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Nampira, Ardi Azhar
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Quantum Dot-Embedded Polymer Films for Flexible Photonic Devices: Fabrication and Characterization Nampira, Ardi Azhar; Zahir, Roya; Khan, Omar; Shofiah, Siti
Research of Scientia Naturalis Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v2i4.2389

Abstract

materials for photonic devices that can conform to non-planar surfaces. Quantum dots (QDs) are ideal candidates due to their size-tunable emission and high quantum yields, but their integration into durable, flexible platforms remains a key challenge. This study aimed to develop and characterize highly luminescent and mechanically flexible quantum dot-embedded polymer films as a robust platform for next-generation photonic applications. We fabricated composite films by embedding cadmium selenide/zinc sulfide (CdSe/ZnS) core-shell QDs into a polydimethylsiloxane (PDMS) polymer matrix via solution casting. The structural, optical, and mechanical properties were systematically investigated using transmission electron microscopy (TEM), UV-Vis absorption, photoluminescence (PL) spectroscopy, and cyclic bending tests. The results showed that TEM analysis confirmed a uniform dispersion of QDs within the PDMS matrix without aggregation. The composite films exhibited intense, stable photoluminescence, retaining the characteristic sharp emission of the colloidal QDs. Crucially, the films demonstrated exceptional mechanical flexibility, maintaining over 95% of their initial PL intensity after 1,000 bending cycles to a 5 mm radius. The optical properties remained stable under various strain conditions, proving the effective protection afforded by the polymer matrix. This work successfully demonstrates a scalable method for producing high-quality, flexible photonic materials.  
Computational and Experimental Insights into Hydrogen Storage in Metal-Organic Frameworks (MOFs) Nampira, Ardi Azhar; Lee, Ava; Tan, Marcus
Research of Scientia Naturalis Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v2i4.2390

Abstract

The transition to a hydrogen economy is critically dependent on the development of safe and efficient onboard hydrogen storage materials. Metal-Organic Frameworks (MOFs) have emerged as highly promising candidates due to their exceptionally high surface areas and tunable pore environments. This study aimed to combine computational modeling with experimental validation to elucidate the key structural factors governing hydrogen storage capacity in MOFs. A dual approach was employed, using Grand Canonical Monte Carlo (GCMC) simulations to predict hydrogen uptake in a series of MOFs with varying pore sizes and metal centers, followed by experimental synthesis and gas sorption analysis to validate the computational findings. The results revealed a strong correlation between the simulated and experimental data, confirming that both high surface area and optimal pore size (~10-15 Å) are crucial for maximizing physisorption. The GCMC simulations accurately predicted that MOFs with open metal sites exhibit enhanced hydrogen binding energies. This research concludes that a combined computational and experimental approach provides powerful predictive insights, confirming that tailoring pore geometry and introducing strong adsorption sites are key strategies for the rational design of next-generation MOFs for high-density hydrogen storage.    
AI-Powered Digital Histopathology: Predicting Immunotherapy Response Using Deep Learning Judijanto, Loso; Chai, Som; Pong, Ming; Justam, Justam; Nampira, Ardi Azhar
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i3.2379

Abstract

Immunotherapy has revolutionized cancer treatment, yet predicting which patients will respond remains a major clinical challenge. Current predictive biomarkers, such as PD-L1 expression, have limited accuracy and fail to capture the complex interplay of cells within the tumor microenvironment. Digital histopathology, the analysis of digitized tissue slides, combined with artificial intelligence (AI), offers a novel approach to identify complex morphological patterns that could serve as more robust predictive biomarkers. Objective: A deep learning model, specifically a convolutional neural network (CNN), was trained on a large, multi-center cohort of digitized tumor slides from patients with non-small cell lung cancer who had received ICI therapy. The model was trained to identify subtle morphological features and the spatial arrangement of tumor cells and tumor-infiltrating lymphocytes. The model’s predictive performance was rigorously validated on an independent, held-out test cohort, and its performance was compared to the predictive accuracy of PD-L1 staining. The AI-powered model successfully predicted immunotherapy response with a high degree of accuracy, achieving an area under the receiver operating characteristic curve (AUC) of 0.88 in the validation cohort.
AI-Assisted Personalized Vaccine Design Using Multi-Omics Cancer Data Zaman, Khalil; Akhtar, Shazia; Lim, Sofia; Nampira, Ardi Azhar
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i3.2381

Abstract

The development of personalized cancer vaccines represents a promising frontier in oncology, yet traditional approaches struggle with the complexity and volume of multi-omics data. This study addresses this challenge by introducing an AI-assisted framework for the design of personalized vaccines. The primary objective was to leverage machine learning models to identify and prioritize neoantigens from integrated genomic, transcriptomic, and proteomic data of cancer patients. The methodology involved a deep learning pipeline to analyze multi-omics datasets, predicting tumor-specific mutations and their immunogenicity. This was followed by an algorithm to select the most potent neoantigen peptides for vaccine formulation, optimizing for both MHC binding affinity and T-cell activation potential. Our results demonstrate that the AI-driven approach significantly improved the speed and accuracy of neoantigen identification compared to conventional methods. The framework successfully predicted a set of high-quality vaccine candidates for individual patients, which showed strong in silico binding to patient-specific MHC molecules. We conclude that this AI-assisted methodology provides a powerful and scalable solution for personalized vaccine design, accelerating the translation of multi-omics data into clinically actionable immunotherapies.
Internet of Things (IoT) and New Business Opportunities in the Creative Sector Judijanto, Loso; Sato, Haruka; Fujita, Miku; Nampira, Ardi Azhar
Journal of Social Entrepreneurship and Creative Technology Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jseact.v2i3.2054

Abstract

The rapid growth of the Internet of Things (IoT) has led to significant advancements in various industries, including the creative sector. IoT technology enables the interconnection of devices and systems, allowing for new forms of interaction and automation. However, the potential of IoT to generate new business opportunities within the creative industries has not been fully explored. This research aims to investigate the impact of IoT on the creative sector, focusing on how businesses can leverage IoT to innovate, enhance customer experiences, and streamline operations. The study adopts a qualitative research approach, combining case studies and interviews with industry professionals to explore the current applications of IoT in design, media, entertainment, and the arts. The findings indicate that IoT technologies have the potential to revolutionize the creative sector by enabling personalized experiences, optimizing creative workflows, and creating new revenue streams. Businesses are increasingly adopting IoT to offer interactive experiences, such as smart installations, personalized content, and enhanced product design. The study concludes that IoT can offer substantial business opportunities for companies in the creative sector, especially when it comes to enhancing customer engagement and integrating technology into traditional creative processes.   
Co-Authors . Syamsulbahri Akhtar, Shazia Al-Momani, Ammar Apriyanto, Apriyanto Arinie, Farida Arisondha, Edy Arnes Yuli Vandika Baso Intang Sappaile, Baso Intang Chai, Som Costa, Bruna Costa, Tiago Devi, Syahrani Diana Suksesiwaty Lubis Dina Destari Edwin Sugesti Nasution Eva Desembrianita Farah, Rina Fernandez, Carlos Fernandez, Carloz Fujita, Miku Gomez, Raul Harja, Ngiw Aman Idris, Haziq Iqbal, Kiran Irwanto, Miko Mei Iwan Harsono Jenheri, Jenheri Judijanto, Loso Justam, Justam Kara, Sevda Kaya, Cemil Khan, Omar Lee, Ava Lek, Siri Lim, Sofia Lima, Rafaela Liu Yang M. Yogi Riyantama Isjoni M. Zai, Yovita Yulia Malik, Fatima Manik, Erba Kalo Martinez, Isabel Mayasari, Nanny Mendes, Clara Moktar, Brilian Muhammad Faisal Ninik Sri Rahayu, Ninik Sri Noor, Asmirin Nurjannah Nurjannah Nurul Huda Pangastuti, Nova Pong, Ming Putri, Agustin Andhika Rahman, Alkautsar Ramos, Nathaniel Razak, Faisal Rejeki, Marsudi Endang Sri Restu Auliani Reyes, Maria Clara Rudy Haryanto Saleh, Mohamad Zein Samaduri, Lutpi Santos, Luis Sari, Arum Fandan Sari, Ira Wulan Sato, Haruka setiawati, rahmi Setyowati Subroto Sinaga, Jernita Siti Aisyah Hanim, Siti Aisyah Siti Annisa Wahdiniawati, Siti Annisa Siti Shofiah Sothy, Chak Souza, Felipe Sulaiman Sulaiman Tahir, Usman Tan, Marcus Taryana Taryana, Taryana Taufik Taufik Telma Anis Safitri Urnika Mudhifatul Jannah, Urnika Mudhifatul Vita Lystianingrum Wiwit Xiang, Yang Yuliana, Ana Yusuf Iskandar Zahir, Roya Zaman, Khalil Zhang Li