Electroanalysis最新文献

Diagnosis of periodontal disease by biosensors has recently received abundant attention. In this review, recently reported biosensors developed for the diagnosis of periodontal disease are systematically collected, categorized, and reviewed. Currently, the main specific biomarkers for the detection of periodontal disease are Porphyromonas gingivalis (P. gingivalis) and its polymerase chain reaction (PCR) products. In addition, some nonspecific markers such as hydrogen sulfide (H₂S), hydrogen peroxide (H₂O₂), sodium ions (Na⁺), lactoferrin, MMP-8, IL-1β, and odontogenic ameloblast-associated protein (ODAM) have been used for early detection of periodontal disease. In general, the choice of materials in these biosensors depends on interactions between materials and aptamers or application environments. In this paper, the diagnostic mechanism of the sensor and the interaction between the biorecognition element and signal transducer are analyzed. This review provides a systematic classification and comparative analysis of the detection techniques, biorecognition elements, target pathogens, and limits of detection (LOD) of these biosensors and discusses their applicability for early diagnosis of periodontal disease.

Diagnosis of periodontal disease by biosensors has recently received abundant attention. In this review, recently reported biosensors developed for the diagnosis of periodontal disease are systematically collected, categorized, and reviewed. Currently, the main specific biomarkers for the detection of periodontal disease are Porphyromonas gingivalis (P. gingivalis) and its polymerase chain reaction (PCR) products. In addition, some nonspecific markers such as hydrogen sulfide (H₂S), hydrogen peroxide (H₂O₂), sodium ions (Na⁺), lactoferrin, MMP-8, IL-1β, and odontogenic ameloblast-associated protein (ODAM) have been used for early detection of periodontal disease. In general, the choice of materials in these biosensors depends on interactions between materials and aptamers or application environments. In this paper, the diagnostic mechanism of the sensor and the interaction between the biorecognition element and signal transducer are analyzed. This review provides a systematic classification and comparative analysis of the detection techniques, biorecognition elements, target pathogens, and limits of detection (LOD) of these biosensors and discusses their applicability for early diagnosis of periodontal disease.

Diagnosis of periodontal disease by biosensors has recently received abundant attention. In this review, recently reported biosensors developed for the diagnosis of periodontal disease are systematically collected, categorized, and reviewed. Currently, the main specific biomarkers for the detection of periodontal disease are Porphyromonas gingivalis (P. gingivalis) and its polymerase chain reaction (PCR) products. In addition, some nonspecific markers such as hydrogen sulfide (H₂S), hydrogen peroxide (H₂O₂), sodium ions (Na⁺), lactoferrin, MMP-8, IL-1β, and odontogenic ameloblast-associated protein (ODAM) have been used for early detection of periodontal disease. In general, the choice of materials in these biosensors depends on interactions between materials and aptamers or application environments. In this paper, the diagnostic mechanism of the sensor and the interaction between the biorecognition element and signal transducer are analyzed. This review provides a systematic classification and comparative analysis of the detection techniques, biorecognition elements, target pathogens, and limits of detection (LOD) of these biosensors and discusses their applicability for early diagnosis of periodontal disease.

Diagnosis of periodontal disease by biosensors has recently received abundant attention. In this review, recently reported biosensors developed for the diagnosis of periodontal disease are systematically collected, categorized, and reviewed. Currently, the main specific biomarkers for the detection of periodontal disease are Porphyromonas gingivalis (P. gingivalis) and its polymerase chain reaction (PCR) products. In addition, some nonspecific markers such as hydrogen sulfide (H₂S), hydrogen peroxide (H₂O₂), sodium ions (Na⁺), lactoferrin, MMP-8, IL-1β, and odontogenic ameloblast-associated protein (ODAM) have been used for early detection of periodontal disease. In general, the choice of materials in these biosensors depends on interactions between materials and aptamers or application environments. In this paper, the diagnostic mechanism of the sensor and the interaction between the biorecognition element and signal transducer are analyzed. This review provides a systematic classification and comparative analysis of the detection techniques, biorecognition elements, target pathogens, and limits of detection (LOD) of these biosensors and discusses their applicability for early diagnosis of periodontal disease.

Accurate determination of ascorbic acid (AA, vitamin C), an essential, water-soluble antioxidant widely found in foods and pharmaceutical products and playing a critical role in human health, is important for clinical diagnosis, pharmaceutical analysis, and food quality control. Therefore, developing electrochemical sensing platforms with high sensitivity, rapid response, low cost, and simple operating principles is essential for the precise and selective determination of AA. In this study, LaBO₃/g-C₃N₄ (B = Mn, Fe, Co) nanocomposites were synthesized via a solution combustion route followed by ultrasonic coupling with graphitic carbon nitride (g-C₃N₄), and their electrochemical performance toward AA detection was systematically investigated. Comprehensive structural and physicochemical analyses confirmed the generation of highly crystalline perovskite oxides and their successful integration with g-C₃N₄. LaCoO₃/g-C₃N₄ (LCO/GCN) displayed superior optoelectronic and electrochemical properties, exhibiting the highest electrocatalytic activity toward AA oxidation with good selectivity, excellent reproducibility, operational cycling stability over 100 cycles, and successful applicability to real vitamin C samples analyses.

Accurate determination of ascorbic acid (AA, vitamin C), an essential, water-soluble antioxidant widely found in foods and pharmaceutical products and playing a critical role in human health, is important for clinical diagnosis, pharmaceutical analysis, and food quality control. Therefore, developing electrochemical sensing platforms with high sensitivity, rapid response, low cost, and simple operating principles is essential for the precise and selective determination of AA. In this study, LaBO₃/g-C₃N₄ (B = Mn, Fe, Co) nanocomposites were synthesized via a solution combustion route followed by ultrasonic coupling with graphitic carbon nitride (g-C₃N₄), and their electrochemical performance toward AA detection was systematically investigated. Comprehensive structural and physicochemical analyses confirmed the generation of highly crystalline perovskite oxides and their successful integration with g-C₃N₄. LaCoO₃/g-C₃N₄ (LCO/GCN) displayed superior optoelectronic and electrochemical properties, exhibiting the highest electrocatalytic activity toward AA oxidation with good selectivity, excellent reproducibility, operational cycling stability over 100 cycles, and successful applicability to real vitamin C samples analyses.

Accurate determination of ascorbic acid (AA, vitamin C), an essential, water-soluble antioxidant widely found in foods and pharmaceutical products and playing a critical role in human health, is important for clinical diagnosis, pharmaceutical analysis, and food quality control. Therefore, developing electrochemical sensing platforms with high sensitivity, rapid response, low cost, and simple operating principles is essential for the precise and selective determination of AA. In this study, LaBO₃/g-C₃N₄ (B = Mn, Fe, Co) nanocomposites were synthesized via a solution combustion route followed by ultrasonic coupling with graphitic carbon nitride (g-C₃N₄), and their electrochemical performance toward AA detection was systematically investigated. Comprehensive structural and physicochemical analyses confirmed the generation of highly crystalline perovskite oxides and their successful integration with g-C₃N₄. LaCoO₃/g-C₃N₄ (LCO/GCN) displayed superior optoelectronic and electrochemical properties, exhibiting the highest electrocatalytic activity toward AA oxidation with good selectivity, excellent reproducibility, operational cycling stability over 100 cycles, and successful applicability to real vitamin C samples analyses.

Accurate determination of ascorbic acid (AA, vitamin C), an essential, water-soluble antioxidant widely found in foods and pharmaceutical products and playing a critical role in human health, is important for clinical diagnosis, pharmaceutical analysis, and food quality control. Therefore, developing electrochemical sensing platforms with high sensitivity, rapid response, low cost, and simple operating principles is essential for the precise and selective determination of AA. In this study, LaBO₃/g-C₃N₄ (B = Mn, Fe, Co) nanocomposites were synthesized via a solution combustion route followed by ultrasonic coupling with graphitic carbon nitride (g-C₃N₄), and their electrochemical performance toward AA detection was systematically investigated. Comprehensive structural and physicochemical analyses confirmed the generation of highly crystalline perovskite oxides and their successful integration with g-C₃N₄. LaCoO₃/g-C₃N₄ (LCO/GCN) displayed superior optoelectronic and electrochemical properties, exhibiting the highest electrocatalytic activity toward AA oxidation with good selectivity, excellent reproducibility, operational cycling stability over 100 cycles, and successful applicability to real vitamin C samples analyses.

Dopamine (DA) is a key neurotransmitter whose abnormal level fluctuations are implicated in various diseases, rendering highly sensitive and selective detection critical for the early diagnosis and intervention. In this study, a bimetallic CuNi-BTC (BTC = 1,3,5-benzenetricarboxylic acid) metal-organic framework (MOF) served as the carrier for AuPd bimetallic nanoparticles (AuPd NPs); the resultant hybrid was combined with graphene oxide (GO) and subjected to electrochemical reduction, producing an AuPd/CuNi-BTC/electrochemically reduced GO (AuPd/CuNi-BTC/ERGO) composite. The regular one-dimensional rod-like microstructure of the CuNi-BTC MOF served as the structural skeleton and contributed to synergistic effects within the composite. It was an ideal carrier for uniformly dispersing the bimetallic nanoparticles, which enhanced the electrocatalytic activity toward DA. Meanwhile, the ERGO imparted high conductivity, enabling rapid interfacial electron transfer. The resulting composite was employed to modify a glassy carbon electrode for DA sensing. Experimental results demonstrated that the sensor exhibited two linear response ranges for DA, from 0.1 to 10.0 μM and from 10.0 to 400.0 μM, with a detection limit of 0.018 μM (S/N = 3) and a sensitivity of 24.53 μA μM⁻¹ cm⁻². In anti-interference tests, the presence of 10-fold excess of common interferents (e.g., uric acid, ascorbic acid, and glucose) resulted in a current suppression of less than 10%, suggesting favorable selectivity. The sensor also exhibited acceptable reproducibility (RSD = 2.33%, n = 5) and satisfactory long-term stability, retaining 102.6% of its initial response after 42 days of storage at 4 °C. When applied to DA detection in urine and commercial DA injection samples, the recoveries ranged from 100.59% to 104.85% (RSD = 1.68%–5.94%), confirming its potential for practical quantitative analysis of DA.

Dopamine (DA) is a key neurotransmitter whose abnormal level fluctuations are implicated in various diseases, rendering highly sensitive and selective detection critical for the early diagnosis and intervention. In this study, a bimetallic CuNi-BTC (BTC = 1,3,5-benzenetricarboxylic acid) metal-organic framework (MOF) served as the carrier for AuPd bimetallic nanoparticles (AuPd NPs); the resultant hybrid was combined with graphene oxide (GO) and subjected to electrochemical reduction, producing an AuPd/CuNi-BTC/electrochemically reduced GO (AuPd/CuNi-BTC/ERGO) composite. The regular one-dimensional rod-like microstructure of the CuNi-BTC MOF served as the structural skeleton and contributed to synergistic effects within the composite. It was an ideal carrier for uniformly dispersing the bimetallic nanoparticles, which enhanced the electrocatalytic activity toward DA. Meanwhile, the ERGO imparted high conductivity, enabling rapid interfacial electron transfer. The resulting composite was employed to modify a glassy carbon electrode for DA sensing. Experimental results demonstrated that the sensor exhibited two linear response ranges for DA, from 0.1 to 10.0 μM and from 10.0 to 400.0 μM, with a detection limit of 0.018 μM (S/N = 3) and a sensitivity of 24.53 μA μM⁻¹ cm⁻². In anti-interference tests, the presence of 10-fold excess of common interferents (e.g., uric acid, ascorbic acid, and glucose) resulted in a current suppression of less than 10%, suggesting favorable selectivity. The sensor also exhibited acceptable reproducibility (RSD = 2.33%, n = 5) and satisfactory long-term stability, retaining 102.6% of its initial response after 42 days of storage at 4 °C. When applied to DA detection in urine and commercial DA injection samples, the recoveries ranged from 100.59% to 104.85% (RSD = 1.68%–5.94%), confirming its potential for practical quantitative analysis of DA.