P. V. Nidheesh

Renewable Energy, Sustainability and the Environment · 2013–2021

A researcher in the Science corpus: 8 papers; trusts 13 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

Whom P. V. Nidheesh trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. G. Crini
  2. E. Brillas
  3. A. Khataee
  4. I. Sirés
  5. R. Gandhimathi
  6. Vinod Kumar Gupta
  7. Ming-hua Zhou
  8. M. Oturan
  9. Ayhan Demirbas
  10. Marco Panizza

Who trusts P. V. Nidheesh overall

  1. Ali H. Jawad
  2. Ming-hua Zhou
  3. M. Oturan
  4. A. S. Abdulhameed

Papers from P. V. Nidheesh's perspective

Ordered by how strongly the researchers P. V. Nidheesh trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Electrochemical technologies in wastewater treatment
  2. Use of activated carbons prepared from sawdust and rice-husk for adsorption of acid dyes: a case study of Acid Yellow 36
  3. Electrochemical advanced oxidation processes: today and tomorrow. A review (Environmental science and pollution research international)
  4. A review of classic Fenton's peroxidation as an advanced oxidation technique. (Journal of Hazardous Materials)
  5. Non-conventional low-cost adsorbents for dye removal: a review. (Bioresource Technology)
  6. The adsorption of basic dyes from aqueous solution on modified peat-resin particle. (Water Research)
  7. Remediation of dyes in textile effluent: a critical review on current treatment technologies with a proposed alternative. (Bioresource Technology)
  8. Chemically modified graphite felt as an efficient cathode in electro-Fenton for p-nitrophenol degradation
  9. Application of Response Surface Methodology for Optimization of Azo Dye Removal by Oxalate Catalyzed Photoelectro-Fenton Process Using Carbon Nanotube-PTFE Cathode
  10. Photoelectro-Fenton combined with photocatalytic process for degradation of an azo dye using supported TiO2 nanoparticles and carbon nanotube cathode: Neural network modeling

Their papers