment

Objective The objective of the study was to create a reliable method that could be used to evaluate the particle size distribution of samples and pre-mixes in real-world situations, particularly those consisting of typical formulation blends. The goal was to use this method to assess the uniformity of the samples and ensure that they met the required quality standards. The researchers aimed to create a method that could be easily incorporated into the manufacturing process, providing a practical and efficient solution. Significance of review This study demonstrates the use of ImageJ software to analyze the particle size distribution (PSD) of powders. The technique produces qualitative data from microscopy images and quantitative data from analysis of parameters including average diameter, D 10, D 50 , D 90, and standard deviation. The method was tested with various treatments, showing differentiating outcomes in all cases. Key findings The alternate technique provides a rapid and cost-effective method for PSD analysis, surpassing the limitations of sieve analysis. Extensive testing of the method, using a variety of sample types, including typical formulation blends, was performed. The results suggest that the method can effectively assess the morphology of changing materials during batch manufacturing and characterize uniformity in blends. Conclusions The methodology has the capability to identify attributes related to PSD that are typically required to be monitored during manufacturing. The technique allows for accurate and reliable quantification of the attributes through image capture technology. The technique has future potential and has important implications for material science, powder rheology, pharmaceutical formulation development, and continual process monitoring.

Introducing the progress monitoring tools of the EU Biodiversity Strategy for 2030

The European Union’s Biodiversity Strategy for 2030 (EU BDS) represents a pivotal step forward in the commitment to protect and restore biodiversity, not only within Europe but also at a global scale. In order to reinforce its implementation, an innovative biodiversity knowledge governance has been established. It includes, among other measures, a progress monitoring system that fosters transparency and can inform corrective action to be taken when progress is being reviewed. In this context, we introduce the official, publicly available, online tools at the core of the EU BDS progress monitoring system: the actions tracker and the dashboard. The actions tracker is specifically designed to track progress on the implementation of the more than a hundred actions stemming from the EU BDS, while the dashboard monitors progress across 16 targets using indicators. However, while the actions tracker is a mature tool, the work on the dashboard is still in progress, as indicators are missing for several targets. New scientific input is needed to propel policy tracking and ensure transparent and data-driven monitoring of the EU BDS targets. With this paper, we invite the scientific community to seize this opportunity to actively participate in the policy monitoring process.

NMR in Battery Anode Slurries with a V-Shaped Sensor

Inline analytics in industrial processes reduce operating costs and production rejection. Dedicated sensors enable inline process monitoring and control tailored to the application of interest. Nuclear Magnetic Resonance is a well-known analytical technique but needs adapting for low-cost, reliable and robust process monitoring. A V-shaped low-field NMR sensor was developed for inline process monitoring and allows non-destructive and non-invasive measurements of materials, for example in a pipe. In this paper, the industrial application is specifically devoted to the quality control of anode slurries in battery production. The characterization of anode slurries was performed with the sensor to determine chemical composition and detect gas inclusions. Additionally, flow properties play an important role in continuous production processes. Therefore, the in- and outflow effects were investigated with the V-shaped NMR sensor as a basis for the future determination of slurry flow fields.

Unlocking Single Particle Anisotropy in Real‐Time for Photoelectrochemistry Processes at the Nanoscale

The key to rationally and rapidly designing high‐performance materials is the monitoring and comprehension of dynamic processes within individual particles in real‐time, particularly to gain insight into the anisotropy of nanoparticles. The intrinsic property of nanoparticles typically varies from one crystal facet to the next under realistic working conditions. Here, we introduce the operando collision electrochemistry to resolve the single silver nanoprisms (Ag NPs) anisotropy in photoelectrochemistry. We directly identify the effect of anisotropy on the plasmonic‐assisted electrochemistry at the single NP/electrolyte interface. The statistical collision frequency shows that heterogeneous diffusion coefficient among crystal facets facilitates Ag NPs to undergo direction‐dependent mass transfer toward the gold ultramicroelectrode. Subsequently, the current amplitudes of transient events indicate that anisotropy enables variations in dynamic interfacial electron transfer behaviors during photothermal processes. The results presented here demonstrate that the measurement precision of collision electrochemistry can be extended to the sub‐nanoparticle level, highlighting the potential for high‐throughput material screening with comprehensive kinetics information at the nanoscale.

Research on the processing and interpretation methods of distributed fiber optic vibration signal logging injection profiles

The oil and gas industry currently faces the dual challenges of improving extraction efficiency and reducing environmental impacts. Traditional well monitoring technologies are increasingly unable to meet the demands of modern oil and gas extraction due to technological limitations and environmental concerns. Distributed Acoustic Sensing (DAS) technology, which utilizes optical fibers as sensing elements, enables real-time and accurate monitoring of the CO2 injection process in wells. However, DAS technology encounters issues such as weak amplitude signals and noise interference in practical applications. In response, a comprehensive method for processing and interpreting DAS logging data for CO2 injection wells has been proposed. This method involves restoring amplitude signals through wavefront energy compensation and applying Gaussian filtering and wavelet threshold denoising algorithms for noise reduction. The processed results are classified using the K-Medoids clustering algorithm and the CO2 absorption volume in the injection layer is calculated using the integral area method. The accuracy and practicality of this method have been validated through comparative analysis with pulse neutron oxygen activation logging results. This approach aims to address the challenges faced by DAS technology in the oil and gas industry, thereby providing technical support for the efficient, environmentally friendly, and intelligent management of oil and gas fields.

Intricacies of Mass Transport during Electrocatalysis: A Journey through Iron Porphyrin-Catalyzed Oxygen Reduction.

Electrochemical steps are increasingly attractive for green chemistry. Understanding reactions at the electrode-solution interface, governed by kinetics and mass transport, is crucial. Traditional insights into these mechanisms are limited, but our study bridges this gap through an integrated approach combining voltammetry, electrochemical impedance spectroscopy, and electrospray ionization mass spectrometry. This technique offers real-time monitoring of the chemical processes at the electrode-solution interface, tracking changes in intermediates and products during reactions. Applied to the electrochemical reduction of oxygen catalyzed by the iron(II) tetraphenyl porphyrin complex, it successfully reveals various reaction intermediates and degradation pathways under different kinetic regimes. Our findings illuminate complex electrocatalytic processes and propose new ways for studying reactions in alternating current and voltage-pulse electrosynthesis. This advancement enhances our capacity to optimize electrochemical reactions for more sustainable chemical processes.

Unlocking Single Particle Anisotropy in Real-Time for Photoelectrochemistry Processes at the Nanoscale.

The key to rationally and rapidly designing high-performance materials is the monitoring and comprehension of dynamic processes within individual particles in real-time, particularly to gain insight into the anisotropy of nanoparticles. The intrinsic property of nanoparticles typically varies from one crystal facet to the next under realistic working conditions. Here, we introduce the operando collision electrochemistry to resolve the single silver nanoprisms (Ag NPs) anisotropy in photoelectrochemistry. We directly identify the effect of anisotropy on the plasmonic-assisted electrochemistry at the single NP/electrolyte interface. The statistical collision frequency shows that heterogeneous diffusion coefficient among crystal facets facilitates Ag NPs to undergo direction-dependent mass transfer toward the gold ultramicroelectrode. Subsequently, the current amplitudes of transient events indicate that anisotropy enables variations in dynamic interfacial electron transfer behaviors during photothermal processes. The results presented here demonstrate that the measurement precision of collision electrochemistry can be extended to the sub-nanoparticle level, highlighting the potential for high-throughput material screening with comprehensive kinetics information at the nanoscale.

Correlation of C-Reactive Proteins With Chronic Periodontitis. A Prospective Study

Abstract Context: The C-reactive protein (CRP) is an acute-phase reactant protein that is produced in response to various inflammatory and noxious stimuli that include exposure to heat, traumatic conditions, varieties of infectious processes, and conditions of hypoxia. Estimation of CRP levels in serum samples helps in providing important information related to the diagnosis, monitoring, and treatment of inflammatory processes and various associated diseases. Materials and Methods: This prospective longitudinal study was designed to evaluate serum CRPs in subjects who were selected for study participation after fitting into inclusion criteria, which were as follows: a) study participants who were clinically diagnosed with chronic generalized periodontitis along with periodontal pocket depths of ≥5 mm and also radiographic alveolar bone loss; b) subjects within the age range of 35 to 55 years; and c) no smoking habit or use of tobacco in any other form. Results: Patients were assessed for clinical parameters such as gingival index, sulcular bleeding index, mean depth of periodontal pocket, and loss of clinical attachment. Blood was collected at required visits, and CRP was evaluated. Study evaluation periods were baseline (day 0), day 14 (2nd appointment), and day 45 (3rd appointment). Results: Statistically significant reductions in both clinical and serum CRP levels were obtained at subsequent appointments. Conclusion: This study has demonstrated and supported the evidence that serum CRPs act as a biomarker in determining the prognosis of chronic periodontitis patients after nonsurgical periodontal treatment.

A redox homeostasis disruptor based on a biodegradable nanoplatform for ultrasound (US) imaging-guided high-performance ferroptosis therapy of tumors

ABSTRACT The synergistic disruption of intracellular redox homeostasis through the combination of ferroptosis/gas therapy shows promise in enhancing the antitumor efficacy. However, the development of an optimal delivery system encounters significant challenges, including effective storage, precise delivery, and controlled release of therapeutic gas. In this study, we propose the utilization of a redox homeostasis disruptor that is selectively activated by the tumor microenvironment (TME), in conjunction with our newly developed nanoplatforms (MC@HMOS@Au@RGD), for highly efficient ferroptosis therapy of tumors. The TME-triggered degradation of HMOS initiates the release of MC and AuNPs from the MC@HMOS@Au@RGD nanoplatform. The released MC subsequently reacts with endogenous hydrogen peroxide (H2O2) and H+ to enable the on-demand release of CO gas, leading to mitochondrial damage. Simultaneously, the released AuNPs exhibit GOx-like activity, catalyzing glucose to generate gluconic acid and H2O2. This process not only promotes the decomposition of MnCO to enhance CO production but also enhances the Fenton-like reaction between Mn2+ and H2O2, generating ROS through the modulation of the H+ and H2O2-enriched TME. Moreover, the generation of CO bubbles enables the monitoring of the ferroptosis treatment process through ultrasound (US) imaging. The efficacy of our prepared MC@HMOS@Au@RGD disruptors in ferroptosis therapy is validated through both in vitro and in vivo experiments.