The banned items include pharmaceuticals, cement, and poultry products. Notably, the importation of drugs will be restricted under specific HS Codes ranging from 3003.10.00.00 to 3004.90.90.00. Commonly used medicines such as paracetamol, metronidazole, and chloroquine fall under this ban. Currently, Nigeria imports over 70% of its medicines and pharmaceutical raw materials, while local production accounts for less than 30% of national demand.

The Federal Government has granted a 90-day grace period for importers with existing agreements to clear goods under the old duty structure. This allowance aims to ease the transition for businesses affected by the new regulations. Yet, stakeholders express concern about the potential impact of this ban.

Olumide Akintayo from the Pharmaceutical Society of Nigeria stated, “It is one of the best things the government has done, and it is a step in the right direction.” However, Dr Olumide Obube warned that this structural dependency implies that banning finished product imports without first strengthening local active pharmaceutical ingredient (API) production could create a fragile supply system.

The policy replaces a previous framework established in 2023 and will soon be formalized in the Federal Government Gazette. The import ban is expected to have far-reaching implications across multiple sectors as it seeks to promote domestic manufacturing capabilities.

Details remain unconfirmed regarding how effectively local manufacturers can meet demand once imports are restricted. The long-term effects on drug availability and pricing remain uncertain as well.

This sequence of events matters significantly for Nigeria’s economy and public health sector. By reducing reliance on imports, Nigeria aims to foster self-sufficiency in essential goods while potentially creating jobs in local industries.

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The tariff cuts affect 127 product lines and are part of the ECOWAS Common External Tariff framework for 2022–2027. The government has also provided a 90-day grace period for importers with existing agreements to clear goods under the previous duty rates. This gradual approach to tariff adjustments is designed to ease the transition for businesses and consumers alike.

One of the most notable changes is the reduction of rice import duties from 70% to 47.5%. This move has raised concerns among local farmers, who fear that cheaper imported rice will undermine local production efforts. Mohammed Magaji, a representative of the All Farmers Association of Nigeria, expressed worries that the lower prices would discourage farmers from cultivating rice, stating, “It discourages the Nigerian farmers from going to the farm, honestly speaking, because there is no good price, there is no market.”

Pharmaceuticals have welcomed the tariff cuts, with Ayuba-Tanko Ibrahim from the Pharmaceutical Society of Nigeria noting, “A drop in duties on drugs and pharmaceutical products is quite laudable. In normal circumstances, this should signpost a drop in prices of these products and promote accessibility to drugs and healthcare, albeit legitimately.” However, there are calls for stronger regulatory measures to ensure that the benefits of these cuts are realized effectively.

The automobile sector has also voiced concerns regarding the impact of reduced tariffs on local assembly plants. Professor Oscar Odibo warned that the progress made by local automotive assemblers could be jeopardized, stating, “The danger in this is that the progress recorded by the local automotive assemblers will disappear because the investments in the assemblies will be affected, including employment of youths.” Such sentiments highlight the delicate balance the government must maintain between encouraging imports and supporting local industries.

The new tariff regime is part of a broader strategy to enhance Nigeria’s economic landscape, aiming to make essential goods more affordable for consumers while fostering a competitive market environment. The government plans to gradually eliminate Import Adjustment Taxes by 2036, further indicating a long-term commitment to reforming trade policies.

As these changes take effect, the full impact on various sectors remains to be seen. Stakeholders are closely monitoring the situation, particularly in agriculture and manufacturing, to assess how these tariff adjustments will reshape the market dynamics. Details remain unconfirmed regarding the long-term effects on local production and employment in affected industries.

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Recent developments in the field of deep learning have led to significant advancements in the design and application of single-atom catalysts (SACs). These catalysts, composed of isolated metal atoms anchored to a support and coordinated by surrounding ligands, have been utilized in various catalytic processes across fields such as chemical engineering, energy, environmental science, agriculture, pharmaceuticals, and medicine.

Machine learning (ML) has emerged as a fast, high-throughput, and computationally cost-effective tool to enhance SAC design. Researchers have successfully leveraged ML to predict adsorption energies of key intermediates and Gibbs free energy changes of elementary steps, leading to the design of SACs with exceptional activity and selectivity.

For instance, the Gradient Boosting Regression (GBR) model demonstrated a high coefficient of determination (R² = 0.99) and a low root mean square error (RMSE) of 0.03 eV in predicting the Gibbs free energy change for the hydroxyl group (ΔG *OH). This level of accuracy is crucial for optimizing the performance of SACs in various reactions.

Moreover, the Random Forest Regression (RFR) model was employed to predict the activities of 260 graphene-supported SACs, showcasing the potential of ML in analyzing complex datasets. The model’s predictions were based on key features such as the average distance between metal and nitrogen atoms, the distance between metal atoms, and the outer electron quantity of metal atoms, which are essential for understanding the limiting potentials in different catalytic processes.

In a notable study, ML-driven density functional theory (DFT) computations were adopted to explore the relationship between various structural properties of catalysts and hydrogen adsorption-free energy for hydrogen evolution reactions (HER). The integration of ML with DFT has reportedly improved research efficiency by 6.87 times, highlighting the transformative impact of these technologies on catalyst development.

Despite these advancements, challenges remain in fully understanding the complex interfacial effects within dual-atom catalyst (DAC) systems. A new descriptor, φ, has been proposed to quantify these effects, while the number of isolated electrons in d-orbitals has been introduced as a new metric for evaluating the catalytic activities of SACs for nitrogen reduction reactions (NRR).

As the field continues to evolve, the need for ML models to incorporate the properties of intermediates becomes increasingly apparent. This integration is essential for a comprehensive understanding of their influence on catalytic processes involving SACs. The ongoing research aims to refine these models further, ensuring that they can accurately predict and enhance the performance of SACs in various applications.

Overall, the intersection of deep learning and SAC design represents a significant advancement in catalysis research, with the potential to drive innovations in multiple scientific fields. As researchers continue to explore and refine these methodologies, the implications for energy efficiency, environmental sustainability, and industrial applications could be profound.

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