Hao Wang

Mr. Hao Wang


Research Focus & Metrics

Academic Career Length

8Years (2019-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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15 March 2019 in Catalysts
Verified
Journal Article
Open access
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SPShihua Pu
Shihua Pu
HWHao Wang
Hao Wang
JZJiaming Zhu
Jiaming Zhu
LLLihua Li
Lihua Li
LDLong Dingbiao
Long Dingbiao
ZYZeng Yaqiong
Zeng Yaqiong
In this paper, a heterogeneous composite catalyst Cu2O/(001)TiO2 was prepared by the impregnation-reduction method. The crystal form, highly active facet content, morphology, optical properties, and the photogenerated electron-hole recombination rate of the as-prepared catalysts were investigated. The performance of Cu2O/(001)TiO2 was appraised by photocatalytic degradation of ammonia under sunlight and was compared with lone P25, Cu2O, and (001)TiO2 at the same reaction conditions. The results showed that 80% of the ammonia concentration (120 ± 3 ppm) was removed by Cu2O/(001)TiO2, which was a higher degradation rate than that of pure P25 (12%), Cu2O (12%), and (001)TiO2 (15%) during 120 min of reaction time. The reason may be due to the compound’s (Cu2O/(001)TiO2) highly active (001) facets content that increased by 8.2% and the band gap width decreasing by 1.02 eV. It was also found that the air flow impacts the photocatalytic degradation of ammonia. Therefore, learning how to maintain the degradation effect of Cu2O/(001)TiO2 with ammonia will be important in future practical applications.

Verified
Journal Article
Open access
PDF file
JZJiaming Zhu
Jiaming Zhu
ZLZuohua Liu
Zuohua Liu
HWHao Wang
Hao Wang
YJYue Jian
Yue Jian
DLDingbiao Long
Dingbiao Long
SPShihua Pu
Shihua Pu
In this study, an (A-R)TiO2 catalyst (ART) was prepared via the sol–gel method, and g-C3N4 (CN) was used as an amendment to prepare the g-C3N4/(A-R)TiO2 composite catalyst (ARTCN). X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), Raman spectroscopy, N2 adsorption–desorption curves (BET), UV–Vis diffuse absorption spectroscopy (UV–Vis DRS), and fluorescence spectroscopy (PL) were used to evaluate the structure, morphology, specific surface area, optical properties, and photocarrier separation ability of the catalysts. The results showed that when the modifier CN content was 0.5 g, the dispersion of the ARTCN composite catalyst was better, with stronger light absorption performance, and the forbidden band width was smaller. Moreover, the photogenerated electrons in the conduction band of ART transferred to the valence band of CN and combined with the holes in the valence band of CN, forming Z-type heterostructures that significantly improved the efficiency of the photogenerated electron-hole migration and separation, thus increasing the reaction rate. Gaseous and liquid ammonia were used as the target pollutants to investigate the activity of the prepared catalysts, and the results showed that the air wetness and initial concentration of ammonia had a great influence on the degradation of gaseous ammonia. When the initial concentration of ammonia was 50 mg/m3 and the flow rate of the moist air was 0.9 mL/min, the degradation rate of gaseous ammonia by ARTCN-0.5 reached 88.86%, and it had good repeatability. When the catalytic dose was 50 mg and the initial concentration of NH4+ was 100 mg/L, the degradation rate of liquid ammonia by ARTCN-0.5 was 71.60% after 3 h of reaction, and small amounts of NO3− and NO2− were generated. The superoxide anion radical (·O2−) and hydroxyl radical (·OH) were the main active components in the photocatalytic reaction process.

31 July 2024 in Animals
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Journal Article
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ZWZhen Wang
Zhen Wang
ZSZhengxiang Shi
Zhengxiang Shi
Hao LiHao Li
Hao Li
HLHui Liu
Hui Liu
ZXZhaowei Xiao
Zhaowei Xiao
SPShihua Pu
Shihua Pu
Aggression in farm animals affects welfare. Although one-on-one aggression was studied, group-on-individual aggression remains unresolved. This study aimed to examine how herd establishment times and structures influence aggression intensity (AI) of herds towards unfamiliar pigs. Six groups of pigs were established, with a new pig added every three days. AI was measured by skin lesion severity on the new pigs. A parabolic model based on the Levenberg–Marquardt algorithm and conjoint analysis identified factors influencing AI. Results show AI was not significantly affected by herd size but was significantly influenced by the number of pens (p < 0.01). AI showed a significant association with elevated time (T) since the establishment of the herd in six pig herds (Kendall’s tau-τ = 0.976, p < 0.001). The effect of T on the AI became stronger as T increased, which was consistently validated in six pig herds. Furthermore, the interaction effect indicates a significant difference in AI between herds formed with two pens and those with more than two pens when T ≤ 12 (p < 0.05). However, as T increased beyond 12, the number of pens used to form the herd did not significantly affect AI. These findings highlight the complex interactions between herd establishment time and structural composition in shaping aggression intensity towards unfamiliar pigs.

7 September 2025 in Animals
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Journal Article
Open access
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SGShaojuan Ge
Shaojuan Ge
HMHaiyun Ma
Haiyun Ma
XLXiusong Li
Xiusong Li
ZYZeng Yaqiong
Zeng Yaqiong
BLBaoming LI
Baoming LI
Weichao ZhengWeichao Zheng
Weichao Zheng
The rational design of artificial lighting systems in pig housing can enhance animal welfare, thereby boosting gilt health and reproductive performance while improving economic metrics for swine farms. To identify the optimal light environments for gilts under artificial illumination, we conducted self-selection-based photic preference testing, ultimately providing actionable insights for welfare-centric precision lighting protocols in modern pig production. In this study, a dynamic multi-chromatic self-selection system was developed, integrating programmable RGBW-LED arrays for spectral control, inter-compartment access channels for autonomous gilt movement, and real-time image recognition technology to investigate light color preferences. Twenty-four gilts (nulliparous female pigs) were housed for five weeks in pens with white, yellow, green, blue, or red light (100 lux), and they were given free access to all of the chromatic zones through inter-compartment channels. A YOLOv8n-based deep learning framework was used to quantify their spatiotemporal distribution, activity levels, and eating behavior. The key findings were the following: (1) a significant preference for green light environments (21.29 ± 3.77% distribution proportion) (p < 0.05), peaking at 6:00–13:00 and 18:00–20:00; (2) the average activity was the highest in a white light environment (25.49 ± 0.77%), significantly exceeding yellow (22.69 ± 0.63%) and green light (21.55 ± 0.61%) (p < 0.05); and (3) the daily feed consumption under green light was the lowest, significantly lower than that under white, blue, and red light (p < 0.05). The findings from this study offer insights into the light environment preferences of gilts, which could improve animal welfare.