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Positron emission tomography (PET) constitutes a functional imaging technique that is harnessed to probe biological processes in vivo. PET imaging has been used to diagnose and monitor the progression of diseases, as well as to facilitate drug development efforts at both preclinical and clinical stages. The wide applications and rapid development of PET have ultimately led to an increasing demand for new methods in radiochemistry, with the aim to expand the scope of synthons amenable for radiolabeling. In this work, we provide an overview of commonly used chemical transformations for the syntheses of PET tracers in all aspects of radiochemistry, thereby highlighting recent breakthrough discoveries and contemporary challenges in the field. We discuss the use of biologicals for PET imaging and highlight general examples of successful probe discoveries for molecular imaging with PET – with a particular focus on translational and scalable radiochemistry concepts that have been entered to clinical use. Positron emission tomography is widely used to diagnose and monitor different disease states and interest in the technique has led to the demand for the development of new method for radiolabelling. Here the authors review the recent progress in the development of new PET probes.

An accurate prediction of the machining tool condition during the cutting process is crucial for enhancing the tool life, improving the production quality and productivity, optimizing the labor and maintenance costs, and reducing workplace accidents. Currently, tool condition monitoring is usually based on machine learning algorithms, especially deep learning algorithms, to establish the relationship between sensor signals and tool wear. However, deep mining of feature and fusion information of multi-sensor signals, which are strongly related to the tool wear, is a critical challenge. To address this issue, in this study, an integrated prediction scheme is proposed based on deep learning algorithms. The scheme first extracts the local features of a single sequence and a multi-dimensional sequence from DenseNet incorporating a heterogeneous asymmetric convolution kernel. To obtain more perceptual historical data, a “dilation” scheme is used to extract features from a single sequence, and one-dimensional dilated convolution kernels with different dilation rates are utilized to obtain the differential features. At the same time, asymmetric one-dimensional and two-dimensional convolution kernels are employed to extract the features of the multi-dimensional signal. Ultimately, all the features are fused. Then, the time-series features hidden in the sequence are extracted by establishing a depth-gated recurrent unit. Finally, the extracted in-depth features are fed to the deep fully connected layer to achieve the mapping between features and tool wear values through linear regression. The results indicate that the average errors of the proposed model are less than 8%, and this model outperforms the other tool wear prediction models in terms of both accuracy and generalization.

This paper presents an improved inverse identification method for Johnson-Cook model constants (J-C constants) using force and temperature data. Nowadays, J-C constants are identified by either experimental approaches with the complex and costly system, numerical approaches with high computational cost, or analytical approaches with available material properties. The previous model is developed based on a modified chip formation model and an exhaustive search method using temperature and force measurements. The current model is improved by replacing the exhaustive search method with an iterative gradient search method based on the Kalman filter algorithm. The modified chip formation model is used to predict machining forces. The iterative gradient search method is used to determine the J-C constants when the difference between predicted forces and experimental forces reached an acceptable low value. AISI 1045 steel and Al6082-T6 aluminum are chosen to test the proposed methodology. The determined J-C constants are validated by comparing to the documented values in the literature, which were obtained from Split-Hopkinson Pressure Bar tests and validated in published works. Good agreements are observed between identified J-C constants and documented values with an improved computational efficiency. The cutting temperatures are used as inputs in the modified chip formation model. Therefore, the workpiece material properties are not required to predict temperatures and forces, and thus are not required for determining J-C constants. Considering the modified chip formation model using temperatures as inputs, and the effective iterative gradient search method, this method has advantages of less mathematical complexity and high computational efficiency.

Elevated temperature in the machining process is detrimental to the cutting tool due to a thermal softening effect. The increased material diffusion deteriorates the quality of the machined part. Experimental techniques and finite element method-based numerical models in temperature investigation are limited by the restricted accessibility and high computational cost respectively. Physic-based analytical models are developed to overcome those issues. This study investigated three analytical models, namely a modified chip formation model, Komanduri-Hou two heat sources model, and Ning-Liang material flow stress model, in the prediction of machining temperatures in orthogonal cutting. The evaluation and comparison between three models aim to promote the use of the analytical models in real applications, in which real-time prediction is highly appreciated. Temperatures in machining AISI 1045 steel were predicted under various cutting conditions. Acceptable agreements were observed between predictions and documented values in the literature. In the modified chip formation model, machining temperatures and forces were solved iteratively with complex mathematical equations, which reduced computational efficiency, and thus prevented a real-time temperature prediction. The heat partition factors were empirically determined, which resulted in unoptimized prediction accuracy. In Komanduri-Hou model, the input lengths of two shear zones and shear angle cannot be easily obtained from experiments due to the restricted accessibility. With the benefits of high prediction accuracy, high computational efficiency, and low experimental complexity of model inputs, Ning-Liang model was favored in the real-time prediction of machining temperatures.

Grinding has become one of the most efficient precision machining methods to treat with undesired machining defects and improve the surface integrity for the hard and brittle engineering ceramics. However, it is inevitable to cause micro-damages and related transformation of microscopic features, which will eventually affect the grinding quality. This paper is devoted to investigate the high-speed grinding microscopic features of silicon carbide ceramics to reveal the application of high-speed grinding technique in precision machining of ceramics. A comparative study of high-speed and conventional speed grinding of silicon carbide ceramics is discussed in terms of phase transformation, residual stresses, micro-damages, grinding chips, and surface topography. The results show that the high-speed grinding (HSG) process could help substantially improve the workpiece integrity in terms of better surface finish, smaller damages, and controlled residual stresses with a higher material removal rate. Moreover, it has also been proved that a polytypic phase transformation could be induced in HSG process.

The use of photothermal agents (PTAs) in cancer photothermal therapy (PTT) has shown promising results in clinical studies. The rapid degradation of PTAs may address safety concerns but usually limits the photothermal stability required for efficacious treatment. Conversely, PTAs with high photothermal stability usually degrade slowly. The solutions that address the balance between the high photothermal stability and rapid degradation of PTAs are rare. Here, we report that the inherent Cu 2+ -capturing ability of black phosphorus (BP) can accelerate the degradation of BP, while also enhancing photothermal stability. The incorporation of Cu 2+ into BP@Cu nanostructures further enables chemodynamic therapy (CDT)-enhanced PTT. Moreover, by employing 64 Cu 2+ , positron emission tomography (PET) imaging can be achieved for in vivo real-time and quantitative tracking. Therefore, our study not only introduces an “ideal” PTA that bypasses the limitations of PTAs, but also provides the proof-of-concept application of BP-based materials in PET-guided, CDT-enhanced combination cancer therapy. A balance between high stability and rapid degradation is required for effective photothermal anti-cancer agents. Here, the authors use Cu 2+ to accelerate the degradation of black phosphorus nanosheets while enhancing its photothermal ability and apply this material for PET-guided, CDT-enhanced combination cancer therapy in mice.

This paper presents an original method to inversely identify the Johnson–Cook model constants (J-C constants) of ultra-fine-grained titanium (UFG Ti) based on a chip formation model and an iterative gradient search method using Kalman filter algorithm. UFG Ti is increasingly finding usefulness in lightweight engineering applications and medical implant filed because of its sufficient mechanical strength, high manufacturability, and high biocompatibility. Johnson–Cook model is one of the constitutive models widely used in analytical modeling of machining force, temperature, and residual stress because it is effective, simple, and easy to use. Currently, the J-C constants of UFG Ti are unavailable and yet an effective identification methodology based upon machining data is not readily available. In this work, multiple cutting tests were conducted under different cutting conditions, in which machining forces were experimentally measured using a piezoelectric dynamometer. The machining forces were also predicted using the chip formation model with inputs of cutting conditions, workpiece material properties, and a set of given model constants. An iterative gradient search method was enforced to find the J-C constants when the difference between predicted forces and experimental forces reached an acceptable low value. To validate the identified J-C constants, machining forces were predicted using the identified J-C constants under different cutting conditions and then compared to the corresponding experimental forces. Close agreements were observed between predicted forces and experimental forces. Considering the simple orthogonal cutting tests, reliable and easily measurable machining forces, and efficient iterative gradient search method, the proposed method has less experimental complexity and high computational efficiency.

Elevated temperature in the machining process is detrimental to cutting tools—a result of the effect of thermal softening and material diffusion. Material diffusion also deteriorates the quality of the machined part. Measuring or predicting machining temperatures is important for the optimization of the machining process, but experimental temperature measurement is difficult and inconvenient because of the complex contact phenomena between tools and workpieces, and because of restricted accessibility during the machining process. This paper presents an original analytical model for fast prediction of machining temperatures at two deformation zones in orthogonal cutting, namely the primary shear zone and the tool–chip interface. Temperatures were predicted based on a correlation between force and temperature using the mechanics of the cutting process and material constitutive relation. Minimization of the differences between calculated material flow stresses using a mechanics model and a constitutive model yielded an estimate of machining temperatures. Experimental forces, cutting condition parameters, and constitutive model constants were inputs, while machining forces were easily measurable by a piezoelectric dynamometer. Machining temperatures of AISI 1045 steel were predicted under various cutting conditions to demonstrate the predictive capability of each presented model. Close agreements were observed by verifying them against documented values in the literature. The influence of model inputs and computational efficiency were further investigated. The presented model has high computational efficiency that allows real-time prediction and low experimental complexity, considering the easily measurable input variables.

In this paper, an original approach was presented to identify the Johnson-Cook material constants (J-C constants). The Johnson-Cook model is one of the simplest models to describe the material behavior in machining. The five J-C constants are related to strain hardening effect, stain rate hardening effect, and thermal softening effect. The approach was developed based on a chip formation model in orthogonal cutting and Johnson-Cook model. This paper used process variables including the temperature at the primary shear zone, the temperature of the chip, cutting conditions, and the estimations of the material constants as inputs. The machining forces were calculated with the chip formation model using all estimated material constants that were being changed within intervals of 50% of the references. The five J-C constants were determined by searching minimum differences between the calculated forces and measured forces under each cutting condition. The workpiece material properties such as thermal conductivity, specific heat, and melting temperature were not required because of the measurements of the temperatures. The proposed approach has advantages of low experimental cost, low time cost, less experimental complexity, and less mathematical complexity. The determined J-C constants of AISI 1045 steel and 42CrMo4 alloy were compared to their J-C constants from Split-Hopkinson Pressure Bar (SHPB) tests respectively. Close agreements were found for both materials.

Selective laser melting (SLM) is an emerging additive manufacturing (AM) technology for metals. Intricate three-dimensional parts can be generated from the powder bed by selectively melting the desired location of the powders. The process is repeated for each layer until the part is built. The necessary heat is provided by a laser. Temperature magnitude and history during SLM directly determine the molten pool dimensions, thermal stress, residual stress, balling effect, and dimensional accuracy. Laser-matter interaction is a crucial physical phenomenon in the SLM process. In this paper, five different heat source models are introduced to predict the three-dimensional temperature field analytically. These models are known as steady state moving point heat source, transient moving point heat source, semi-elliptical moving heat source, double elliptical moving heat source, and uniform moving heat source. The analytical temperature model for all of the heat source models is solved using three-dimensional differential equations of heat conduction with different approaches. The steady state and transient moving heat source are solved using a separation of variables approach. However, the rest of the models are solved by employing Green’s functions. Due to the high temperature in the presence of the laser, the temperature gradient is usually high which has a substantial impact on thermal material properties. Consequently, the temperature field is predicted by considering the temperature sensitivity thermal material properties. Moreover, due to the repeated heating and cooling, the part usually undergoes several melting and solidification cycles, and this physical phenomenon is considered by modifying the heat capacity using latent heat of melting. Furthermore, the multi-layer aspect of the metal AM process is considered by incorporating the temperature history from the previous layer since the interaction of the layers have an impact on heat transfer mechanisms. The proposed temperature field models based on different heat source approaches are validated using experimental measurement of melt pool geometry from independent experimentations. A detailed explanation of the comparison of models is also provided. Moreover, the effect of process parameters on the balling effect is also discussed.