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Biosensor chips for immune-based assay systems have been investigated for their application in early diagnostics. The development of such systems strongly depends on the effective protein immobilization on polymer substrates. In order to achieve this complex heterogeneous interaction the polymer surface must be functionalized with chemical groups that are reactive towards proteins in a way that surface functional groups (such as carboxyl, -COOH; amine, -NH2; and hydroxyl, -OH) chemically or physically anchor the proteins to the polymer platform. Since the proteins are very sensitive towards their environment and can easily lose their activity when brought in close proximity to the solid surface, effective surface functionalization and high level of control over surface chemistry present the most important steps in the fabrication of biosensors. This paper reviews recent developments in surface functionalization and preparation of polymethacrylates for protein immobilization. Due to their versatility and cost effectiveness, this particular group of plastic polymers is widely used both in research and in industry.

The COVID-19 pandemic has disrupted many areas of the human and organizational ventures worldwide. This includes new innovative technologies and strategies being developed by educators to foster the rapid learning-recovery and reinstatement of the stakeholders (e.g., teachers and students). Indeed, the main challenge for educators has been on what appropriate steps should be taken to prevent learning loss for the students; ranging from how to provide efficient learning tools/curriculum that ensures continuity of learning, to provision of methods that incorporate coping mechanisms and acceleration of education in general. For several higher educational institutions (HEIs), technology-mediated education has become an integral part of the modern teaching/learning instruction amidst the Covid-19 pandemic, when digital technologies have consequently become an inevitable and indispensable part of learning. To this effect, this study defines a hybrid educational model (HyFlex + Tec) used to enable virtual and in-person education in the HEIs. Practically, the study utilized data usage report from Massive Open Online Courses (MOOCs) and Emotions and Experience Survey questionnaire in a higher education setting for its experiments. To this end, we applied an Exponential Linear trend model and Forecasting method to determine overall progress and statistics for the learners during the Covid-19 pandemic, and subsequently performed a Text Mining and Univariate Analysis of Variance (ANOVA) to determine effects and significant differences that the teaching–learning experiences for the teachers and students have on their energy (learning motivation) levels. From the results, we note that the hybrid learning model supports continuity of education/learning for teachers and students during the Covid-19 pandemic. The study also discusses its innovative importance for future monitoring (tracking) of learning experiences and emotional well-being for the stakeholders in leu (aftermath) of the Covid-19 pandemic.

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The COVID-19 pandemic and the enforced restrictions have harshly affected educational sectors in 161 countries around the world. With more than 1.6 billion students away from normal school life, the crisis threatens the teaching and learning processes and the students’ emotional health. Herein, we present the result of a careful assessment of the feelings of over 13,000 students at high school, undergraduate, and postgraduate levels across 36 campuses over 8 subsequent weeks from the onset of the COVID-19 pandemic. The results indicate a general low energy level and dominance of negative feelings among the students regardless of their academic levels. We have recorded 5 responses (being anxious , stressed , overwhelmed , tired , and depressed ) as the most frequently reported feelings in the time of lockdown. Overall, 14% of those who have reported to suffer from these feelings have also identified a need for professional help in managing their feelings throughout the quarantine period. The current study also presents several strategies to combat the undesirable consequences of COVID-19 pandemic.

Today, modern educational models are concerned with the development of the teacher-student experience and the potential opportunities it presents. User-centric analyses are useful both in terms of the socio-technical perspective on data usage within the educational domain and the positive impact that data-driven methods have. Moreover, the use of information and communication technologies (ICT) in education and process innovation has emerged due to the strategic perspectives and the process monitoring that have shown to be missing within the traditional education curricula. This study shows that there is an unprecedented increase in the amount of text-based data in different activities within the educational processes, which can be leveraged to provide useful strategic intelligence and improvement insights. Educators can apply the resultant methods and technologies, process innovations, and contextual-based information for ample support and monitoring of the teaching-learning processes and decision making. To this effect, this paper proposes an Educational Process and Data Mining (EPDM) model that leverages the perspectives or opinions of the students to provide useful information that can be used to enhance the end-to-end processes within the educational domain. Theoretically, this study applies the model to determine how the students evaluate their teachers by considering the gender of the teachers. We analyzed the underlying patterns and determined the emotional valence of the students based on their comments in the Students Evaluation of Teaching (SET). Thus, this work implements the proposed EPDM model using SET comments captured in a setting of higher education.

Carbon nanomaterials play a vital role in biosensing applications in a wide variety of analytical devices due to their attractive electrochemical properties including relatively high electrical conductivity, wide electrochemical stability, biocompatibility, and chemical inertness. Biorecognition in electrochemical biosensing requires fine-tuning of the electrolyte/carbon interface resulting in the specific binding of the analyte and the measurement of the transduced signal. Successful protein immobilization on carbon nanomaterials can be achieved through surface functionalization methodologies that modify carbon from a naturally inert material to a physico-chemically active interface that can readily react with the analytes of interest. Any surface functionalization method influences electronical properties at the interface of the modified carbon thus interfacial signal transduction strongly depends on the type of surface modification. In this paper, we review the latest strategies for surface functionalization of carbon nanomaterials applied for covalent immobilization of different biomolecular entities. Furthermore, we summarize recently published techniques for carbon functionalization for achieving highly sensitive protein recognition. Some of these methodologies involve "multi-functionalization" of the carbon platforms enabling multiple biomarkers detection. Such advances in functionalization strategies open new windows of opportunity to the future of carbon as a biosensing material of choice.