Engineering Applications Research Articles

Study on preparation and properties of GA/h-BN modified road petroleum asphalt based on ball milling method

ABSTRACT Graphene (GA), a two-dimensional nanomaterial, is noted for its exceptional mechanical, thermal and electrical properties. The integration of GA with road materials can potentially develop high-performance composite road materials. However, its application in road engineering is limited by complex preparation processes and high costs. This paper presents a GA/h-BN composite material developed through mechanical stripping using flake graphite and hexagonal boron nitride (h-BN). A mathematical model was created employing uniform design methods and optimised through numerical analysis to identify the optimal process parameters. Microscopic techniques, including X-ray diffraction and atomic force microscopy, confirmed that the GA/h-BN product consists of approximately three layers, forming a ‘graphene-hexagonal boron nitride-graphene’ nanolayer structure. The composite was then used to modify matrix asphalt, with performance tests determining the optimal dosage. The results indicated that adding 0.3% GA/h-BN significantly improved the asphalt's high- and low-temperature performance, fatigue resistance and cracking resistance. Moreover, the evaluation of the physical properties of matrix asphalt modified solely with h-BN revealed that, despite h-BN comprising 70% of the mass fraction in the GA/h-BN modifier, it exerted a minimal impact on the performance of the asphalt. This study introduces a novel source of raw materials for the application of GA in road engineering.

Enhancing mechanism of mechanical properties of lightweight and high-strength concrete prepared with autoclaved silicate lightweight aggregate

Lightweight aggregate concrete (LAC) possesses the merits of low density, excellent thermal insulation performance and outstanding seismic performance. However, the practical application in structural engineering is limited because its mechanical properties are weaker than those of ordinary aggregate concrete. It was found that the compressive strength of high-strength concrete prepared by autoclaved silicate lightweight aggregate (cloud concrete stone) was higher than that of ordinary aggregate high-strength concrete, and it showed significant lightweight and high-strength characteristics. To reveal the enhancing mechanism of the mechanical properties of high-strength concrete prepared by cloud concrete stone (CCS), this research examines the internal curing effect of CCS and the mechanical performances of the interfacial transition zones (ITZ). The influences of the elastic modulus of CCS, the shape of aggregate and fracture patterns on the mechanical properties of concrete are simulated via the discrete element method. The findings suggest that the internal curing effect of CCS delays the onset of capillary stress and mitigates autogenous shrinkage. The homogeneousness, elastic modulus, and fracture toughness of ITZ around CCS aggregate are superior than that ITZ around ordinary gravel in high-strength concrete. Furthermore, the small difference in elastic modulus between CCS aggregate and cement matrix is advantageous for preventing microcracks during compression tests. The round shape of the aggregate also mitigates the development of microcracks caused by the stress concentration effect. These three reasons contribute to the enhancement of mechanical properties in lightweight and high-strength concrete. The innovation of this research is that the intrinsic relationship between CCS aggregate and concrete’s mechanical properties is thoroughly revealed from the aspects of the characteristics of the interface transition zone, the morphology of the aggregate and the elastic modulus of aggregate. That provides a theoretical basis for the application of light aggregate in high-strength structural concrete.

High-modulus engineered cementitious composites: Design mechanism and performance characterization

Engineered cementitious composites features with high tensile performance while relatively weak compressive stiffness. This study endeavors to address the inherent trade-off between tensile properties and elastic modulus in conventional Engineered Cementitious Composites (ECC). Utilizing the distinctive characteristics of iron ore aggregates, known for their relatively stiff nature, smooth surface and rounded shape, an Iron Sand-based ECC (IS-ECC) emphasizing both high elastic modulus and ductility is formulated. Guided by micromechanical design theory and multiscale homogenization model, this study systematically explores the impacts of aggregate types, water-to-binder (w/b) ratios, sand-to-binder (s/b) ratios, and sand particle sizes on ECC properties. Compared with Quartz Sand-based ECC (QS-ECC), IS-ECC exhibits notably enhanced matrix fluidity and elastic modulus, reduced matrix toughness, and more robust strain-hardening behavior. The proposed three-level multiscale homogenization model accurately predicts the elastic modulus of ECC and provides insights into the underlying mechanism contributing to the enhanced elastic modulus of IS-ECC. With a resulting high elastic modulus of 33.3–48.6 GPa and superior tensile properties, IS-ECC holds promise for widespread applications in structural engineering.

310-Helix stabilization and screw sense control via stereochemically configured 4-atom hydrocarbon staples

The 310-helix is a crucial secondary structure in proteins, playing an essential role in various protein–protein interactions, yet stabilizing it in biologically relevant peptides remains challenging. In this study, we investigated the potential of 4-atom hydrocarbon staples to stabilize 310-helices in peptides. Using ring-closing metathesis, we demonstrated that the staple’s configuration is critical for both the stabilization and screw sense control of 310-helices. Circular dichroism spectroscopy revealed that the Ri,i+3S(4) staple—a 4-atom cross-link with (R)-configuration at the i position, (S)-configuration at the i + 3 position, and flanked by methyl groups—strongly induces right-handed 310-helices, especially in sequences with proteinogenic l-amino acids. Furthermore, multiple staples effectively stabilized longer peptides, underscoring the versatility of this approach for applications in peptide therapeutics and biomolecular engineering.

Three-dimensional Darcy-Forchheimer modelling of MHD hybrid nanofluid over rotating stretching/shrinking surface with Hamilton-Crosser and Yamada-Ota conductivity models

Instead of single nanoparticles, the combined effects of more than one solid nanoparticle have presented wide range of real-word application in several engineering as well as biomedical areas. The present analysis brings out a combined effect of Hamilton-Crosser and Yamada-Ota thermal conductivity models for the magnetohydrodynamic flow of hybridised fluid vi a rotating stretching/shrinking surface. The hybridised fluid comprised of silver and molybdenum tetrasulphide nanoparticle in association with the effect of Joule heating enriches the flow properties. Additionally, the Darcy-Forchheimer inertial drag with the impose of thermal radiation affecting the flow as well as heat transfer properties. The proposed mathematical model equipped with physical assumptions is transmuted into dimensionless form by utilizing similarity functions. Further, the traditional numerical technique is taken care of for the solution of the transmuted model equipped with diversified factors. The important characteristic of several factors are deployed graphically affecting various flow profiles. Finally, the outstanding features explored in the proposed investigation are stated as below; the comparative analysis reveals that, the heat transport properties became advanced in case of Hamilton-Crosser model rather than the Yamada-Ota conductivity model. However, the heat transportation rate is controlled by the increasing Eckert number but thermal radiation enhances it significantly.

Influence of manufacturing parameters on bioactive glass 45S5: Structural analysis and applications in bone tissue engineering

Bioactive glass (BG-45S5) production through the melting process is affected by a wide variety of parameters. This study investigated the synthesis of BG-45S5 granules and the process variables to produce a bioactive and osteoinductive BG for bone grafting applications. The melting process was initially analyzed by varying parameters such as crucible type and pouring environment using P2O5 as phosphorus precursor. The obtained products were characterized by crystalline phases, characteristic chemical groups, particle size distribution, and chemical composition. Materials poured into graphite or steel molds resulted in particle sizes more suitable for applications in granular form. Using a platinum crucible yielded a chemical composition closer to the target when compared with another ceramic crucible. Subsequently, the melting process was evaluated to different phosphorus precursor (P2O5 or Na2HPO4) and melting duration (1 or 2 h) in a platinum crucible verifying their effects on the thermal behavior, chemical composition and structure of BG-45S5. Employing Na2HPO4 as a precursor led to higher glass transition and crystallization temperatures as compared to P2O5, enhancing glass homogeneity and structural stability. The product with better characteristics in terms of composition and structure was further characterized for bioactivity and cell culture behavior, showing a greater amount of mineralization nodules when compared to commercial hydroxyapatite. This is particularly due to its behavior as the solubility and interaction in biological environments.

Decoding burst swimming performance: a scaling perspective on time-to-fatigue.

Fatigue curves quantify fish swimming performance, providing information about the time ([Formula: see text]) fish can swim against a steady flow velocity (Uf) before fatiguing. Such curves represent a key tool for many applications in ecological engineering, especially for fish pass design and management. Despite years of research, though, our current ability to model fatigue curves still lacks theoretical foundations and relies primarily on fitting empirical data, as obtained from time-consuming and costly experiments. In the present article, we address this shortcoming by proposing a theoretical analysis that builds upon concepts of fish hydrodynamics to derive scaling laws linking statistical properties of [Formula: see text] to velocities Uf, pertaining to the so-called burst range. Theoretical arguments, in the present study, suggest that the proposed scaling laws may hold true for all fish species and sizes. A new experimental database obtained from over 800 trials and five small-sized Cypriniformes support theoretical predictions satisfactorily and calls for further experiments on more fish species and sizes to confirm their general validity.

Development of automatic CNC machine with versatile applications in art, design, and engineering

The area of computer numerical control (CNC) machines has grown fast, and their use has risen significantly in recent years. This article presents the design and development of a CNC writing machine that uses an Arduino, a motor driver, a stepper motor, and a servo motor. The machine is meant to create 2D designs and write in numerous input languages using 3-axis simultaneous interpolated operations. The suggested machine is low-cost, simple to build, and can be operated with merely G codes. The performance of the CNC writing machine was assessed by testing it on a range of solid surfaces, including paper, cardboard, and wood. The results reveal that the machine can generate high-quality text and images with great accuracy and consistency. The proposed machine's ability to write in several input languages makes it appropriate for various applications, including art, design, and engineering.

A vine-copulas based multi-sensor fusion structural damage monitoring method and its application in dam engineering

As a civil engineer that is susceptible to the mechanism of slow and continuous deterioration, the dam must detect damage according to the static data of the arranged sensors, which is an important issue in structural health monitoring. Due to the characteristics of huge solid structures, damage with a slight degree and small range usually does not cause significant changes in the monitoring data of a single sensor. Therefore, it is difficult to effectively detect local damage by traditional monitoring methods that establish operating warning values for a single sensor. Currently, there have been some approaches to fuse sensor information that consider correlations between multiple sensors as well as the overall operational behavior of the dam. However, their objectives are focused on improving the accuracy of response variable prediction models without addressing the local damage identification aspect. In this study, we apply the Vine-Copula model for the first time in a multi-sensor information fusion dam damage detection method based on static monitoring data. In this way, it improves the sensitivity of local damage identification for huge solid structures. A prediction model is fitted with the measuring data of each sensor to obtain the residual series. Then the joint probability distribution of the multidimensional residuals is acquired by constructing the Vine-Copula model. Finally, the joint cumulative distribution function value is used as an alarm limit to determine whether the structure has an abnormal damage condition. The effectiveness and accuracy of the proposed method were demonstrated in both a numerical arch dam simulating local damage and an actual rockfill dam engineering with cracks at the crest.

Impact of Polydeoxyribonucleotides on the Morphology, Viability, and Osteogenic Differentiation of Gingiva-Derived Stem Cell Spheroids

Background and Objectives: Polydeoxyribonucleotides (PDRN), composed of DNA fragments derived from salmon DNA, is widely recognized for its regenerative properties. It has been extensively used in medical applications, such as dermatology and wound healing, due to its ability to enhance cellular metabolic activity, stimulate angiogenesis, and promote tissue regeneration. In the field of dentistry, PDRN has shown potential in promoting periodontal healing and bone regeneration. This study aims to investigate the effects of PDRN on the morphology, survival, and osteogenic differentiation of gingiva-derived stem cell spheroids, with a focus on its potential applications in tissue engineering and regenerative dentistry. Materials and Methods: Gingiva-derived mesenchymal stem cells were cultured and formed into spheroids using microwells. The cells were treated with varying concentrations of PDRN (0, 25, 50, 75, and 100 μg/mL) and cultivated in osteogenic media. Cell morphology was observed over seven days using an inverted microscope, and viability was assessed with Live/Dead Kit assays and Cell Counting Kit-8. Osteogenic differentiation was evaluated by measuring alkaline phosphatase activity and calcium deposition. The expression levels of osteogenic markers RUNX2 and COL1A1 were quantified using real-time polymerase chain reaction. RNA sequencing was performed to assess the gene expression profiles related to osteogenesis. Results: The results demonstrated that PDRN treatment had no significant effect on spheroid diameter or cellular viability during the observation period. However, a PDRN concentration of 75 μg/mL significantly enhanced calcium deposition by Day 14, suggesting increased mineralization. RUNX2 and COL1A1 mRNA expression levels varied with PDRN concentration, with the highest RUNX2 expression observed at 25 μg/mL and the highest COL1A1 expression at 75 μg/mL. RNA sequencing further confirmed the upregulation of genes involved in osteogenic differentiation, with enhanced expression of RUNX2 and COL1A1 in PDRN-treated gingiva-derived stem cell spheroids. Conclusions: In summary, PDRN did not significantly affect the viability or morphology of gingiva-derived stem cell spheroids but influenced their osteogenic differentiation and mineralization in a concentration-dependent manner. These findings suggest that PDRN may play a role in promoting osteogenic processes in tissue engineering and regenerative dentistry applications, with specific effects observed at different concentrations.

Statistical analysis on the rate of heat transfer of radiative water‐based hybrid nanofluid flow over an elongating surface due to velocity slip and melting heat condition

AbstractThe performances of the heat transfer due to dissipative heat likely magnetic dissipation is a challenging field of research because of it's diversifies applications in industries and engineering. Particularly, nowadays, in biomedical field of research the implementation of dissipation along with thermal radiation is vital. Based upon the current scenario, the present investigation leading to the role of magnetic dissipation vis‐à‐vis thermal radiation on the water‐based hybrid nanofluid past an electro‐magnetized elongating surface associated with melting boundary conditions. The adaptation of the transformed rule particularly, similarity rules are useful to convert the proposed set of problems in to ordinary. Further, shooting‐based numerical technique is proposed to handle the governing equations. The interesting and novel approach in this topic is the use of robust statistical technique, that is, response surface methodology for the optimizing Nusselt number for the use of various factors. Moreover, the validation is obtained by conducting a hypothetical testing using analysis of variance (ANOVA) which conforms a best output model for appropriate response. Further, the major outcomes of the study are depicted as; the unsteadiness parameter decelerates the velocity profile of the hybrid nanofluid for the interaction. However, the increasing particle concentration favours in enhancing the heat transport phenomenon.

Predictive models for 3D inkjet material printer using automated image analysis and machine learning algorithms

Additive manufacturing (AM) is a smart manufacturing process to fabricate components with high precision, minimal post-processing, and increased component complexity in a variety of materials. This research focuses on developing automated image analysis and predictive models for a widely used 3D material inkjet printing (IJP) process. The interplay of four input process parameters, which include frequency, voltage, temperature, and meniscus vacuum, on the output metrics of the inkjet printer was evaluated using statistical measures (ANOVA). Droplet types were classified as no drop, satellite drop, and normal drop using four machine learning classifiers, including random forest, support vector classifier, k-nearest neighbor, and decision trees. Hyperparameter tuning was performed for each model for over 486 data points. Regression predictive models were developed for both ink droplet velocity and volume with three linear models (linear, lasso, and ridge regression) and four non-linear models (random forest, decision tree, support vector regression, and k-nearest neighbor). Mean squared error and the coefficient of determination, r-squared value, were used to evaluate the performance of the predictive models. For the drop type classification models, k-fold of 5 yielded the highest accuracy for the RF, KNN, and DT models of around 98%. Similarly, for the regression based predictive models RF, DT and KNN accuracy results ranged from 97 to 99%. All the machine learning models were validated with experimental data with high prediction accuracies accuracy. This research serves as a foundation for developing design guidelines for 3D material inkjet printing with applications in biosensors, flexible electronics, and regenerative tissue engineering.

Machine-learning assistant DFT study of half-metallic full-Heusler alloy N2CaNa: Structural, electronic, mechanical, and thermodynamics properties

We studied the structural, electronic, mechanical, and thermodynamic properties of N2CaNa full Heusler alloys using density functional theory (DFT). Results for the structural analysis establish structural stability with a minimum formation energy of 29.9 eV. The compound is brittle and mechanically stable, having checked out with the Pugh criteria. The B/G ratio of bulk modulus B to shear modulus G for N2CaNa is 4.766, hence the material is ductile. N2CaNa alloy is ductile in nature. The Debye model correctly predicts the low-temperature dependence of heat capacity, which is proportional to Debye’s T3 law. Just like the Einstein model, it also recovers the Dulong–Petit law at high temperatures, suggesting the thermodynamic stability of the compounds at moderate temperatures. The results demonstrate potential N2CaNa for applications in spintronics, structural engineering, and other fields requiring materials with tailored properties.

Flexible and Flame Retardant Cotton Fiber-reinforced CS/CNF/EP Composites For a Sensitive Fire Warning

Enhancing fire safety performance in resin-based composites is critical for mitigating fire hazards. Composites that provide early warnings during initial fire stages and maintain prolonged alarm capabilities under flame exposure are essential for minimizing injuries and property damage. This study employs chitosan (CS) as a cross-linking and char-forming agent, while carbon nanofiber (CNF) acts as a flame retardant and conductive component. Cotton fiber-reinforced chitosan/carbon nanofiber composites with varying epoxy and CNF contents (CS/CNF/EP) were prepared through a straightforward low-temperature curing process (40 °C, 4 h). Crosslinking polymerization between chitosan and epoxy was confirmed via Fourier Transform Infrared Spectroscopy (FTIR). The presence of the tough structure of CS and the rigid structure of EP results in a synergistic toughening and reinforcement effect in the CS/CNF/EP composites. Additionally, due to the carbon facilitating properties of CS and the enhanced conductivity of CNF, the composites exhibit a sensitive fire alarm functionality. At 35 % epoxy content, the CS/CNF/EP composites demonstrated exceptional properties, including mechanical flexibility (tensile strength of 7.09 MPa and breaking elongation of 129.05 %) and outstanding flame retardancy (LOI of 32.3 % and UL-94 V-0 rating). These composites provided a rapid alarm (∼1 s), sustaining a 263 s warning under flame exposure and over 35 min post-flame removal. These findings indicate that CS/CNF/EP composites, integrating mechanical flexibility, flame retardancy, and superior fire warning properties, present significant potential applications in fire safety engineering.

Steel slag aggregate property improvement in cold mixed asphalt mixtures through surface modification treatment

Expanding the resource utilization of solid waste steel slag (SS) is necessary to reduce the environmental impact of stockpiling and disposal. However, SS has the shortcomings of poor volume stability and poor water damage resistance, which limits its application in pavement engineering, especially in cold mixed asphalt mixtures. In this work, hexamethylene diisocyanate biuret (containing -NCO) and hydroxyl acrylic resin (containing -OH) were utilized as raw materials to prepare NCO-OH composite modifier (NOCM) by regulating NCO/OH (0.75, 1.00, 1.25, and 1.50) for SS surface modification. Surface morphology, adhesion properties, aggregate properties, and prepared mixture properties of SS before and after modification were investigated, respectively, and the curing mechanism of NOCM and action mechanism were analyzed. Results show that the cured NOCM forms a dense modified film on the SS surface, and the rough surface and large pores of SS were blocked, which effectively hindered the free oxides in SS from coming into contact with water. NOCM reduced the apparent relative density, water absorption, and wear loss of SS. NOCM with NCO/OH=1.25 was recommended to modify SS to obtain the most balanced properties, at which time MSS has a roughest surface to mechanically interlock with asphalt, and there are excess -NCO groups to chemically bond with polar components in asphalt. Mixtures prepared at the optimum NCO/OH had good water damage resistance, freeze-thaw cycle resistance, and volume stability. Applying NOCM-modified SS in environment-friendly cold mixed asphalt mixtures can effectively improve pavement durability and bring good environmental benefits.

Tailoring optical and electrical properties of PVP-I2 complexes through gamma irradiation-induced structural defect Engineering for optoelectronic applications

This study investigates the complexation of iodine with polyvinylpyrrolidone (PVP) subjected to gamma irradiation at doses of 0, 10, 20, and 30 kGy. The complex formation involves I2 acting as a Lewis acid with electron-rich PVP as a Lewis base through a charge transfer mechanism. UV–visible spectroscopy revealed a red shift of the absorption peak at 337 nm for irradiated PVP-I2 samples, indicating stronger interactions between the triiodide ion (I3¯) and irradiated PVP. FTIR analysis confirmed the formation of new C≡N moieties arising from PVP-I2 complexation. The FT-Raman analysis revealed a new C≡N peak at 2360 cm−1 in irradiated PVP-I2 samples, with increasing intensity correlating to higher irradiation doses, indicating enhanced electron delocalization due to complex formation. XRD analysis showed that PVP-I2 samples irradiated at 0 and 10 kGy exhibited higher crystallinity than those irradiated at 20 and 30 kGy, suggesting that high-dose irradiation causes PVP radiolysis and ring opening. Dielectric measurements demonstrated enhanced polarizability and ionic conductivity of irradiated PVP-I2 films, related to structural disorder promoting charge dissociation and ion mobility. The direct bandgap decreased from 2.75 eV for unirradiated samples to 2.55 eV for samples irradiated at 30 kGy, indicating the creation of new optically active defect states. Urbach energy increased from 0.85 eV to 1.55 eV with increasing irradiation dose, confirming increased disorder in the PVP structure. These findings reveal that gamma irradiation significantly alters the optical and electrical properties of PVP-I2 films by creating defects and disrupting structure, offering new insights for tailoring these materials for optoelectronic applications.

Preface to the Special Issue on “The 4th International Conference on Engineering Physics, MEMS-Biosensors and Applications (4ICEBA2023)”

Preface to the Special Issue on “The 4th International Conference on Engineering Physics, MEMS-Biosensors and Applications (4ICEBA2023)”

Automated model discovery for textile structures: The unique mechanical signature of warp knitted fabrics

Textile fabrics have unique mechanical properties, which make them ideal candidates for many engineering and medical applications: They are initially flexible, nonlinearly stiffening, and ultra-anisotropic. Various studies have characterized the response of textile structures to mechanical loading; yet, our understanding of their exceptional properties and functions remains incomplete. Here we integrate biaxial testing and constitutive neural networks to automatically discover the best model and parameters to characterize warp knitted polypropylene fabrics. We use experiments from different mounting orientations, and discover interpretable anisotropic models that perform well during both training and testing. Our study shows that constitutive models for warp knitted fabrics are highly sensitive to an accurate representation of the textile microstructure, and that models with three microstructural directions outperform classical orthotropic models with only two in-plane directions. Strikingly, out of 214=16,384 possible combinations of terms, we consistently discover models with two exponential linear fourth invariant terms that inherently capture the initial flexibility of the virgin mesh and the pronounced nonlinear stiffening as the loops of the mesh tighten. We anticipate that the tools we have developed and prototyped here will generalize naturally to other textile fabrics–woven or knitted, weft knit or warp knit, polymeric or metallic–and, ultimately, will enable the robust discovery of anisotropic constitutive models for a wide variety of textile structures. Beyond discovering constitutive models, we envision to exploit automated model discovery for the generative material design of wearable devices, stretchable electronics, and smart fabrics, as programmable textile metamaterials with tunable properties and functions. Our source code, data, and examples are available at https://github.com/LivingMatterLab/CANN. Statement of significanceTextile structures are rapidly gaining popularity in many biomedical applications including tissue engineering, wound healing, and surgical repair. A precise understanding of their unique mechanical properties is critical to tailor them to their specific functions. Here we integrate mechanical testing and machine learning to automatically discover the best models for knitted polypropylene fabrics. We show that warp knitted fabrics possess a complex symmetry with three distinct microstructural directions. Along these, the behavior is dominated by an exponential linear term that characterize the initial flexibility of the virgin mesh and the nonlinear stiffening as the loops of the fabric tighten. We expect that our technology will generalize naturally to other fabrics and enable the robust discovery of complex anisotropic models for a wide variety of textile structures.

Topological phase engineering of rutile GeO2 with strain

Nodal-line semimetals serve as the parent phase for various topological states. By manipulating spin-orbit coupling (SOC), time-reversal symmetry, or spatial inversion symmetry, a Dirac nodal-line semimetal can transition into a 3D Dirac semimetal, Weyl semimetal, or topological insulator.In this study, we present the topological phase engineering of rutile GeO2 under strain through first-principles calculations. Without considering SOC effect, applying tensile strain to GeO2 induces a transformation from a trivial insulator to a Dirac nodal-line semimetal, characterized by two orthogonal and interconnected Dirac nodal rings protected by mirror symmetry. When SOC is taken into account, the band degeneracy persists only at two points, resulting in a 3D Dirac semimetal. Nonetheless, due to the negligible strength of SOC in GeO2, its nodal-line semimetal properties remain largely intact even after including SOC effects.Our findings provide valuable insights for the topological phase engineering and potential spintronics applications of GeO2.

Matching analysis between the heating/cooling capacity of PVT heat pumps and the heating/cooling demand of residential buildings across various regions in China

PVT-HP (Photovoltaic-Thermal Heat Pump) is a novel type of energy saving technology. Existing researches on PVT-HP technology focuses only on its operating performance under some specific conditions, ignoring the matching of its energy supply capacity with the energy demand of buildings, which is very important for its engineering design and application. In this paper, the matching relationship between the heating/cooling capacity of the PVT-HP system and the heating/cooling demand of residential buildings in major cities of China has been analyzed. To achieve this goal, a novel prediction model used to calculate the heating/cooling performance of the PVT-HP system has been proposed. And the daily heating guarantee rate and daily cooling guarantee rate have been put forward to evaluate the matching relationship. Based on the evaluation indexes, the applicable matching relationship between the installed capacity of the PVT-HP system and the heating/cooling area of residential buildings has been analyzed. The result of case study shows that for the selected cities of Shenyang, Beijing and Wuhan, the heating/cooling area of residential building for PVT-HP system with installed capacity of 1hp should not exceed 17m2, 31m2 and 25m2, respectively. Further matching analysis of major cities in China shows that the PVT-HP system is more suitable for application in hot summer and cold winter zones of China, in where the suggested heating/cooling area for the PVT-HP system with installed capacity of one horse power is within the range of 30m2 to 37m2.